AI-driven Intrusion Detection System Using Machine Learning: An Extensive Study

Wed, 09 Sep 2026 00:00:00 +0000

Network Intrusion Detection Systems (NIDS) have gained significant importance in modern cybersecurity owing to the escalating frequency and complexity of cyber-attacks on enterprise networks, cloud computing platforms, and Internet of Things (IoT). Traditional signature-based NIDS cannot identify zero-day attacks and evolving attacks, which is why intelligent techniques like machine learning (ML) and deep learning have been introduced to identify threats. This survey reviews recent advancements in machine-learning-based and deep-learning-based intrusion detection by comparatively analyzing five primary research studies and supporting literature covering different datasets, attack scenarios, learning approaches, and research challenges. In this survey, common datasets used, feature engineering techniques, classifiers, and metrics have been described along with their pros and cons. Moreover, key challenges faced by current NIDS include dataset imbalance, false positives, inadequate cross-dataset generalization, detection of unknown attacks, computational cost, and lack of interpretability. Additionally, potential future research directions for intrusion detection, namely explainable artificial intelligence (XAI), federated learning, transformer-based IDSs, continual learning, and adaptive hybrid models, have been discussed in this survey.

Lightweight Authentication and Encryption Protocols for Resource-constrained IoT Devices: A Review

Sonam Ashokrao Rewatkar — Mon, 14 Sep 2026 00:00:00 +0000

The rapid proliferation of Internet of Things (IoT) devices across healthcare, industrial automation, smart cities, and consumer applications has intensified the need for security mechanisms that operate within tight constraints on memory, computation, and energy. Conventional cryptographic primitives such as AES-256 and RSA, while secure, are frequently unsuitable for microcontrollers, RFID tags, and sensor nodes with kilobyte-scale memory and battery-limited operation. This has driven a distinct research area — lightweight cryptography — dedicated to authentication and encryption schemes tailored to resource-constrained hardware. This review synthesizes recent literature (2014–2025) on lightweight authentication and encryption protocols for IoT, organizing the field into five strands: lightweight block ciphers and authenticated encryption, including the NIST-standardized ASCON family; elliptic-curve-based mutual authentication; physically unclonable function-based schemes; RFID-specific ultra-lightweight protocols; and blockchain-assisted authentication architectures. Across these strands, the review compares reported computational overhead, memory footprint, energy consumption, and resilience against common IoT threats such as impersonation, desynchronization, and modeling attacks. The analysis shows that no single approach dominates across all constraint profiles: block ciphers suit bulk data confidentiality, PUFs suit tamper-evident device identity, and ECC suits scenarios requiring public-key functionality at higher, though still bounded, cost. The review concludes by highlighting persistent gaps, including post-quantum readiness and the recurring cryptanalysis-and-redesign cycle in ultra-lightweight RFID schemes, and proposes directions for future standardization and formal verification efforts.

Journal of IoT Security and Smart Technologies (e-ISSN: 2583-6226)

AI-driven Intrusion Detection System Using Machine Learning: An Extensive Study

Lightweight Authentication and Encryption Protocols for Resource-constrained IoT Devices: A Review