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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Sistem Informasi dan Teknologi Jaringan International Journal of Basic and Applied Science Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) DISTANCE: Journal of Data Science, Technology, and Computer Science Jurnal Mandiri IT Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Sistem Informasi Galuh Jurnal Ilmu Pendidikan dan Humaniora Jurnal Pengabdian Masyarakat Nauli Journal of Defense Technology and Engineering
Manurung, Jonson
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Journal : journal of defense technology and engineering

 1 
Cryptographic algorithm optimization for defense data security using quantum inspired algorithms Bagus Hendra Saputra; Jonson Manurung; Jeremia Paskah Sinaga
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The rapid advancement of quantum computing poses a critical threat to classical public-key cryptographic systems widely used in defense communication infrastructures, while the practical deployment of post-quantum cryptography (PQC) remains constrained by excessive key sizes, computational overhead, and energy consumption in bandwidth- and latency-sensitive military environments. This study aims to develop and evaluate a quantum-inspired multi-objective optimization framework to enhance the operational feasibility of standardized PQC schemes without compromising cryptographic security. The proposed method applies a Quantum Genetic Algorithm (QGA) to optimize configuration parameters of CRYSTALS-Kyber and CRYSTALS-Dilithium by simultaneously balancing security strength, computational performance, resource efficiency, and deployability. Experiments were conducted using official NIST test vectors and defense-oriented communication scenarios, with performance evaluated across encryption and signature latency, throughput, key and signature sizes, memory footprint, and energy consumption, while security was validated against classical and quantum attack models. The results demonstrate that the optimized configurations achieve key and signature size reductions of up to 10.3%, throughput improvements of up to 15.5%, and energy consumption reductions of up to 12.5% compared to baseline NIST implementations, while fully maintaining NIST security levels and robust resistance to quantum adversaries. These improvements significantly enhance the suitability of PQC for tactical radios, satellite communications, and resource-constrained defense platforms. The findings indicate that quantum-inspired multi-objective optimization is a critical enabler for transitioning post-quantum cryptography from theoretical security constructs to deployable, mission-ready solutions in real-world defense systems.
Multimodal deep learning framework for detection and attribution of adversarial information operations on social media platforms Nick Holson M. Silalahi; Jonson Manurung; Bagus Hendra Saputra
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Adversarial information operations on social media platforms pose critical threats to national security, with state-sponsored actors exploiting multimodal content manipulation to conduct sophisticated disinformation campaigns. Existing detection approaches focus on single-modality analysis, lacking comprehensive frameworks for simultaneous detection, attribution, and coordination identification. This research develops an integrated multimodal deep learning framework combining RoBERTa-large transformer, Vision Transformer, Graph Convolutional Networks, and bidirectional LSTM, unified through cross-modal attention fusion with multi-task learning optimization. Experimental validation utilizes eight datasets including Russian IRA tweets (3.8M posts), Fakeddit (1M submissions), TweepFake (25K accounts), FakeNewsNet (23K articles), MM-COVID (6.7K posts), CREDBANK (60M tweets), and MEMES (12K items). Results demonstrate 93.24% detection accuracy, 79.34% attribution accuracy across 15 threat actor groups, 91.67% coordination F1-score, 88.62% narrative classification accuracy, and 448ms inference latency suitable for real-time deployment. Ablation studies reveal graph neural networks provide largest performance contribution (5.82% improvement), highlighting social network analysis importance for detecting coordinated behavior. Future directions include large-scale pre-training, adversarial training, continual learning, human-AI collaboration, multilingual expansion, federated learning, and causal inference methods.
Blockchain-enhanced security framework for defense supply chain management: an AI-driven smart contract approach with distributed ledger technology Hondor Saragih; Jonson Manurung; Hengki Tamando Sihotang; I Made Aditya Pradhana Putra
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Defense supply chains face critical security challenges including counterfeit components, unauthorized access, data tampering, and supply chain attacks that compromise operational integrity and national security. Existing blockchain implementations suffer from limited scalability, inadequate threat detection mechanisms, and insufficient integration with modern AI technologies for real-time security monitoring. This research develops an AI-Enhanced Blockchain Security Framework combining smart contracts with distributed ledger technology specifically designed for defense supply chain management. The framework employs multi-signature authentication, cryptographic verification, and machine learning-based anomaly detection across a three-layer architecture (blockchain layer, security layer, analytics layer). Validation using the DataCo supply chain dataset (180K operations) and Backstabber's knife collection attack patterns (174 documented attacks) demonstrates 94.7% attack detection accuracy, 87.3% reduction in unauthorized access attempts, and 99.2% data integrity verification rate. The system achieved 850 transactions per second (TPS) throughput with 1.8-second average latency and 40% cost reduction compared to traditional centralized systems. Smart contract execution showed 99.96% reliability across 10,000 test scenarios with automated enforcement of security policies. Statistical validation confirmed significant superiority over conventional approaches (p<0.001). Future work includes quantum-resistant cryptography, federated learning for privacy-preserving analytics, cross-chain interoperability, and integration with IoT sensors for real-time supply chain monitoring.
A multi-objective Particle Swarm Optimization framework for defense logistics decision-making under dynamic and crisis conditions anindito anindito; Adam Mardamsyah; Jonson Manurung
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The complexity of decision-making in defense logistics systems has increased significantly due to demands for cost efficiency, distribution speed, and operational resilience in dynamic and crisis conditions. Conventional optimization approaches generally fail to capture these conflicting objectives simultaneously. This study aims to develop and evaluate a multi-objective optimization framework based on Multi-Objective Particle Swarm Optimization (MO-PSO) to support adaptive and performance-based defense logistics decision-making. The proposed method optimizes three main objective functions, namely minimizing operational costs, minimizing distribution time, and maximizing logistics readiness levels, with numerical parameter adjustments designed for the defense environment. Simulation results show that MO-PSO is capable of producing a more convergent and evenly distributed Pareto Front compared to comparison methods such as NSGA-II and standard MOPSO, with a 12.4–18.7% increase in hypervolume and a 21.3% decrease in solution dominance error. These findings indicate that the proposed approach is more effective in simultaneously balancing multi-objective trade-offs. Practically, the research results provide policy implications for defense planners in designing logistics strategies that are more efficient, responsive, and resilient to operational uncertainty.
Co-Authors Adam Mardamsyah Adha, Rochedi Idul Agus Firmansyah Agustina Simangunsong Al Hashim, Safa Ayoub Amran Sitohang Andri Budiman, Mohammad anindito anindito Bagus Hendra Saputra Bagus Hendra Saputra Barus, Nadela Bosker Sinaga Bosker Sinaga Bosker Sinaga, Bosker Sinaga Br Sitepu, Siska Feronika Br Tarigan, Nera Mayana Dhaifullah, Rendi Hanif Erika Novianti Eryan Ahmad Firdaus Febrian Wahyu Christanto Ferdinand Tharorogo Wau Firdaus Laia Firdaus Situmorang Frainskoy Rio Naibaho Hanan, Rohman Ali Hardy Priyatno Ambarita Harpingka Sibarani Hasugian , Paska Marto Hengki Tamando Sihotang Hidayati, Ajeng Hoga Saragih Hondor Saragih I Made Aditya Pradhana Putra Jeremia Paskah Sinaga Johanes Perdamenta Sembiring Kadin Darlianto Tinambunan Kanur L. P. Situmorang Logaraj Logaraj Logaraj, Logaraj M Azhar Prabukusumo Mardamsyah, Adam Maria Siahaan Maya Theresia Br. Barus Maya Theresia Br. Barus Merlin Helentina Napitupulu Mina Kumari Muhammad Azhar Prabukusumo Muthmainnah, Ihmatull Nasyira, Muhammad Sulthan Nick Holson M. Silalahi Nuriansyah, Agam Pandiangan, Boyner Phatoni, Khaerul Imam Piliang, Rizqullah Aryaputra Poltak Sihombing Prabukusumo, M Azhar Prabukusumo, M. Azhar Prabukusumo Prabukusumo, Muhammad Azhar Pradhana Putra, I Made Aditya Putra, Muhammad Ridho Alghifari Ramen, Sethu Rinaldy Chaniago Sawaluddin Sawaluddin, Sawaluddin Sethu Ramen Sethu Ramen, Sethu Ramen Sidiq, Maulana Sigalingging, Miranda Bintang Maharani Sihombing, Agus Putra Emas Sihotang, Amran Silalahi, Monalisa Hotmauli Simangunsong, Humala Sinaga, Jeremia Sinaga, Jeremia Paska Sinaga, Ryan Fahlepy Sri Kumala Sari Tsany, Tazky Uzitha Ram Vernando, Deden