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Proliferation of the internet by multiple devices has led to dramatic increases in network traffic. The Internet medium has also been growing with this usage, but this fast growth has also resulted in new threats making networks vulnerable to intruders and attackers or malicious users. This has made network security an important factor due to excessive usage of ICT (Information and Communications Technology) as threats to IVTs has also grown manifold. Securing data is a major issue, especially when they are transmitted across open networks. IDSs (Intrusion Detection Systems) are methods or techniques or algorithm which cater to detection of intrusions while on transit. IDSs are useful in identifying harmful operations. Secure automated threat detection and prevention is a more effective procedure to reduce workloads of monitors by scanning the network, server functions and inform monitors on suspicious activity. IDSs monitor systems continually in the angle of threat. This paper’s proposed technique detects suspicious activities using AI (Artificial Intelligence) and analyzes networks concurrently for defense from harmful activities. The proposed algorithm’s experimental results conducted on the UNSW_NB15_training-set shows good performances in terms of accuracy clocking above 96%.

TC-CNN: A Tree Classified model using AI for identifying malware intrusions in open Networks