International Journal of Digital Technology and Network Security System (e-ISSN: 3108-3307)

4G LTE and 5G in Military Missions: Operational Challenges and Strategic Opportunities

Settapong Malisuwan — 2026-01-30

Modern military operations increasingly rely on communication networks that can support high data volumes, flexibility, and interoperability across land, air, maritime, cyber, and space domains. Traditional military communication systems have delivered reliability over decades, but their voice-centric design and limited data capacity restrict their ability to support modern requirements such as real-time situational awareness, sensor fusion, and joint or coalition operations. Consequently, armed forces are progressively adopting commercial cellular technologies, particularly 4G LTE and 5G, within military communication architectures. This paper examines the role of LTE and 5G in military missions, focusing on their operational and strategic value. Comparative analysis indicates that these technologies provide advantages in deployment flexibility, cost efficiency, ease of maintenance, and interoperability with government and public safety networks. Evidence shows that 4G LTE is already a mature and operationally proven solution, widely used by the United States, NATO, and allied forces for bases, training, mission command, logistics, and civil–military support. Building on this foundation, 5G is emerging as a complementary capability, offering ultra-low latency, unified network management, and support for data-intensive multidomain operations. Together, LTE and 5G enable a phased, mission-driven modernization of military communications while preserving security, resilience, and long-term adaptability.

An Intelligent Machine Learning-based System for Detection of Phishing Domains Using AI/ML

Ashish Vats — 2026-02-28

Phishing attacks continue to pose a serious challenge to cybersecurity by exploiting deceptive domain names that closely resemble legitimate websites. Phishing detection techniques mainly depend on static features that are chosen using traditional feature selection or ranking techniques. Existing detection techniques, including blacklist-based and rule-driven systems, are often ineffective against newly created phishing domains and zero-day attacks. In this work, an intelligent machine learning-based system is developed to identify phishing domains by analysing URL and domain-level characteristics. The proposed approach focuses on extracting lexical, structural, and statistical features that capture common patterns observed in malicious domains. Machine learning classifiers, including Random Forest and Support Vector Machine, are trained and evaluated using a publicly available dataset containing both phishing and legitimate URLs. Random Forest shows strong performance with faster execution, making it suitable for real-time applications where fast and reliable decisions are essential. Experimental evaluation shows that the Random Forest model achieves higher accuracy, precision, recall, and F1-score, and better overall performance, compared to SVM. The results indicate that domain-level feature analysis combined with machine learning provides an effective and scalable solution for real-time phishing domain detection. The findings confirm that feature-driven intelligent systems can successfully detect phishing domains that imitate the look and feel of genuine websites. The proposed system offers a scalable, accurate, and real-time solution for phishing domain detection and contributes to strengthening cybersecurity defences against evolving phishing strategies. The purpose is to give users a reliable tool that warns them instantly about suspicious websites, reduces the risk of financial and data loss, and strengthens overall trust in online activities.

Autoencoders and Support Vector Machine for Zero-Day Threat Detection in Web Applications

Bhagyashali Sunil Pandarkar — 2026-01-20

Zero-day attacks represent one of the most critical and complex threats to modern web-based systems, as they exploit previously unknown vulnerabilities before security patches or signatures become available. Traditional signature-based intrusion detection systems often fail to identify such attacks due to their reliance on known patterns. To address this limitation, this research proposes a hybrid intelligent detection framework that combines Autoencoders and Support Vector Machines (SVMs) for effective zero-day attack detection in web environments. The Autoencoder component operates as an unsupervised anomaly detection mechanism, learning latent representations of normal network traffic and identifying deviations that may indicate suspicious behavior. These detected anomalies are then processed by an SVM classifier, which performs supervised learning to distinguish between benign and malicious activities. The proposed framework is evaluated using widely recognized benchmark datasets, including CICIDS2017 and NSL-KDD, ensuring robustness and comparability with existing approaches. Comprehensive preprocessing techniques such as feature normalization, dimensionality reduction, and class balancing are applied to enhance model performance. Experimental results demonstrate that the hybrid Autoencoder–SVM model achieves higher detection accuracy, improved generalization, and significantly reduced false-positive rates compared to standalone machine learning and deep learning models. The findings highlight the effectiveness of integrating unsupervised and supervised learning techniques to detect evolving and previously unseen attack patterns. Overall, this study presents a scalable and resilient solution for zero-day threat detection, contributing to enhanced security of web applications and network infrastructures.