Research & Reviews: Journal of Engineering and Technology
MenuISSN: 2319-9873
ISSN: 2319-9873
+44 7456 035580Ebrima Jaw
College of Computer Science and Technology, China
ScientificTracks Abstracts: RRJET
The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyber-attacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement effective and optimized intrusion detection systems (IDSs) to help mitigate these un-fortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, one-class SVM, DBSCAN, and expectation-maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. Consequently, the proposed HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, UNSW-NB15 datasets, and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced false alarm rate and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current leading-edge approaches. These areas, along with applications, will be discussed. The talk will end, giving a few directions for research.
Ebrima Jaw completed his BSc in 2016 from the University of The Gambia. He spent almost three years serving as a Teaching Assistant and a Faculty Officer at the university, as mentioned above, before joining Guizhou University for his MSc. in Information and Network Security, and he is expected to graduate in June 2022. His research interests are Network Security, Big Data, Cloud Computing, Artificial Intelligences, specifically, Disruptive Technologies and their impact on society. He has published more than two papers in reputable journals