Seiring dengan lonjakan volume dan semakin canggihnya metode serangan jaringan, infrastruktur keamanan siber saat ini menghadapi tantangan yang semakin kompleks. Dalam upaya mitigasi, integrasi Deep Learning (DL) ke dalam Sistem Deteksi Intrusi (IDS) telah merevolusi kemampuan deteksi, khususnya dalam mengidentifikasi serangan zero-day dengan tingkat akurasi yang signifikan. Namun, keunggulan performa ini membawa konsekuensi serius berupa munculnya masalah &rdquo;black-box&rdquo;. Sebagaimana ditekankan oleh Neupane et al. (2022) dan Capuano et al. (2022), model DL sering kali gagal memberikan penjelasan rasional mengenai mengapa suatu lalu lintas jaringan diklasifikasikan sebagai ancaman. Ketidakmampuan ini menciptakan hambatan operasional dan psikologis bagi analis keamanan. Tanpa transparansi, keputusan otomatis sulit diverifikasi, yang berpotensi memicu ketidakpercayaan atau bahkan pengabaian terhadap peringatan keamanan yang kritis. Oleh karena itu, transparansi bukan lagi opsi tambahan, melainkan kebutuhan fundamental untuk memastikan efektivitas IDS dalam lingkungan produksi. Untuk mengatasi ketidaktransparanan model, riset terkini berfokus pada adopsi teknik Explainable AI (XAI) bersifat post-hoc dan model-agnostic. Dua metode yang paling menonjol dalam literatur adalah SHAP (Shapley Additive Explanations) dan LIME (Local Interpretable Model-agnostic Explanations). Onyilo dan Uzuegbu (2025) serta Gaspar et al. (2024) menunjukkan bahwa metode ini sangat efektif ketika diintegrasikan dengan arsitektur kompleks seperti Multi-Layer Perceptron (MLP) atau hybrid CNN-LSTM.
<img src="../../backend/uploads/content/1775211346842-266497492.webp">
Peta Teknik Pembelajaran Mesin dan Hubungan Performance-Explainability Gaspar et.al (2024)
Melalui mekanisme kontribusi fitur, XAI memungkinkan identifikasi atribut spesifik seperti source bytes, flow duration, atau urutan system calls yang menjadi pemicu utama alarm keamanan. Temuan ini didukung oleh Udofot et al. (2024), yang membuktikan bahwa visualisasi penjelasan tersebut secara signifikan meningkatkan efisiensi kerja analis di Security Operations Center (SOC) dengan memberikan konteks yang jelas di balik setiap deteksi. Integrasi XAI dalam IDS melampaui fungsi teknis semata; ini adalah langkah strategis menuju ekosistem AI yang etis dan bertanggung jawab. Terdapat tiga peluang utama yang diidentifikasi dari penerapan ini:
<ol style="text-align: justify;">
<li style="font-family: georgia, serif;">Kerangka kerja Explainable IDS (X-IDS) selaras dengan standar global seperti EU AI Act dan panduan NIST. Standar ini menuntut akuntabilitas penuh pada sistem berbasis AI, terutama yang digunakan dalam infrastruktur kritis (Onyilo &amp; Uzuegbu, 2025).</li>
<li style="font-family: georgia, serif;">Transparansi memungkinkan pengembang memahami akar penyebab kegagalan model, seperti false positives. Dengan informasi ini, penyempurnaan fitur dapat dilakukan secara lebih presisi untuk meningkatkan robustness model.</li>
<li style="font-family: georgia, serif;">XAI mengubah peran AI dari mesin otomatis menjadi mitra kolaboratif. Kemampuan AI untuk memberikan "argumen" atas keputusannya memfasilitasi kepercayaan pakar keamanan manusia terhadap sistem otomatis.</li>
</ol>
<img src="../../backend/uploads/content/1775211411909-486303611.webp">
Arsitektur Framework Evaluasi XAI pada Model Machine Learning untuk Keamanan SiberArreche et al. (2024)
<ol style="text-align: justify;">
<li style="font-family: georgia, serif;">Meskipun menjanjikan, hasil studi mengungkap beberapa tantangan fundamental yang belum sepenuhnya teratasi dan memerlukan perhatian serius: Risiko Serangan Adversarial: Capuano et al. (2022) dan Arreche et al. (2024) memperingatkan bahwa XAI dapat menjadi "pedang bermata dua". Penyerang yang canggih dapat memanfaatkan penjelasan dari XAI untuk memetakan logika internal IDS, kemudian merancang serangan balik yang memanipulasi fitur agar tetap berada di bawah radar deteksi.</li>
<li style="font-family: georgia, serif;">Beban Komputasi (Overhead): Proses generasi penjelasan menuntut sumber daya tambahan. Pada jaringan berkecepatan tinggi yang memproses jutaan paket per detik, menyediakan penjelasan secara real-time tanpa mengganggu performa deteksi merupakan hambatan teknis yang nyata.</li>
<li style="font-family: georgia, serif;">Standarisasi dan Stabilitas: Arreche et al. (2024), melalui kerangka kerja E-XAI, menyoroti ketidakstabilan penjelasan XAI. Gangguan kecil pada data input dapat menghasilkan penjelasan yang sangat berbeda, yang berpotensi membingungkan analis dan mengurangi keandalan sistem secara keseluruhan.</li>
</ol>
Evolusi menuju Explainable Intrusion Detection Systems (X-IDS) merupakan kebutuhan mendesak dalam ekosistem keamanan siber yang semakin otomatis. Penelitian saat ini telah membuktikan bahwa transparansi dapat dicapai tanpa mengorbankan akurasi, dengan kinerja mencapai &gt;98% pada dataset benchmark. Namun, tantangan besar tetap ada pada aspek ketahanan terhadap manipulasi serangan dan standarisasi kualitas penjelasan. Pengembangan masa depan harus berfokus pada penciptaan model yang transparan secara intrinsik (intrinsic interpretability) serta penguatan keamanan pada lapisan XAI itu sendiri. Hal ini bertujuan agar mekanisme penjelasan tidak menjadi celah baru bagi peretas, melainkan tetap menjadi pilar kepercayaan dalam pertahanan siber. 
Referensi : Arreche, O., Guntur, T. R., Roberts, J. W., &amp; Abdallah, M. (2024). E-XAI: Evaluating black-box explainable AI frameworks for network intrusion detection. IEEE Access, 12, 23971-23988. Capuano, N., Fenza, G., Loia, V., &amp; Stanzione, C. (2022). Explainable artificial intelligence in cybersecurity: A survey. IEEE Access, 10, 93576-93600. Gaspar, D., Silva, P., &amp; Silva, C. (2024). Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, 30161-30175. Neupane, S., Ables, J., Anderson, W., Mittal, S., Rahimi, S., Banicescu, I., &amp; Seale, M. (2022). Explainable intrusion detection systems (X-IDS): A survey of current methods, challenges, and opportunities. IEEE Access, 10, 112392-112415. Onyilo, J., &amp; Uzuegbu, V. C. (2025). Explainable artificial intelligence intrusion detection system: Improving transparency and trust in cybersecurity. Nature Journal of Emerging Sciences Technologies and Innovations, 1(1), 85-103. 
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With the surge in attack volume and the increasing sophistication of cyberattack methods, today’s cybersecurity infrastructure faces increasingly complex challenges. As a mitigation strategy, the integration of Deep Learning (DL) into Intrusion Detection Systems (IDS) has revolutionized detection capabilities, particularly in identifying zero-day attacks with a high degree of accuracy. However, this performance advantage carries serious consequences in the form of the “black-box” problem. As emphasized by Neupane et al. (2022) and Capuano et al. (2022), DL models often fail to provide rational explanations for why certain network traffic is classified as a threat. This inability creates operational and psychological barriers for security analysts. Without transparency, automated decisions are difficult to verify, potentially leading to distrust or even disregard of critical security alerts. Therefore, transparency is no longer an optional feature but a fundamental requirement to ensure the effectiveness of IDS in production environments. To address model opacity, recent research focuses on the adoption of post-hoc and model-agnostic Explainable AI (XAI) techniques. The two most prominent methods in the literature are SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). Onyilo and Uzuegbu (2025) and Gaspar et al. (2024) demonstrate that these methods are highly effective when integrated with complex architectures such as Multi-Layer Perceptrons (MLPs) or hybrid CNN-LSTMs.
<img src="../../backend/uploads/content/1775211346842-266497492.webp">
Machine Learning Techniques Map and Performance-Explainability Relationship by Gaspar et al. (2024)
Through feature contribution mechanisms, XAI enables the identification of specific attributes—such as source bytes, flow duration, or the sequence of system calls—that serve as the primary triggers for security alerts. These findings are supported by Udofot et al. (2024), who demonstrated that such explanatory visualizations significantly enhance analyst efficiency in Security Operations Centers (SOCs) by providing clear context behind each detection. The integration of XAI into IDS goes beyond mere technical functionality; it is a strategic step toward an ethical and responsible AI ecosystem. Three key opportunities have been identified from this implementation:
<ol style="text-align: justify;">
<li style="font-family: georgia, serif;">The Explainable IDS (X-IDS) framework aligns with global standards such as the EU AI Act and NIST guidelines. These standards demand full accountability for AI-based systems, particularly those used in critical infrastructure (Onyilo &amp; Uzuegbu, 2025).</li>
<li style="font-family: georgia, serif;">Transparency enables developers to understand the root causes of model failures, such as false positives. With this information, feature refinement can be performed more precisely to enhance model robustness.</li>
<li style="font-family: georgia, serif;">XAI transforms the role of AI from an automated machine to a collaborative partner. AI’s ability to provide “reasoning” for its decisions fosters human security experts’ trust in automated systems.</li>
</ol>
<img src="../../backend/uploads/content/1775211411909-486303611.webp">
Architecture of the XAI Evaluation Framework for Machine Learning Models in Cybersecurity: Arreche et al. (2024)
<ol style="text-align: justify;">
<li style="font-family: georgia, serif;">Although promising, the study’s findings reveal several fundamental challenges that remain unresolved and require serious attention: Adversarial Attack Risks: Capuano et al. (2022) and Arreche et al. (2024) warn that XAI can be a “double-edged sword.” Sophisticated attackers can exploit XAI explanations to map the internal logic of an IDS, then design counterattacks that manipulate features to remain undetected.</li>
<li style="font-family: georgia, serif;">Computational Overhead: The explanation generation process demands additional resources. In high-speed networks processing millions of packets per second, providing real-time explanations without compromising detection performance poses a significant technical challenge.</li>
<li style="font-family: georgia, serif;">Standardization and Stability: Arreche et al. (2024), through the E-XAI framework, highlight the instability of XAI explanations. Minor disruptions in input data can yield vastly different explanations, which could potentially confuse analysts and reduce the system’s overall reliability.</li>
</ol>
The evolution toward Explainable Intrusion Detection Systems (X-IDS) is an urgent need in an increasingly automated cybersecurity ecosystem. Current research has demonstrated that transparency can be achieved without sacrificing accuracy, with performance reaching &gt;98% on benchmark datasets. However, significant challenges remain regarding resilience against attack manipulation and the standardization of explanation quality. Future development must focus on creating models with intrinsic interpretability and strengthening security within the XAI layer itself. This aims to ensure that explanation mechanisms do not become new vulnerabilities for hackers but remain a pillar of trust in cybersecurity defense. 
References: Arreche, O., Guntur, T. R., Roberts, J. W., &amp; Abdallah, M. (2024). E-XAI: Evaluating black-box explainable AI frameworks for network intrusion detection. IEEE Access, 12, 23971–23988. Capuano, N., Fenza, G., Loia, V., &amp; Stanzione, C. (2022). Explainable artificial intelligence in cybersecurity: A survey. IEEE Access, 10, 93576–93600. Gaspar, D., Silva, P., &amp; Silva, C. (2024). Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, 30161–30175. Neupane, S., Ables, J., Anderson, W., Mittal, S., Rahimi, S., Banicescu, I., &amp; Seale, M. (2022). Explainable intrusion detection systems (X-IDS): A survey of current methods, challenges, and opportunities. IEEE Access, 10, 112392–112415. Onyilo, J., &amp; Uzuegbu, V. C. (2025). Explainable artificial intelligence intrusion detection system: Improving transparency and trust in cybersecurity. Nature Journal of Emerging Sciences, Technologies, and Innovations, 1(1), 85–103.

Integrasi Explainable AI pada Sistem Deteksi Intrusi: Transformasi Menuju Transparansi Keamanan Siber

Integrating Explainable AI into Intrusion Detection Systems: A Shift Toward Cybersecurity Transparency

Saat ini banyak orang telah mengimplementasikan Large Languange Model (LLM) pada berbagai tugas sebagai sebuah agen kecerdasan buatan. Namun kebanyakan masih menggunakan agen yang bersifat general, agen kecerdasan buatan dianggap sebagai sebuah pisau swiss yang dapat diterapkan pada semua tugas, padahal kenyataannya agen kecerdasan buatan akan memberikan hasil yang lebih optimal apabila di fine-tune untuk sebuah tugas tertentu. Agentic AI adalah sebuah paradigma baru implementasi kecerdasan buatan dimana terdapat beberapa layer kecerdasan buatan, berupa agen kecerdasan buatan dengan kapabilitas tertentu, yang saling bekerja sama untuk dapat melaksanakan sebuah tugas secara mandiri. 
Sebagai contoh kita dapat meminta sebuah agen kecerdasan buatan untuk membuat sebuah proposal proyek, agen tersebut akan secara otomatis menjalankan langkah &ndash; langkah untuk membuat suatu proposal (i.e mengumpulkan data, membuat hipotesis, Menyusun format, etc). Dengan paradigma Agentic AI, Langkah &ndash; langkah tersebut tidak akan dikerjakan oleh 1 agen saja, melainkan akan dibagi kepada beberapa agen dengan kapabilitas khusus, dimana setiap agen akan saling berkomunikasi untuk menyelesaikan tugas tersebut. Misalnya pada contoh pembuatan sebuah proposal proyek, akan digunakan beberapa agen seperti agen manajer, agen riset, dan agen penulis dimana setiap agen memiliki tugas masing &ndash; masing, agen manajer akan menentukan langkah yang diperlukan, agen riset mengumpulkan data, dan agen penulis menentukan format.
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<img src="../../backend/uploads/content/1774771675845-281286380.webp">
(Source : <a href="https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full">https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full</a>)
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Gambar diatas mengilustrasikan perbedaan paradigma Agentic AI dengan agen kecerdasan buatan. Seorang yang menggunakan agen kecerdasan buatan akan memberikan input secara mendetail pada agen tersebut setiap kali agen tersebut perlu melaksanakan tugas. Pada paradigma Agentic AI pengguna akan memberikan sebuah input berupa tujuan yang ingin dicapai, kemudian layer &ndash; layer agen kecerdasan buatan akan saling berkomunikasi dan kemudian mengerjakan tugas &ndash; tugas mereka secara mandiri untuk dapat mencapai tujuan tersebut. Secara sederhana dapat dikatakan sebuah agen kecerdasan buatan dapat menyelesaikan sebuah tugas tertentu, sedangkan Agentic AI mengimplementasikan berbagai agen kecerdasan buatan dalam sebuah SOP.
Paradigma Agentic AI dapat diimplementasikan pada berbagai bidang keilmuan, sebagai contoh Amazon telah mengembangkan sebuah platform layanan kesehatan berbasis Agentic AI yaitu Amazon Connect Health. Berdasarkan artikel dari reuters, Amazon melalui Amazon Web Services meluncurkan platform Amazob Connect Health yang berbasis kecerdasan buatan untuk mengotomatisasi tugas administratif di sektor kesehatan, seperti penjadwalan pasien, verifikasi data, dokumentasi klinis, dan pengkodean medis. Platform ini memanfaatkan teknik pembelajaran khusus berbasis data dan pedoman kesehatan, serta melalui proses evaluasi berlapis untuk memastikan keamanan dan akurasi, termasuk keterlibatan tenaga klinis dalam proses verifikasi. Selain itu, sistem mampu mentranskripsikan percakapan dokter&ndash;pasien, menyusun catatan klinis secara real-time untuk ditinjau, serta menghasilkan ringkasan yang mudah dipahami pasien. Fitur evidence mapping juga disediakan untuk meningkatkan transparansi dengan mengaitkan output AI ke sumber data aslinya, seperti rekam medis atau transkrip percakapan. Implementasi Agentic AI ini berpotensi mengurangi beban administratif tenaga kesehatan yang selama ini signifikan, sehingga tenaga medis dapat lebih fokus pada pelayanan klinis kepada pasien.
Sebagai kesimpulan, paradigma Agentic AI merepresentasikan evolusi signifikan dalam pemanfaatan kecerdasan buatan, dari pendekatan agen tunggal yang bersifat umum menuju sistem multi-agen yang terstruktur, kolaboratif, dan berorientasi pada tujuan. Dengan membagi tugas ke dalam agen-agen dengan kapabilitas spesifik yang saling berinteraksi secara mandiri, Agentic AI mampu meningkatkan efisiensi, akurasi, dan skalabilitas dalam penyelesaian masalah kompleks. Implementasinya dalam sektor kesehatan, seperti pada platform Amazon Connect Health menunjukkan potensi untuk mengoptimalkan proses administratif yang secara tidak langsung akan meningkatkan kualitas layanan klinis. Agentic AI menjadi sebuah peluang untuk transformasi sistem kesehatan yang lebih adaptif, efisien, dan berpusat pada pasien.
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Referensi :
<a href="https://link.springer.com/article/10.1007/s10462-025-11422-4">https://link.springer.com/article/10.1007/s10462-025-11422-4</a>
<a href="https://www.nature.com/articles/s41746-026-02517-5">https://www.nature.com/articles/s41746-026-02517-5</a>
<a href="https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full">https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full</a>
<a href="https://www.reuters.com/business/healthcare-pharmaceuticals/amazon-launches-ai-enabled-platform-automate-healthcare-administrative-tasks-2026-03-05/">https://www.reuters.com/business/healthcare-pharmaceuticals/amazon-launches-ai-enabled-platform-automate-healthcare-administrative-tasks-2026-03-05/</a>
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Currently, many people have implemented Large Language Models (LLMs) for various tasks as artificial intelligence agents. However, most still use general-purpose agents; artificial intelligence agents are often viewed as a “Swiss Army knife” that can be applied to all tasks, when in reality they deliver more optimal results when fine-tuned for a specific task. Agentic AI is a new paradigm for implementing artificial intelligence where there are several layers of artificial intelligence—comprising AI agents with specific capabilities—that collaborate to perform a task autonomously. 
For example, we can ask an AI agent to create a project proposal; the agent will automatically execute the steps required to create a proposal (i.e., collecting data, formulating hypotheses, structuring the format, etc.). With the Agentic AI paradigm, these steps are not performed by a single agent but are distributed among several agents with specialized capabilities, where each agent communicates with one another to complete the task. For instance, in the example of creating a project proposal, several agents would be used, such as a manager agent, a research agent, and a writer agent, each with their own specific tasks: the manager agent determines the necessary steps, the research agent gathers data, and the writer agent defines the format.
 
<img src="../../backend/uploads/content/1774771675845-281286380.webp">
(Source: <a href="https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full">https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full</a>)
 
The image above illustrates the difference between the Agentic AI paradigm and traditional artificial intelligence agents. A user of a traditional AI agent would provide detailed input to the agent every time it needs to perform a task. In the Agentic AI paradigm, the user provides an input in the form of a desired goal; then, the layers of the artificial intelligence agents communicate with one another and perform their tasks autonomously to achieve that goal. Simply put, an artificial intelligence agent can complete a specific task, whereas Agentic AI implements various artificial intelligence agents within a standard operating procedure (SOP).
The Agentic AI paradigm can be implemented across various fields of science; for example, Amazon has developed a healthcare service platform based on Agentic AI called Amazon Connect Health. According to a Reuters article, Amazon, through Amazon Web Services, launched the Amazon Connect Health platform—an AI-based system designed to automate administrative tasks in the healthcare sector, such as patient scheduling, data verification, clinical documentation, and medical coding. This platform utilizes specialized data-driven learning techniques and healthcare guidelines, and undergoes a multi-layered evaluation process to ensure safety and accuracy, including the involvement of clinical staff in the verification process. Additionally, the system can transcribe doctor-patient conversations, compile clinical notes in real-time for review, and generate summaries that are easy for patients to understand. An evidence mapping feature is also provided to enhance transparency by linking AI outputs to their original data sources, such as medical records or conversation transcripts. This implementation of Agentic AI has the potential to reduce the significant administrative burden on healthcare workers, allowing medical staff to focus more on clinical care for patients.
In conclusion, the Agentic AI paradigm represents a significant evolution in the utilization of artificial intelligence, moving from a general-purpose single-agent approach toward a structured, collaborative, and goal-oriented multi-agent system. By dividing tasks among agents with specific capabilities that interact autonomously, Agentic AI can enhance efficiency, accuracy, and scalability in solving complex problems. Its implementation in the healthcare sector, such as on the Amazon Connect Health platform, demonstrates the potential to optimize administrative processes, which will indirectly improve the quality of clinical services. Agentic AI presents an opportunity for a more adaptive, efficient, and patient-centered transformation of the healthcare system.
 
 
References:
<a href="https://link.springer.com/article/10.1007/s10462-025-11422-4">https://link.springer.com/article/10.1007/s10462-025-11422-4</a>
<a href="https://www.nature.com/articles/s41746-026-02517-5">https://www.nature.com/articles/s41746-026-02517-5</a>
<a href="https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full">https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1753443/full</a>
<a href="https://www.reuters.com/business/healthcare-pharmaceuticals/amazon-launches-ai-enabled-platform-automate-healthcare-administrative-tasks-2026-03-05/">https://www.reuters.com/business/healthcare-pharmaceuticals/amazon-launches-ai-enabled-platform-automate-healthcare-administrative-tasks-2026-03-05/</a>

AGENTIC AI : TEROBOSAN BARU UNTUK PELAYANAN KESEHATAN BERBASIS KECERDASAN BUATAN

AGENTIC AI: A NEW BREAKTHROUGH IN ARTIFICIAL INTELLIGENCE-BASED HEALTHCARE SERVICES

Abstract The digital world is transforming rapidly. This transformation is creating cybersecurity super important. This paper is about developing proficient at cybersecurity. It essentially nubs on a major area: Cryptography, Managing Risks, testing for weaknesses and using Artificial Intelligence in numerous businesses. It demonstrates how educating cybersecurity in schools undertaking research and working strongly with businesses can help stop cyber threats. Several areas of study need to work to make digital systems secure and reliable. The core conclusion of this paper presents, that merging what we learn in school with what we do in life is the source to becoming cybersecurity better equally in schools and in industries. Cybersecurity is essential, we ought to make absolutely we are performing everything we can to make digital systems secure. To marrow it up cybersecurity is roughly we all need to concentration on every day. We should all try to acquire more about cybersecurity. Keywords: Cybersecurity, Cryptography, Risk Management, Penetration Testing, Artificial Intelligence, Industry 4.0
Introduction. We are using technologies every day. This is why cybersecurity is important. Lots of devices and systems are connected so organizations have a time keeping their systems safe.
I. Work in cybersecurity so I know a lot about it. My thing is making sure that what I teach is not just theory but things that actually work. I do lots of things like teaching, researching and working with companies on things, like cryptography, managing cybersecurity risks, testing for weaknesses and making sure AI systems are safe. I like to work on these things because it helps us come up with solutions to security problems that we are seeing with cybersecurity.
II. Cryptography and Secure Communication Cryptography is the key to secure communication. It helps keep information safe and makes sure it does not get changed. Cryptography also verifies who is sending the information. I have developed courses that teach students about cryptography and how it is used in the world. These courses teach students about the theory of cryptography and how to use it in practice so students can design systems. I also teach students about using cryptography in networks, where security is very important. Cryptography is very important, in these networks.
III. Cybersecurity Risk Management and Governance Managing cybersecurity risks is very important. It really helps to reduce threats to assets. My expertise is in using frameworks to find, assess, evaluate and manage risks. I have created coursework and projects that help students learn about risk assessment methods. These methods are based on standards like ISO/IEC 27001 and NIST frameworks. This helps students assess risks and come up with plans to fix them. I also stress the need, for governance to ensure security policies are put in place and checked regularly
IV. Vulnerability Assessment and Penetration Testing (VAPT) Vulnerability Assessment and Penetration Testing is important for cybersecurity. It finds out the weaknesses in the system. Pretends to be a cyberattack to see what would happen if it really happened. My area of expertise is using the tools and methods that the industry uses for Vulnerability Assessment and Penetration Testing. I have made some hands-on assignments that let students test the security of systems in a place. This helps them understand the expertise they require and understand what truth is and incorrect. My research is also exploring into utilizing Artificial Intelligence to yield it beneficial, at acquiring limitations.
V. Secure and Trustworthy AI in Industrial Systems AI in businesses is a covenant, and it effects a lot of security argues. This security argues involve phenomena like attacks on AI systems and data alteration. The issue is that AI simulations can be susceptible to these attacks. My work is, about making convinced AI systems are reliable and protected to use. I want to make confident that AI systems are protected so that people can conviction them. AI systems essential to be reliable. That is what I am working on. I have worked on research. Given demonstrations about AI in Internet of Things and factory automation. I always talk about how significant it's to be able to recognise and confidence the findings made by AI systems. This suggests exchanging with the circumstance that AI models can be like a box and composing sure systems can conduct faulty inputs without failing. AI systems need to be strong and safe from people who want to hurt them. AI in industries needs to be secure. That is what I focus on, trustworthy AI systems and AI, in industries.
VI. Academic. Industry Collaboration My work in academics is about creating courses and study materials for cybersecurity. I also teach these modules. Start research projects. I work with companies to form partnerships with them. This helps me share what I know with them and do research that they can use. I do this kind of research with the companies. My work with cybersecurity is important, to me. I like developing cybersecurity curricula and teaching cybersecurity modules. I also like starting cybersecurity research initiatives.
VII. Future Directions and Professional Development My future directions will be about making Artificial Intelligence at helping with cybersecurity. I desire to operate on systems that can spontaneously observe threats and keep us safe. Acquiring a license that says I am a Professional Technologist is very essential to me. This is because it will support me give opinion to anybody and to assist yield latest policies. I also want to train others about Artificial Intelligence and how it can be used for cybersecurity. Artificial Intelligence is a part of my work, and I want to make sure that I can assistance people to understand how to use it to keep their information safe.
VIII. Conclusion This is all about my expertise in the domain of cybersecurity. It discusses about details like cryptography and risk management and VAPT and secure intelligence systems. I can also handle cybersecurity challenges if I use my knowledge of cybersecurity and work with people who're professionals in the cybersecurity industry. Cybersecurity is constantly changing. So, it needs to come up with suggestions and work with people from different disciplines who have the knowledge about cybersecurity. I also require retaining acquiring more about cybersecurity and get suitable at managing cybersecurity problems. If I can be able to do these things I can develop systems that're really sound and can assist new technology for a long time. Cybersecurity is very essential. Everyone need to continue working on cybersecurity to become sure their digital systems and should be confident that their system is secure and safe, from cybersecurity threats.

Abstract The digital world is transforming rapidly. This transformation is creating cybersecurity super important. This paper is about developing proficient at cybersecurity. It essentially nubs on a major area: Cryptography, Managing Risks, testing for weaknesses and using Artificial Intelligence in numerous businesses. It demonstrates how educating cybersecurity in schools undertaking research and working strongly with businesses can help stop cyber threats. Several areas of study need to work to make digital systems secure and reliable. The core conclusion of this paper presents, that merging what we learn in school with what we do in life is the source to becoming cybersecurity better equally in schools and in industries. Cybersecurity is essential, we ought to make absolutely we are performing everything we can to make digital systems secure. To marrow it up cybersecurity is roughly we all need to concentration on every day. We should all try to acquire more about cybersecurity. Keywords: Cybersecurity, Cryptography, Risk Management, Penetration Testing, Artificial Intelligence, Industry 4.0
Introduction. We are using technologies every day. This is why cybersecurity is important. Lots of devices and systems are connected so organizations have a time keeping their systems safe.
I. Work in cybersecurity so I know a lot about it. My thing is making sure that what I teach is not just theory but things that actually work. I do lots of things like teaching, researching and working with companies on things, like cryptography, managing cybersecurity risks, testing for weaknesses and making sure AI systems are safe. I like to work on these things because it helps us come up with solutions to security problems that we are seeing with cybersecurity.
II. Cryptography and Secure Communication Cryptography is the key to secure communication. It helps keep information safe and makes sure it does not get changed. Cryptography also verifies who is sending the information. I have developed courses that teach students about cryptography and how it is used in the world. These courses teach students about the theory of cryptography and how to use it in practice so students can design systems. I also teach students about using cryptography in networks, where security is very important. Cryptography is very important, in these networks.
III. Cybersecurity Risk Management and Governance Managing cybersecurity risks is very important. It really helps to reduce threats to assets. My expertise is in using frameworks to find, assess, evaluate and manage risks. I have created coursework and projects that help students learn about risk assessment methods. These methods are based on standards like ISO/IEC 27001 and NIST frameworks. This helps students assess risks and come up with plans to fix them. I also stress the need, for governance to ensure security policies are put in place and checked regularly
IV. Vulnerability Assessment and Penetration Testing (VAPT) Vulnerability Assessment and Penetration Testing is important for cybersecurity. It finds out the weaknesses in the system. Pretends to be a cyberattack to see what would happen if it really happened. My area of expertise is using the tools and methods that the industry uses for Vulnerability Assessment and Penetration Testing. I have made some hands-on assignments that let students test the security of systems in a place. This helps them understand the expertise they require and understand what truth is and incorrect. My research is also exploring into utilizing Artificial Intelligence to yield it beneficial, at acquiring limitations.
V. Secure and Trustworthy AI in Industrial Systems AI in businesses is a covenant, and it effects a lot of security argues. This security argues involve phenomena like attacks on AI systems and data alteration. The issue is that AI simulations can be susceptible to these attacks. My work is, about making convinced AI systems are reliable and protected to use. I want to make confident that AI systems are protected so that people can conviction them. AI systems essential to be reliable. That is what I am working on. I have worked on research. Given demonstrations about AI in Internet of Things and factory automation. I always talk about how significant it&#x27;s to be able to recognise and confidence the findings made by AI systems. This suggests exchanging with the circumstance that AI models can be like a box and composing sure systems can conduct faulty inputs without failing. AI systems need to be strong and safe from people who want to hurt them. AI in industries needs to be secure. That is what I focus on, trustworthy AI systems and AI, in industries.
VI. Academic. Industry Collaboration My work in academics is about creating courses and study materials for cybersecurity. I also teach these modules. Start research projects. I work with companies to form partnerships with them. This helps me share what I know with them and do research that they can use. I do this kind of research with the companies. My work with cybersecurity is important, to me. I like developing cybersecurity curricula and teaching cybersecurity modules. I also like starting cybersecurity research initiatives.
VII. Future Directions and Professional Development My future directions will be about making Artificial Intelligence at helping with cybersecurity. I desire to operate on systems that can spontaneously observe threats and keep us safe. Acquiring a license that says I am a Professional Technologist is very essential to me. This is because it will support me give opinion to anybody and to assist yield latest policies. I also want to train others about Artificial Intelligence and how it can be used for cybersecurity. Artificial Intelligence is a part of my work, and I want to make sure that I can assistance people to understand how to use it to keep their information safe.
VIII. Conclusion This is all about my expertise in the domain of cybersecurity. It discusses about details like cryptography and risk management and VAPT and secure intelligence systems. I can also handle cybersecurity challenges if I use my knowledge of cybersecurity and work with people who&#x27;re professionals in the cybersecurity industry. Cybersecurity is constantly changing. So, it needs to come up with suggestions and work with people from different disciplines who have the knowledge about cybersecurity. I also require retaining acquiring more about cybersecurity and get suitable at managing cybersecurity problems. If I can be able to do these things I can develop systems that&#x27;re really sound and can assist new technology for a long time. Cybersecurity is very essential. Everyone need to continue working on cybersecurity to become sure their digital systems and should be confident that their system is secure and safe, from cybersecurity threats.

A Professional Overview of Expertise in Cybersecurity, Cryptography, and Emerging Technologies

<span lang="IN" style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Dalam ancaman siber modern, supply chain attack telah berkembang menjadi salah satu vektor serangan yang paling efektif. Attacker memanfaatkan integrasi antar system seperti distribusi software, layanan cloud, serta dependency software dengan tujuan mendapatkan initial access tanpai harus mengeksploitasi kerentanan secara langsung. Studi &ldquo;Supply Chain Characteristics as Predictors of Cyber Risk&rdquo; tahun 2022, menunjukkan bahwa pendekatan ini secara signifikan meningkatkan probabilitas insiden siber seiring dengan meningkatnya kompleksitas ekosistem digital.
<span lang="IN" style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="../../backend/uploads/content/1774409752316-58745601.webp">
Laporan IBM X-Force Threat Intelligence Index 2026 menunjukkan bahwa insiden supply chain compromise meningkat hampir empat kali lipat dalam lima tahun terakhir. 
<img src="../../backend/uploads/content/1774409770238-402910274.webp">
Tren ini mencerminkan pergeseran strategi attacker yang memanfaatkan trusted integration sebagai jalur initial access, sehingga memungkinkan serangan berkembang secara sistemik dan berdampak pada banyak entitas dalam satu rantai distribusi. Hal ini juga diperkuat oleh laporan High-Tech Crime Trends Report 2026 dari Group-IB yang mengidentifikasi bahwa berbagai threat actor, seperti Lazarus, Scattered Spider, HAFNIUM, serta grup kriminal seperti DragonForce dan 888 secara aktif memanfaatkan supply chain attack dalam operasional mereka.
Salah satu contoh supply chain attack adalah campaign pada ekosistem NPM yang dikaitkan dengan Shai-Hulud, di mana threat actor menyisipkan malicious code ke dalam package atau mempublikasikan versi yang telah dimodifikasi. Saat package di-install, malicious code akan otomatis dieksekusi untuk mencuri kredensial seperti token GitHub, NPM, dan akses cloud. Kredensial ini kemudian digunakan untuk menginfeksi package lain, sehingga serangan dapat menyebar secara luas dalam ekosistem.
<img src="../../backend/uploads/content/1774409888754-925312581.webp">
Karakteristik utama dari supply chain attack adalah kemampuannya dalam menyamarkan aktivitas berbahaya sebagai bagian dari proses normal. Karena aktivitas tersebut tampak legitimate (normal) dalam log dan monitoring sistem, banyak kontrol keamanan konvensional tidak mampu mendeteksi serangan pada tahap awal. Kondisi ini memberikan ruang bagi threat actor untuk mempertahankan akses, melakukan lateral movement, dan memperluas kontrol tanpa terdeteksi dalam jangka waktu yang lama.
Dampak dari supply chain attack tidak hanya bersifat teknis, tetapi juga berdampak langsung terhadap operasional, finansial, serta reputasi organisasi. Oleh karena itu, risiko ini perlu dipandang sebagai isu strategis di tingkat manajemen. Sebagai respons, organisasi perlu mengadopsi pendekatan keamanan yang berfokus pada peningkatan visibilitas dan kontrol terhadap jalur distribusi serta integrasi sistem, serta menerapkan prinsip Zero Trust, di mana setiap process harus diverifikasi secara langung untuk bisa dikategorikan aman.
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Referensi :
<a href="https://arxiv.org/abs/2210.15785">https://arxiv.org/abs/2210.15785</a>
<a href="https://www.ibm.com/reports/threat-intelligence">https://www.ibm.com/reports/threat-intelligence</a>
<a href="https://www.ibm.com/think/insights/more-2026-cyberthreat-trends">https://www.ibm.com/think/insights/more-2026-cyberthreat-trends</a>
<a href="https://www.enisa.europa.eu/publications/threat-landscape-for-supply-chain-attacks">https://www.enisa.europa.eu/publications/threat-landscape-for-supply-chain-attacks</a>
<a href="https://www.group-ib.com/media-center/press-releases/htct-2026-supply-chain/">https://www.group-ib.com/media-center/press-releases/htct-2026-supply-chain/</a>
<a href="https://jfrog.com/blog/shai-hulud-npm-supply-chain-attack-new-compromised-packages-detected/">https://jfrog.com/blog/shai-hulud-npm-supply-chain-attack-new-compromised-packages-detected/</a>
<a href="https://phoenix.security/shai-hulud-campaign-timeline/">https://phoenix.security/shai-hulud-campaign-timeline/</a>
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<span lang="IN" style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">In the modern cyber threat landscape, supply chain attacks have emerged as one of the most effective attack vectors. Attackers exploit integrations between systems—such as software distribution, cloud services, and software dependencies—to gain initial access without having to directly exploit vulnerabilities. The 2022 study “Supply Chain Characteristics as Predictors of Cyber Risk” shows that this approach significantly increases the probability of cyber incidents as the complexity of the digital ecosystem grows.
<span lang="IN" style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="../../backend/uploads/content/1774409752316-58745601.webp">
The IBM X-Force Threat Intelligence Index 2026 report indicates that supply chain compromise incidents have increased nearly fourfold over the past five years. 
<img src="../../backend/uploads/content/1774409770238-402910274.webp">
This trend reflects a shift in attacker strategies that leverage trusted integrations as a pathway for initial access, enabling attacks to spread systematically and impact multiple entities within a single distribution chain. This is further reinforced by Group-IB’s High-Tech Crime Trends Report 2026, which identifies that various threat actors—such as Lazarus, Scattered Spider, HAFNIUM—as well as criminal groups like DragonForce and 888—are actively leveraging supply chain attacks in their operations.
One example of a supply chain attack is the campaign within the NPM ecosystem linked to Shai-Hulud, where the threat actor injects malicious code into packages or publishes modified versions. When the package is installed, the malicious code is automatically executed to steal credentials such as GitHub tokens, NPM tokens, and cloud access credentials. These credentials are then used to infect other packages, allowing the attack to spread widely within the ecosystem.
<img src="../../backend/uploads/content/1774409888754-925312581.webp">
A key characteristic of a supply chain attack is its ability to disguise malicious activity as part of normal processes. Since such activity appears legitimate (normal) in system logs and monitoring, many conventional security controls fail to detect the attack in its early stages. This situation allows threat actors to maintain access, perform lateral movement, and expand their control undetected for extended periods.
The impact of a supply chain attack is not merely technical but also directly affects an organization’s operations, finances, and reputation. Therefore, this risk must be viewed as a strategic issue at the management level. In response, organizations need to adopt a security approach focused on enhancing visibility and control over distribution channels and system integrations, as well as implementing the Zero Trust principle, where every process must be verified in real-time to be classified as secure.
 
References:
<a href="https://arxiv.org/abs/2210.15785">https://arxiv.org/abs/2210.15785</a>
<a href="https://www.ibm.com/reports/threat-intelligence">https://www.ibm.com/reports/threat-intelligence</a>
<a href="https://www.ibm.com/think/insights/more-2026-cyberthreat-trends">https://www.ibm.com/think/insights/more-2026-cyberthreat-trends</a>
<a href="https://www.enisa.europa.eu/publications/threat-landscape-for-supply-chain-attacks">https://www.enisa.europa.eu/publications/threat-landscape-for-supply-chain-attacks</a>
<a href="https://www.group-ib.com/media-center/press-releases/htct-2026-supply-chain/">https://www.group-ib.com/media-center/press-releases/htct-2026-supply-chain/</a>
<a href="https://jfrog.com/blog/shai-hulud-npm-supply-chain-attack-new-compromised-packages-detected/">https://jfrog.com/blog/shai-hulud-npm-supply-chain-attack-new-compromised-packages-detected/</a>
<a href="https://phoenix.security/shai-hulud-campaign-timeline/">https://phoenix.security/shai-hulud-campaign-timeline/</a>

Supply Chain Attack: Ancaman Sistemik di Balik Ekosistem Digital Modern

Supply Chain Attacks: A Systemic Threat in the Modern Digital Ecosystem

<img src="../../../backend/uploads/content/1773996033723-599589983.webp" alt="" width="1440" height="1080">
Tiga pekan lebih perang US-ISRAEL dengan IRAN membuat kita terperangah telah terjadi perubahan mekanisme serangan dengan kekuatan yang saling menghacurkan. Kekuatan jumlah pasukan tidak mutlak menjadi penentu tapi teknologi pendukung dengan model serangan terbaru menjadi kunci : AI, Drone dan . Satelite sebagai pemantau sekaligus pengarah target menjadi utama, selain itu kekuatan enkapsulasi dan Teknik jamming radar juga menyadarkan kita bagaimana perang saat ini berangsur berubah. Gambar di atas diambil pada 15 Maret 2026 dari sebuah acara The Big Weekend Show di Fox News. Ramai di media sosial dalam beberapa hari ini setelah ramainya isu netizen tentang kapal induk US berhasil dirusak oleh Iran dan puncaknya dengan video Perdana Menteri Israel muncul yang disinyalir sebagai hasil AI.
Dalam penelitian yang dimulai oleh (Robinson, Jones, &amp; Janicke, 2015) sampai penelitian terbaru oleh (Kilincer, Ertam, &amp; Sengur, 2021), (Hasan et al., 2023), (Vennela, Akarapu, Rakshith, Asirvatham, &amp; Sunil, 2026) menjelaskan tentang penanganan serangan Cyber, terutama isu-isu serangan cyberwar, malware dan sisi hitam lainnya di Internet. Dimana saat ini serangan di Internet semakin beragam, berdampak luas dan kuat ditambah dengan mudahnya didapatkan tools peretas yang mempunyai fitur perusak yang lengkap tanpa perlu kemampuan di bidang coding dan network.
<div style="font-family: helvetica, arial, sans-serif;"><img style="margin: auto; display: block;" src="../../../backend/uploads/content/1773881643042-903730376.webp"></div>
Gambar 1: Hirarki Jenis perperangan (Robinson et al., 2015)
Cyber warfare telah memasuki era baru, dimana tindakan suatu negara dengan dukungan sumber dayanya berupaya untuk menembus komputer atau jaringan negara lain dengan tujuan menyebabkan kerusakan atau gangguan secara masif terutama menggangu layanan publik. Seperti pada gambar 1 yang telah di sampaikan oleh Robinson, dengan menggambarkan alur sebuah langkah / keputusan untuk mengkategorikan sebuah tindakan konflik yang dibuat atau terjadi. <img style="margin-top: auto; margin-bottom: auto;" src="../../../backend/uploads/content/1773881507859-890637632.webp">Gambar 2 : Weapons Life Cycles (Robinson et al., 2015)
Pada gambar 2 disajikan oleh Robinson tentang dua model serangan jika terjadi suatu perang militer antar negara dan terbukti saat ini hal tersebut terjadi baik serangan konvensional dengan misile balistik berpandu sampai dengan Drone yang bisa menerjang tepat sasaran dari jarak ribuan kilometer dari pelontar. Sampai dengan peretasan aset dengan penyadapan informasi pada sumber daya interkoneksi telekomunikasi atau komando sentral dengan cara menanam malware untuk menjejak dan tapping informasi yang lewat dan melakukan tindakan berikutnya seperti : remote backdoor, man in the middle attack dan sebagainya
 Dalam kondisi saat ini seperti disampaikan dalam editorial (Michael, Herold, &amp; Roussos, 2026), Deepfake telah menjadi bagian dari Cyberwarfare, yang sangat mungkin telah mengkoreksi kondisi warface sebelumnya. Deepfake by AI telah digunakan untuk menggiring opini dengan mudah membuat berita dan menyebar secara massive di socmed. Dengan demikian Deepfake akan menjadi isu kedepan terutama dalam perang informasi untuk saling klaim kemenangan dan progress yang dicapai.
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References
Hasan, M. K., Habib, A. K. M. A., Shukur, Z., Ibrahim, F., Islam, S., &amp; Razzaque, M. A. (2023). Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, 103540. doi:https://doi.org/10.1016/j.jnca.2022.103540 Kilincer, I. F., Ertam, F., &amp; Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. COMPUTER NETWORKS, 188, 107840. doi:https://doi.org/10.1016/j.comnet.2021.107840 Michael, K., Herold, R., &amp; Roussos, G. (2026). Security and regulation: Cybersecurity, privacy, and trust- protecting information and ensuring responsible technology use. Computers &amp; Security, 162, 104804. doi:https://doi.org/10.1016/j.cose.2025.104804 Robinson, M., Jones, K., &amp; Janicke, H. (2015). Cyber warfare: Issues and challenges. Computers &amp; Security, 49(0), 70-94. doi:http://dx.doi.org/10.1016/j.cose.2014.11.007 Vennela, A., Akarapu, R. B., Rakshith, B. L., Asirvatham, L. G., &amp; Sunil, G. (2026). Intelligent cybersecurity systems for phishing attack detection - An overview. Computers and Electrical Engineering, 130, 110829. doi:https://doi.org/10.1016/j.compeleceng.2025.110829
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<img src="../../../backend/uploads/content/1773996033723-599589983.webp" alt="" width="1440" height="1080">
More than three weeks into the U.S.-Israel war with Iran, we are stunned to see that the mechanics of the attacks have shifted toward mutually destructive forces. The sheer number of troops is no longer the decisive factor; instead, supporting technology with the latest attack models is key: AI, drones, and satellites—which serve as both monitors and target guides—have become paramount. Additionally, the use of stealth technology and radar jamming techniques has made us realize how modern warfare is gradually evolving. The image above was taken on March 15, 2026, during an episode of *The Big Weekend Show* on Fox News. Social media has been abuzz in recent days following widespread netizen speculation that a U.S. aircraft carrier was successfully damaged by Iran, culminating in a video of the Israeli Prime Minister that is alleged to be an AI-generated image.
In research ranging from (Robinson, Jones, &amp; Janicke, 2015) to the latest studies by (Kilincer, Ertam, &amp; Sengur, 2021), (Hasan et al., 2023), (Vennela, Akarapu, Rakshith, Asirvatham, &amp; Sunil, 2026) explain the handling of cyberattacks, particularly issues related to cyberwarfare, malware, and other dark web activities on the Internet. Currently, cyberattacks are becoming increasingly diverse, with widespread and severe impacts, compounded by the ease of accessing hacking tools that possess comprehensive destructive features without requiring expertise in coding or networking.
<div style="font-family: helvetica, arial, sans-serif;"><img style="margin: auto; display: block;" src="../../../backend/uploads/content/1773881643042-903730376.webp"></div>
Figure 1: Hierarchy of Warfare Types (Robinson et al., 2015)
Cyber warfare has entered a new era, where a nation, supported by its resources, attempts to penetrate another nation’s computers or networks with the aim of causing massive damage or disruption, particularly targeting public services. As illustrated in Figure 1 presented by Robinson, this depicts the flow of steps or decisions used to categorize actions resulting from or occurring during a conflict. <img style="margin-top: auto; margin-bottom: auto;" src="../../../backend/uploads/content/1773881507859-890637632.webp">Figure 2: Weapons Life Cycles (Robinson et al., 2015)
Figure 2, presented by Robinson, illustrates two attack models in the event of a military war between nations. It is evident that such scenarios are currently unfolding, ranging from conventional attacks using guided ballistic missiles to drones capable of striking precise targets from thousands of kilometers away from their launch points. This extends to hacking assets through information interception on interconnected telecommunications resources or central command systems by implanting malware to track and intercept passing information, followed by subsequent actions such as: remote backdoors, man-in-the-middle attacks, and so on.
 In the current situation, as outlined in the editorial (Michael, Herold, &amp; Roussos, 2026), Deepfakes have become an integral part of cyberwarfare, likely reshaping the landscape of warfare. AI-generated deepfakes have been used to manipulate public opinion by easily creating news and spreading it massively on social media. Thus, deepfakes will become a major issue in the future, particularly in information warfare, where parties vie to claim victory and highlight progress achieved.
 
References
Hasan, M. K., Habib, A. K. M. A., Shukur, Z., Ibrahim, F., Islam, S., &amp; Razzaque, M. A. (2023). Review on cyber-physical and cyber-security systems in smart grids: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, 103540. doi:https://doi.org/10.1016/j.jnca.2022.103540 Kilincer, I. F., Ertam, F., &amp; Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. COMPUTER NETWORKS, 188, 107840. doi:https://doi.org/10.1016/j.comnet.2021.107840 Michael, K., Herold, R., &amp; Roussos, G. (2026). Security and regulation: Cybersecurity, privacy, and trust—protecting information and ensuring responsible technology use. Computers &amp; Security, 162, 104804. doi:https://doi.org/10.1016/j.cose.2025.104804 Robinson, M., Jones, K., &amp; Janicke, H. (2015). Cyber warfare: Issues and challenges. Computers &amp; Security, 49(0), 70-94. doi:http://dx.doi.org/10.1016/j.cose.2014.11.007 Vennela, A., Akarapu, R. B., Rakshith, B. L., Asirvatham, L. G., &amp; Sunil, G. (2026). Intelligent cybersecurity systems for phishing attack detection - An overview. Computers and Electrical Engineering, 130, 110829. doi:https://doi.org/10.1016/j.compeleceng.2025.110829

Issue Volume 1 Nomor 1 (2026)

Articles in This Issue