Heuristic Initialization Using Grey Wolf Optimizer Algorithm for Feature Selection in Intrusion Detection

Abstract

Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of the various mechanisms that are employed to execute anomaly detection is strongly dependent on the set of features that are utilized. Thus, not every feature in the dataset may be employed in the classification operation since certain characteristics may result in poor solution quality. Feature selection (FS) may reduce the size of high-dimensional datasets by eliminating unimportant features. Modified binary grey wolf optimizer (MBGWO) is a successful metaheuristic that has been used for FS in anomaly detection. Nonetheless, the MBGWO is a randomization population-based algorithm that has an issue in finding a good quality solution during the initial population procedure. Thus, this study proposes a heuristic modified binary grey wolf optimizer (heuristic MBGWO) algorithm for FS in intrusion detection to enhance the initial population of the MBGWO using a heuristic-based ant colony optimization algorithm (ACO). The heuristic MBGWO algorithm was evaluated on NSL-KDD benchmark dataset from the University California Irvine (UCI) repository against five (5) benchmark metaheuristic algorithms. experimental results of the heuristic MBGWO algorithm on the NSL-KDD dataset in terms of the number of chosen attributes and classification accuracy are superior to other benchmark optimization algorithms, where it obtained the best features with 99.85% classification accuracy. The proposed heuristic MBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains.

Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

“…A predetermined number of grey wolves are used at the start of the procedure, and their locations are chosen at random. The following mathematical equations [26] dictate how each group in the pack will encircle one another [27]:…”

“…As stated by the following equations [23][24][25][26][27], the prey position Xp (iter + 1) update is determined by averaging the locations of grey wolves α, β, and Δ (the three temporarily ideal solutions), while the others are discarded for position update:…”

“…( 13) specifies their starting and ending positions. The grey wolf attacks to end the hunt when the victim finally stops moving [23][24][25][26][27]. The basic approach to developing a process model is to progressively lower the value of an, which lowers the range of A's fluctuations.…”

Enhanced feature clustering method based on ant colony optimization for feature selection

“…In addition, 𝐹𝐹 − 𝑆𝑆𝑐𝑐𝑜𝑜𝑓𝑓𝑓𝑓(𝐹𝐹 𝑑𝑑 ) in Equation ( 9) indicates the feature score 𝐹𝐹 𝑑𝑑 based on the class relevance [19], calculated as in Equation (12).…”

“…Techniques based on swarm intelligence as a search technique for FS include ant colony optimization (ACO) [11], grey wolf optimizer algorithm [12], artificial bee colony [13], and whale optimization algorithm [14] . Metaheuristic swarm search algorithms are highly beneficial due to their global search capabilities in high-dimensional data.…”