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Echo state network, Intrusion detection, misuse detection, neural networks, computer security

Identifying instances of network attacks by comparing current activity against the expected actions of an intruder has become an important. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. Transmission of data over the internet keeps on increasing, which needs to protect connected systems also increasing. Intrusion Detection Systems (IDSs) are the latest technology used for this purpose. Although the field of IDSs is still developing, the systems that do exist are still not complete, in the sense that they are not able to detect all types of intrusions. Some attacks which are detected by various tools available today cannot be detected by other products, depending on the types and methods that they are built on. In this work, an artificial neural network using echo state network algorithm has been used to implement the IDS. This paper proposes an approach to implement recurrent echo state network real time IDS. Twenty four packet information both normal and intrusion have been considered for training. Testing has been done with new sets of packet information. The result of intrusion detection (ID) is very close to 90%. The topology of the echo state network is (41 X 20 X 1). The network converged with 24 iterations. However, very huge amount of packets are to be evaluated to know the complete performance of the developed system.