Visible to the public Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System

TitleMachine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System
Publication TypeConference Paper
Year of Publication2020
AuthorsRashid, A., Siddique, M. J., Ahmed, S. M.
Conference Name2020 3rd International Conference on Advancements in Computational Sciences (ICACS)
Date PublishedFeb. 2020
ISBN Number978-1-7281-4235-7
KeywordsAPI, APIs, application programming interface, Bayes methods, CIDDS-001 dataset, compositionality, cyber security, cybersecurity organizations, Deep Learning, hybrid feature selection, IDS, Internet, intranet, intranets, Intrusion detection, intrusion detection system, Intrusion Detection System (IDS), K-NN, learning (artificial intelligence), machine learning, Naive Bayes Classifiers, nearest neighbour methods, network security threats, neural nets, NSL-KDD dataset, pattern classification, performance indicator metrics, pubcrawl, resilience, Resiliency, security of data, self-learning-based classification algorithms, Support vector machines, vulnerable source code

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naive Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naive Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.

Citation Keyrashid_machine_2020