Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Abstract

Hackers have evolved at a faster rate in terms of their capabilities because of precise technological developments like networks and communication systems. These crooks are constantly looking for new ways to jeopardize the security of computer networks (CN). Intrusion detection systems (IDS) consequently automatically become important parts of a CN. An IDS is a piece of software or hardware that tracks a company's network for potential threats. Meanwhile, an IDS is capable of reacting to and reporting any fraudulent activity. Node-based or host-based IDS (HIDS) [1, 2] Network based IDS (NIDS) or distributed-based IDS (DIDS), and hybrid-based IDS (HYIDS) are the three basic categories into which IDSs fall based on the unique IDS operation concept, this classification was created. *Author for correspondenceThe primary fundamental design goals of IDS are a decrease in false positive (FP) alerts and an increase in detection accuracy. Subsequently, when designing and implementing any IDS, that perspective must be taken into consideration [3]. In currently decades, machine learning (ML)-based IDS have taken over as the industry standard. According to ML systems may now potentially learn from the past and improve. Broadly speaking, the two ML philosophies of supervised ML and unsupervised ML can be separated. Models are learned using labeled data in supervised ML [4]. Unsupervised ML uses unstructured data to train models.The multiclass and binary classification objectives are carefully taken into consideration when using supervised ML techniques in this study. The classification process occurs whenever a supervised ML technique is asked to identify a specific quality. The training data for the techniques in this configuration frequently has large sizes and high-