Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationIntrusion DetectionNetwork Intrusion DetectionSimilar Papers 제목 키워드 기반
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection
Intrusion detection has been a commonly adopted detective security measures to safeguard systems and networks from various threats. A robust intrusion detection system (IDS) can essentially mitigate threats by providing …
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Intrusion DetectionA Hybrid Approach for an Interpretable and Explainable Intrusion Detection System
Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable neces…
Intrusion DetectionA Machine Learning Based Intrusion Detection System for Software Defined 5G Network
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challen…
BIG-bench Machine LearningIntrusion DetectionManagementA Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats t…
Anomaly DetectionIntrusion DetectionNetwork Intrusion DetectionRobust Intrusion Detection System with Explainable Artificial Intelligence
Machine learning (ML) models serve as powerful tools for threat detection and mitigation; however, they also introduce potential new risks. Adversarial input can exploit these models through standard interfaces, thus cre…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Intrusion Detection