Conventional Network intrusion detection system (NIDS) mostly uses individual classification techniques, such system fails to provide the best possible attack detection rate. In this paper, we propose a new two-stage hybrid classification method using Support Vector Machine (SVM) as anomaly detection in the first stage, and Artificial Neural Network (ANN) as misuse detection in the second. The ...

This study introduces a new method based on Greedy-Boost, a multiple classifier system, for better and faster intrusion detection. Detection of the anomalies in the data-processing networks is regarded as a problem of data classification allowing to use data mining and machine learning techniques to perform intrusion detection. With such automatic processing procedures, human expertise only foc...

Anomaly detection from Big Cybersecurity Datasets is very important; however, this a challenging and computationally expensive task. Feature selection (FS) an approach to remove irrelevant redundant features select subset of features, which can improve the machine learning algorithms’ performance. In fact, FS effective preprocessing step anomaly techniques. This article’s main objective quantif...

Network-based computer systems play increasingly vital roles in modern society; they have become the target of intrusions by our enemies and criminals. Intrusion detection system attempts to detect computer attacks by examining various data records observed in processes on the network. This paper presents a hybrid intrusion detection system models, using Learning Vector Quantization and an enha...

Information systems have become more complex and highly interconnected. While ensuring real-time connectivity, these systems encounter an increasing amount of malicious traffic. Hence the need to establish a defense method. One of the most common tools for network security is intrusion detection and prevention systems (IDPS). An IDS, while supervising the incoming traffic, tries to identify sus...

The translation of traffic flow data into images for the purposes classification in machine learning tasks has been extensively explored recent years. However, method a significant impact on success such attempts. In 2019, called DeepInsight was developed to translate genetic information images. It then adopted 2021 purpose translating network images, allowing retention semantic about relations...

In the internet of vehicles, safety-based communication is carried out for prevention, mitigation, and alleviation accidents through cooperative messages, position sharing, exchange speed data between vehicle (nodes) corresponding roadside units. However, such networks are susceptible to false alarms mispositioning vehicles. It therefore imperative authenticate identify normal messages from agg...

Intrusion detection in computer networks is of great importance because its effects on the different communication and security domains. The network intrusion a challenge. Moreover, remains challenging task as massive amount data required to train state-of-the-art machine learning models detect threats. Many approaches have already been proposed recently detection. However, they face critical c...

This rapid growth of computer networks for the past decade, security has become a very important issue for computer systems. The detection of attacks by using IDS against computer networks is becoming a major problem to solve in the area of network security. In this paper we are going to present Genetic Algorithm to identify various harmful/attack type of connections. This algorithm takes into ...

A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method ...