Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.
Code (0)
등록된 구현이 없습니다.
Tasks
Intrusion DetectionSimilar Papers 제목 키워드 기반
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning
Federated learning is a technique of decentralized machine learning. that allows multiple parties to collaborate and learn a shared model without sharing their raw data. Our paper proposes a federated learning framework …
Federated LearningIntrusion DetectionOutlier DetectionEvaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion Detection
As the Internet of Things (IoT) continues to expand its footprint across various sectors, including healthcare, industrial automation, and smart homes, the security of these interconnected devices becomes paramount. With…
Intrusion DetectionAdaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection
As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrus…
Domain AdaptationIntrusion DetectionPseudo LabelRecommendation Systems+1Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey
With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line o…
Intrusion DetectionEnhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks
With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samp…
Graph LearningIntrusion DetectionSelf-Supervised Learning