Intrusion Detection Using Ant Clustering Information Technology essay
Targeting the low detection accuracy of traditional clustering algorithm in intrusion detection under cloud computing platform, a network intrusion detection method based on improved ant colony algorithm combined with cluster analysis is proposed. The purpose of the ant colony clustering module is to distinguish most ant colonies. In the early 1990s, an algorithm called Ant System AS was proposed by Dorigo and colleagues as a new nature-inspired metaheuristic approach to the. Intrusion detection maintains network security by detecting intrusion behavior. There are many clustering algorithms that can be directly used for intrusion detection. K-means is a simple and efficient method used in data clustering. However, k-means tends to converge to local optima and depends on the initial value of. Many algorithms have been proposed for intrusion detection using various soft computing approaches such as self-organizing map SOM, clustering etc. In this paper, an attempt has been made to improve the intrusion detection algorithm proposed by Nadya et al. The proposed improvement of the algorithm is done by adding the SOM, Fessi et al. 46 proposed a data fusion model based on clustering for intrusion detection, to overcome the weakness of some existing literature on clustering, such as the lack of capabilities to do so . As the world is about to venture into fifth-generation communications technology and embrace concepts like these Like virtualization and cloudification, the most crucial aspect remains “security” as more and more data is connected to the Internet. This article reflects a model designed to measure the various parameters of data in a network. This paper proposes efficient hybrid cluster and classification models to implement an anomaly-based IDS for classifications of malicious attack types such as normal no intrusion, DoS. The paper presents an improved method for network intrusion detection using a machine learning-based approach. The study trained four types of deep neural network models, including two feed-forward. A cluster model based on self-organizing ant colony networks CSOACN is systematically proposed for intrusion detection systems. Instead of using the CSI's linear segmentation function. Intrusion detection systems are expected to detect and prevent malicious activity in a network, such as a smart grid. However, it is the most important systems that are targeted by cyber attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large ones. Numerous intrusion detection methods have been proposed in the literature to address computer security threats, which can be broadly classified into signature-based intrusion detection systems. And propose an intrusion detection model using PSO-WENN. This can effectively classify the attacks and reduce the number of false alarms generated by an intrusion detection system and the. Intrusion Detection and Prevention System IDPS is a device or software application designed to monitor a network or system. It detects vulnerabilities, reports malicious activity and takes action. In this section, we have proposed an approach called multi-view clustering, which uses the ant clustering method for community detection. To ensure the quality of.