Detection of Real-Time Malicious Intrusions & Attacks in IOT Empowered Cyber security & Infrastructures
Keywords:
IoT Security, Intrusion Detection System, Generative Adversarial Networks (GAN), Malicious Attacks, Deep Learning, Network Security, Attack Detection, UNSW-NB15 Dataset, Real-Time Monitoring, Signature-Based Detection, Precision, Recall, Confusion MatrixAbstract
This paper proposes a new framework for classifying malicious intrusions and attacks in IoT networks using a deep learning model, namely GAN. Any traditional IDS based on defined patterns face challenges when faced with constantly changing attack signatures. To overcome this, we describe the deployment of a GAN model, which comprises a generator and a discriminator to identify both known and unknown attacks’ signatures. UNSW-NB15 was adopted for both training and testing the model and it consists of different attack signatures. The proposed system has a 98% detection rate of the growth of malicious activities in the system without necessarily relying on given signature attacks. Some other evaluation pointers like precision rate, recall rate and confusion matrix also support the model’s ability to drastically reduce the number of false positives and/or false negatives. The experimental outcomes provide a note for GAN-based intrusion detection that can effectively improve security in the evolving IoT context and promote a real-time IoT security platform.
