Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security
Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair
Abstract
In recent years, cyber security has been received interest from several research communities with respect to Intrusion Detection System (IDS). Cyber security is "a fast-growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc." An IDS is a software that monitors a single or a network of computers from malicious activities (attacks). Detecting an intrusion or prevention (due to increase the usage of internet), is becoming a critical issue. In past, several techniques have been proposed to overcome or detect intrusion in a network. But most of the techniques (used now days in detecting IDS) are not able to overcome this problem (in efficient manner).Together this, Machine Learning (ML) also has been adopted in various applications (due to providing good accuracy results (in respective domain)). Hence, this work discusses "How machine learning anddata mining can be used to detect IDS in a network" in near future.ML use efficient methods like classification, regression, etc., with efficient results like high detection rates, lower false alarm rates and less communication costs. This work also provides a detail comparison with metrics in table 1-3 (with their performance/ algorithms/ dataset or metrics used).