The growing use of the Internet with its vulnerabilities has necessitated the adoption of Intrusion Detection Systems (IDS) to assure security. IDSs are protective systems that detect outsider infiltrations, unauthorised accesses and malfunctions occurring in computer networks. Intrusions can be detected and reported to the network administrator by IDSs using various pieces of information such as port scanning and irregular traffic detection. Intrusion detection is a classification problem, and identifying effective features is an essential aspect of classification methods. Standard methods used for classification are neural networks, fuzzy logic, data mining techniques and metaheuristics. One of the novel metaheuristic algorithms introduced to address optimisation problems is the Horse herd Optimisation Algorithm (HOA). This paper introduces a new approach on the basis of HOA for network intrusion detection. The new method uses horse behaviours in the herd to select effective features to detect intrusions and interactions between features. For the purpose of the new approach, HOA is first updated into a discrete algorithm using the floor function. The binarised algorithm is then converted into a quantum-inspired optimiser by integrating the concepts of quantum computing with HOA to improve the social behaviours of the horses in the herd. In quantum computing, Q-bit and Q-gate aid in striking a greater balance between the exploration and exploitation processes. The resulting algorithm is then converted into a multi-objective algorithm, where the objectives can be chosen from a set of optimal solutions. The new algorithm, MQBHOA, is then used for intrusion detection in computer networks, which is a multi-objective optimisation problem. For the classification, the K-Nearest Neighbour (KNN) classifier is applied. To evaluate the new algorithm’s performance, two data sets, NSL-KDD (Network Security Laboratory—Knowledge Discovery and Data Mining) and CSE-CIC-IDS2018, are employed in which the network packets are classified into five categories: normal packets plus four intrusions packet types of Denial of Service (DoS), User to Root (U2R), Remote to Local (R2L) and Probing (Prob). The new algorithm’s performance was evaluated and compared with other well-known metaheuristic algorithms, and the influence of the parameters of the algorithm on the degree of intrusion was investigated. The results show a 6% more success rate in the average size of feature selection and the accuracy of classification in comparison with other evaluated algorithms. It also demonstrates an accuracy of 99.8% in detecting network intrusions compared to other methods.
Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.
Automated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.