# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 1 Introduction  
Today, many anomalies are found in any systems such as in healthcare, blockchain, cloud computing, and other networks. Any deviation from normal activity in these systems is known as an anomaly. These anomalies are creating economic-, time-, and resources-based problems [1]. Similarly, network security has become an open challenge to make cyberspace stronger than ever to make it cyber-attack-free. Cybersecurity is a structure containing certain protocols to follow such as in organizations to prevent unauthorized use of information and communications in a network [2]. Warfare using the internet between countries is also rising in this modern and digital era. Therefore, cybersecurity also becomes a weapon such as to strengthen the security of national assets and to protect citizens' information and the confidentiality of their data [3]. However, an efficient intruder detection system is needed to detect network attacks or anomalies found in systems. These anomalies are of two types: network- and host-based anomalies [4]. The host-based anomalies could be detected using Host Intruder Detection and Prevention Systems (HIDPSs), which use an application that is installed on the host side and used to monitor the incoming traffic by monitoring abnormal activities in it [5]. It does not provide any protection to the network on which the host is being installed. To take action against malicious attacks on a network, a NIDPS is needed, whereas it is installed just after the firewall is placed on the network [6]. These NIDPSs work based upon intelligent methods provided by AI. Mainly, these methods belong to Machine Learning (ML) and Deep Learning (DL) domains. These algorithms took cleaned data as input by applying pre-processing operations on raw data to develop a NIDPS [7]. These trained and classifiable methods are then installed on a NIDPS to detect the abnormal behavior of network traffic and pop up an alert to network engineers. The data created by internet sources and the big networks themselves have an abundance of traffic that makes the network complex and wide. These big data and wide networks make networks more vulnerable to malicious attacks. However, the response against each type of network type is different. Therefore, the precise detection of network attacks with their category is more challenging to solve. The probability to detect attack-type affects performance as the problem becomes multiclass [8]. NIDPS problems are not only about its performance efficiency against detection of multiple types of attacks, but it also has problems while proposing an optimal solution using ML [9] and DL [10] methods. These problems include the selection of the most distinguishing features to detect network attacks. For feature selection, many methods such as wrapper method [11], algorithm-based methods, and evolutionary computing domain [12] pruning methods are used. Feature selection before developing NIDPS is an important task as it reduces the feature matrix size while feeding it to classifiers. The evolutionary computing domain-based methods are used for automatic feature selection, which leads to full automation with a reduction of time and space complexity of NIDPS. The Particle Swarm Optimization (PSO) method is also used for feature selection, which improved the classification results [13], whereas this study suggested using more methods of PSO for parameters optimization and to increase the NIDPS performance as well. Furthermore, a survey is conducted based on the issue of the \"curse of dimensionality\". It gives an in-depth look to swarm intelligence methods for feature selection and highlights that these methods are good for feature selection due to their simpler and potential searching nature [14]. Therefore, these methods are more useful when needed to apply to big datasets. Furthermore, class imbalance problems arise in developing NIDPS, which is a big problem.