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An intrusion detection system using optimized deep neural network architecture
Author(s) -
Ramaiah Mangayarkarasi,
Chandrasekaran Vanmathi,
Ravi Vinayakumar,
Kumar Neeraj
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.4221
Subject(s) - intrusion detection system , computer science , the internet , artificial neural network , anomaly based intrusion detection system , classifier (uml) , vulnerability (computing) , computer security , host based intrusion detection system , network security , internet of things , computer network , data mining , artificial intelligence , machine learning , intrusion prevention system , operating system
Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks.