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Rising of computer violence, such as Distributed Denial of Service (DDoS), web vandalism, and cyber bullying are becoming more serious issues when they are politically motivated and intentionally conducted to generate fear in society. These kinds of activity are categorized as cyber terrorism. As the number of such cases increase, the availability of information regarding these actions is required...
Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
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...
This paper proposes Modified Ant Miner algorithm for intrusion detection. Ant Miner and its descendant have produced good result on many classification problems. Data mining technique is still relatively unexplored area for intrusion detection. In this paper, modification has been suggested in basic ant miner algorithm to improve accuracy and training time of algorithm. The KDD Cup 99 intrusion data...
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...
Intrusion detection is a critical component of secure information systems. Data Intrusion Detection Processing System often contains a lot of redundancy and noise features, bringing the system a large amount of computing resources, a long training time, a poor real-time, and a bad detection rate. For high dimensional data, feature selection can find the information-rich feature subset, thus enhance...
When collecting network connection information, we can not obtain a complete data set at once, which result in SVM training insufficiently and high error rate of prediction. To solve this problem, this paper proposes a new method that combines support vector machine with clustering algorithm, based on analyzing the relation between boundary support vectors and KKT condition. In the method, firstly,...
Unauthorized network address translation (NAT) devices may be a significant security problem. They provide unrestricted access to any number of hosts connecting to them. Some attackers may use computers hidden behind NAT devices to conduct malicious activities such as denial of service. An algorithm is proposed in this work to detect hosts hidden behind NAT. Different from previous researches, the...
Using frequency weighted mining algorithm with real-time data processing capability to calculate each system call's frequency value for existed audit records, and we got a vector set of progress. The vector set was linearly scanned and its progresses were labeled as ??normal?? or ??attack?? according to their distance relations. Then we got a SVM training set without man-made supervision. Finally,...
Intelligent algorithms being applied in intrusion detection system (IDS) becomes a tendency in recent years. This paper presents a new method of hybrid detection based on BPSO-SVM, a mixed algorithm that is composed of modified binary particle swarm optimization (BPSO) and support vector machine (SVM). This algorithm proposes a simultaneous feature selection and SVM parameters optimization. Experiments...
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