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Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility...
The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs...
In order to avoid the network intrusion, the network intrusion detection method is studied and developed. In the paper, a hybrid method of rough set and support vector machine are adopted to network intrusion detection. The detection model includes the data reduction by rough set and network intrusion recognition by support vector machine. The 680 cases are collected to study the superiority of the...
Organization has come to realize that network security technology has become very important in protecting its information. With tremendous growth of internet, attack cases are increasing each day along with the modern attack method. One of the solutions to this problem is by using Intrusion Detection System (IDS). Machine Learning is one of the methods used in the IDS. In recent years, Machine Learning...
With development and popularization of computer network, network security problems increasingly bring into prominence. Intrusion detection technique can effectively enlarge the scope of protection on network and system. An intrusion detection method based on support vector machine (SVM) is studied. Aiming at the shortcoming of SVM on detecting precision, an intrusion detection model based on improved...
This paper proposed a new algorithm of multi-category SVM incremental learning by analyzing the distribution characteristics of the intrusion detection data. Samples used in learning were selected by measuring the distance between samples and their class-centers, and they are just those samples which will most possibly be the SVs in incremental learning. By several binary-class hyper-planes, the zones...
As the rapid growth of network attacking tools, patterns of network intrusion events change gradually. Although many researches have been proposed to analyze attackers' behaviors to improve the detection rate, they still suffer in high false rate in intrusion detection. Therefore, an effective intrusion detection system (IDS) deployment requires carefully planning, preparation, prototyping, testing,...
Support vector machine-based intrusion detection methods are increasingly being researched because it can detect novel attacks. But solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by the feature dimensions and number of training samples. Feature selection or attribution reduction can help reduce the SVM classification time and saving memory...
Polymorphic attacks threaten to make many intrusion detection schemes ineffective. In order to address the threat of advanced attacks, model based techniques are required. In this paper we improve our Grammar Based Modeling techniques to be more resilient to attacks that change in form by using advanced classification techniques. Similarity distances from known models are input as features input to...
To accommodate the information security growth and hacker's improved strategies and tools, intrusion detection systems (IDSs) are required to be allocated across the network. Furthermore, previous studies showed that the choice of network features used for the IDS is dependent on the type of the attack. Accordingly, each TCP/IP network layer has specific type of network attacks, which means that each...
In this paper, we introduce a novel, non-linear, fast, online algorithm for learning on large data sets. This algorithm, which we call Voted Spheres (VS) is a combination of hypersphere-fitting, and the idea of voting. The algorithm builds hyperspheres around points, with different hyperspheres belonging to different classes allowed to overlap. The advantages of the algorithm are that it is simple...
Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. Reducing the features set improves the system accuracy and speeds up the training and testing phases...
Intrusion detection systems (EDSs) deal with large amounts of data containing irrelevant and/or redundant features. These features result in a slow training and testing process, heavy computational resources, and low detection accuracy. Features selection, therefore, is an important issue in EDSs. A reduced features set improves system accuracy and speeds up the training and testing process considerably...
Support vector machine (SVM) is a widely used tool in classification problem. SVM solves a quadratic optimization problem to decide which instances of training dataset are support vectors, i.e., the necessarily informative instances to form the classifier. The support vectors are intact tuples taken from the training dataset. Releasing the SVM classifier to public use or shipping the SVM classifier...
Masquerade detection discovers suspicious activities in a computer system by creating userspsila normal profiles, then raising an alert when the audited behavior does not fit. We propose to apply the SVM algorithm to the concurrently employed patterns that have been weighted according to their frequencies in order to identify masquerading attacks. Our approach not only reduces the complexity of the...
The detection of unknown malicious executables is beyond the capability of many existing detection approaches. Machine learning or data mining methods can identify new or unknown malicious executables with some degree of success. Feature selection is a key to apply data mining or machine learning to successfully detect malicious executables. We propose a method to extract features which are most representative...
Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it still suffers from detecting an unknown attack, i.e., 0-day attack, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack. Unlike the existing approaches that investigate raw traffic data, we introduced a feature extraction...
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