Design and Implementation of a Lightweight Adaptive Machine Learning Framework for Real-Time DDoS Mitigation in Resource-Constrained IoT Devices
TL;DR
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.
Abstract
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.