Visible to the public Hybrid Approach to Detect Network Based Intrusion

TitleHybrid Approach to Detect Network Based Intrusion
Publication TypeConference Paper
Year of Publication2018
AuthorsRani, Sonam, Jain, Sushma
Conference Name2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)
Keywordsanomaly detection, artificial intelligence, artificial intelligence systems, composability, Computer science, feature extraction, Fuzzy, Fuzzy logic, genetic algorithms, host, hybrid particle swarm optimization fuzzy rule based inference engine, IDS, inference mechanisms, Internet, internet based communication, Intrusion detection, intrusion detection techniques, intrusion tolerance, Network, network based intrusion, network intrusion detection system, particle swarm optimisation, population stochastic technique, PSO, PSO algorithm, pubcrawl, Resiliency, security of data, Stochastic processes
AbstractIn internet based communication, various types of attacks have been evolved. Hence, attacker easily breaches the securities. Traditional intrusion detection techniques to observe these attacks have failed and thus hefty systems are required to remove these attacks before they expose entire network. With the ability of artificial intelligence systems to adapt high computational speed, boost fault tolerance, and error resilience against noisy information, a hybrid particle swarm optimization(PSO) fuzzy rule based inference engine has been designed in this paper. The fuzzy logic based on degree of truth while the PSO algorithm based on population stochastic technique helps in learning from the scenario, thus their combination will increase the toughness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal as well as anomalism behaviour in the network. DARPA-KDD99 dataset examined on this system to address the behaviour of each connection on network and compared with existing system. This approach improves the result on the basis of precision, recall and F1-score.
DOI10.1109/ICCUBEA.2018.8697434
Citation Keyrani_hybrid_2018