Abstract:
In this fast-growing era of 21st century, where technology has taken over most of our aspects
of life, the development of computer and networks has indeed made our life a lot easier. But
this development has also brought insecurity of information on the internet and has the risk of
information theft and hacking. Researchers, all over the world, are focusing on providing
counter for such risks and security against hacking using different methods and techniques.
There are two types of Intrusion Detection System (IDS), anomaly-based and signature-based.
In the research work that is being presented here, anomaly-based IDS is modeled using
Machine Learning and Swarm Intelligence algorithm to decide whether the attack is taking
place or not. A methodology / algorithm based on stochastic optimization technique is
proposed. It’s called Particle Swarm Optimization (PSO), which is a part of swarm
intelligence. PSO is used for feature selection process after which we’ll get optimized values
to decide which features are best suited for classification. For classification, K Nearest
Neighbour (KNN), Support Vector Machine (SVM) and Logistic Regression (LR) classifiers
were implemented. Statistical results obtained concludes that using proposed methodology
KNN got an accuracy of 77%, SVM got an accuracy of 83% and LR got an accuracy of 97%.
