The rapid growth of the Internet of Things (IoT) has intensified the demand for lightweight cryptography that can secure billions of resource-constrained devices without prohibitive costs in energy or latency. This paper proposes a performance–security balanced framework that specifies (i) an adaptive key exchange protocol based on X25519 with HKDF-SHA256, (ii) authenticated encryption using ChaCha20-Poly1305 or AES-GCM with strict nonce/counter management, and (iii) hardware acceleration on ARM Cortex-M4 and FPGA modules to reduce computation and energy overhead. Protocol message flows, nonce/rekey rules, and full test vectors are provided to ensure reproducibility. Experimental evaluation on structured IoT traffic datasets shows that hardware-assisted AEAD achieves up to 38% lower encryption latency and 29% reduced energy consumption compared to software-only baselines. Security validation includes formal arguments, replay/MITM/downgrade attack experiments, and side-channel leakage assessment (TVLA), all of which confirm robustness against the defined threat model. The resulting framework offers a quantifiable and reproducible approach to securing IoT infrastructures in domains such as healthcare, smart cities, industrial networks, and intelligent transportation systems.
Shalini B, Jayaganesh J· International journal of com...· 0 citations
The rapid expansion of the Internet of Things (IoT) has raised additional concerns about security, and there was a major risk of Distributed Denial-of-Service (DDoS) attacks because the IoT devices have limited computation, memory, and energy capabilities. Traditional intrusion detection methods, which are at times contrived to support a high capacity, are incompetent at these limitations, delaying detections, having too many false alarms, and also compromising the system performance. This study offers a resource-efficient, adaptive machine learning system that was suitable to be used in the operation of DDoS attacks in resource-confined IoT settings. The technique combines the hybrid feature selection algorithms based on mutual information gain and recursive feature elimination to construct a more compact and high-utility feature set together with the optimization of the lightweight classifiers, including stochastic gradient descent and shallow decision trees. The concept drift was solved by an online incremental learning mechanism that guarantees long-term trend detection over time against changing patterns of attacks. The evaluation of the benchmark datasets (CICDDoS2019, BoT-IoT, TON_IoT) using experimental evaluation on a heterogeneous testbed IoT and assessing both security metrics and resource efficiency was researched. The model suggested had a precision of 0.973, a recall of 0.959, an F1-score of 0.966, and an average decrease of malicious traffic by 93 percent at the expense of legitimacy throughput. Latency was decreased to 2.6 seconds when detecting high-intensity attacks, and the CPU and memory usage continued to be less than 35 percent and 70 percent of the device capacity, respectively. A better result in terms of accuracy, response time, false positive rates, and not using resource budgets was witnessed when compared to baseline models through comparative analysis. The results verify the framework's ability to provide low latency and correct DDoS mitigation directly on the IoT devices, which can be considered a feasible solution to achieve resilience improvement of critical IoT deployments in health care, industrial automation, and smart cities.
Selvi T, Jayaganesh J· International journal of com...· 0 citations
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