A Lightweight Intrusion Detection System for Constrained IoT Devices

Hybrid Intrusion Detection System with Real-Time Concept Drift Detection for Enhanced IoT Security

A hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring is presented, providing initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability.

Detecting and Preventing Cyberattacks in Internet of Things (IoT) Systems

This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions.

Adaptive Machine Learning Framework for Real-Time Cyber-Attack Detection and Prevention in IoT Networks

This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.

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

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.

AI-Driven Intrusion Detection and Energy-Efficient Routing in Internet of Things (IoT) Networks

Analysis of Spoofing and Phishing Attacks on IoT Device

Internet of Things (IoT) device security remains a concern due to their limited computational resources and increasing exposure to network-based cyberattacks. While recent IoT security research has focused on machine-learning and blockchain-based defense mechanisms, many of these approaches introduce computational overhead that may not be suitable for resource-constrained devices. This study investigates lightweight host-based countermeasures against network-level spoofing and phishing attacks targeting IoT environments. Specifically, the research examines attack entry points, classifies associated vulnerabilities, and evaluates three practical defense mechanisms: packet analysis using Wireshark, port monitoring using iptables, and host file reconfiguration. Detection is performed through the identification of abnormal TCP communication patterns, unauthorized port activity, and malicious-domain access attempts. A controlled experimental environment based on a Raspberry Pi IoT platform was used to assess the effectiveness and resource consumption of each approach. The results indicate that host file reconfiguration achieved the highest detection accuracy (96%) with the lowest CPU overhead (7%), while the combined deployment of all mechanisms achieved a 99% blocking success rate. The findings demonstrate that lightweight host-based defenses can provide protection against phishing and spoofing attacks while maintaining operational suitability for resource-constrained IoT devices.