The Anomaly- and Signature-Based IDS for Network Security Using Hybrid Inference Systems

Abstract

With the expansion of communication in today’s world and the possibility of creating interactions between people through communication networks regardless of the distance dimension, the issue of creating security for the data and information exchanged has received much attention from researchers. Various methods have been proposed for this purpose; one of the most important methods is intrusion detection systems to quickly detect intrusions into the network and inform the manager or responsible people to carry out an operational set to reduce the amount of damage caused by these intruders. The main challenge of the proposed intrusion detection systems is the number of erroneous warning messages generated and the low percentage of accurate detection of intrusions in them. In this research, the Suricata IDS/IPS is deployed along with the NN model for the metaheuristic’s manual detection of malicious traffic in the targeted network. For the metaheuristic-based feature selection, the neural network, and the anomaly-based detection, the fuzzy logic is used in this research paper. The latest stable version of Kali Linux 2020.3 is used as an attacking system for web applications and different types of operating systems. The proposed method has achieved 96.111% accuracy for detecting network intrusion.

Application of Data Mining in Predictive Analysis of Network Security Model

“…e associated database can provide the function of cluster analysis for data mining technology and can be deeply identified according to the characteristics of network security problems. For example, when malicious behavior attacks the computer terminal system, the associated database can comprehensively record and summarize its running track, execution program, and basic characteristics [20][21][22]. In the whole process, the cluster analysis algorithm plays a very important role.…”

Class Image Processing Application of XSS Intrusion Intelligent Detection for Big Data in Campus Network Construction

“…In 2018, the number of mine hijacking attacks was four times that of 2017. (2) In terms of mobile devices, the number of ransomware infections on mobile devices increased by 33% compared with 2017. e United States is the hardest hit area for mobile malware, accounting for 63% of the total, followed by China (13%) and Germany (10%). (3) In terms of web attacks, the total number of Web attacks on endpoints increased by 56% in 2018.…”

Comparison of the Host Based Intrusion Detection Systems and Network Based Intrusion Detection Systems

“…IDS systems become the most critical security systems for e-government and e-business applications [21]. One of the ways to identify hackers is to use and tightly tune intrusion detection systems (IDSs), which try to detect or predict the probability of an attack in various ways, such as controlling the network traffic and detecting those trying to attack by creating too much traffic on the network [22]. Session Initiation Protocol (SIP) packet replay attack, SIP packet insertion attack, TLS connection reset attack, flood attacks, and fake message attacks are the most known DoS attacks.…”