CyberWatch: Deep Learning-Driven Network Intrusion Detection

Abstract

Abstract—The present era is being dominated by the digital world which has also given rise to growing cyber threats and crimes, the project introduces an innovative intrusion detection system(IDS) that deploys deep learning’s pattern recognition capabilities with a real-time algorithm approach to threats and network security. The project’s framework involves two key components: Graph theory and Artificial Neural Network (ANN). The graph theory is used to represent the IoT network structure which helps represent relations between network structures and analyze the anomalies or malicious activities present in the network. Artificial Neural Network (ANN) model, on the other hand, is based on a human’s brain neural network; which works as a machine learning algorithm to recognize and predict patterns. Cyberwatch is trained on various datasets such as CIC IDS 2018 and CIC IDS 2017. The project’s main aim is to provide an effective solution to the ever-changing landscape of network intrusion. Keywords—Intrusion Detection System(IDS), Energy Ef- ficiency, Internet Of Things(IoT), Network Forensics, Graph Theory Support Vector Machine(SVM), Genetic Algorithm, Artificial Neural Networks(ANN), Cybercrime