Abstract
Intrusion detection is the process of detecting unauthorized traffic on a network or a device. Intrusion Detection Systems (IDS) are designed to detect the real-time intrusions and to stop the attack. An IDS is a software or a physical device that monitors traffic on the network and detect unauthorized entry that violates security policy. We present in this paper the various Neural Network approaches adopted by the different Intrusion Detection Systems. Artificial Intelligence plays significantly role in intrusion detection. Machine learning can also be applied to intrusion detection systems. Artificial Neural Networks are modelled inline with the learning processes that take place in biological systems. The Neural Networks are basically consists of a set of inputs, some intermediate layers and one output. They are capable of identifying the patterns and its variations. They can be “trained” to produce an accurate output for a given input. Neural Networks are capable of predicting new observations from other observations after executing a process of so called learning from existing data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
