## 1 Introduction  
In recent years, the advancement of technologies, namely, modern Industrial Control Systems, Cloud Computing, IoT (Internet of Things) and other web applications, has been rapidly increasing. These systems often handle large volumes of information and work in symbiosis in complex and vast communication networks. This results in intrusions by various ill-intentioned entities and hackers, who find new ways to break computer systems [1]. So, it will be important to improve the security of these network systems. Generally, an IDS monitors network traffic so as to detect suspicious activities, and creates an alert when these activities are found. It represents a software application that scans a system or network for harmful events or policy-breaching. Violations or malicious ventures are typically reported to a propriator, or centrally gathered with the use of SIEM (Security Information and Event Management). An SIEM system collects the results from multiple sources and utilizes alarm filtering methodologies for differentiating malicious events from FAR (False Alarm Rates). Though IDS can identify potential malicious activities, it is also liable to FARs. Thus, organizations need to fine-tune their IDS products once they have already been installed. This suggests that establishing the proper settings of an IDS for recognizing threats in network traffic is of the utmost importance, and these setting can be categorized based on the detection technique employed [2]. Moreover, in the area of cyber-security, ML methodologies have been productively implemented in developing effective approaches. These ML-based techniques show extensive potential to identify various types of intrusions, perform malware classification, protect privacy, evade cyber-attacks etc. Hence, ML has become a valuable tool for defenders. Contemporary threats have evolved to a sophisticated and complex level with the rapid evolution of adversarial methodologies. Most of the present security technologies will encounter various threats. Consequently, self-learning methodologies should be used, as they are capable of handling these issues. Accordingly, ML tools have emerged as crucial across the whole security sector. ML is an area of AI associated with data mining, data science and computational statistics. It is predominantly concerned with training machines to learn efficiently from input data. Hence, ML is considered a data-driven method, the initial phase of which is understanding the raw data to construct smart security frameworks and generate forecasts [3]. Correspondingly, several studies have endorsed the use of various models for enhancing the performance of IDSs with the use of diverse methods, such as wrapper, filter, optimization, and bio-inspired algorithms, etc. Specifically, bio-inspired algorithms are utilized for improving the performance of network IDSs via their ability to find the most efficient solutions in less time. Each of the bio-inspired algorithms possesses its own merits and demerits. Using hybrid methods, each of the algorithms employed could adopt the strengths of the other, as well as resolving its demerits [4]. Several recent studies have also found that hybridization enhances the performance of bio-inspired methods. In accordance with this, Ref. [5] used an efficient method to detect and classify attacks based on a UNSW-NB15 dataset. For improving accuracy, feature selection has been developed with the use of the OSS (Optimized Sine Swarm) approach, which chooses significant features. Lastly, ID has been performed with OSS-RF (OSS-Random Forest). The performances of classifiers deployed have been assessed by use of various evaluation metrics. Comparisons have been made with various conventional models for confirming the efficacy of this approach. The endorsed system achieved a better performance, with 98.15% accuracy. Following this, the research is [6] aimed to enhance the classification rate and minimize the redundancy arising in feature selection during intrusion detection.