A network security system includes a system data store capable of storing a variety of data associated with an encrypted computer network and communications transmitted thereon, a communication interface supporting communication over a communication channel and a system processor. Data corresponding to communications transmitted over the encrypted communication network are received. One or more tests are applied to the received data to determine whether a particular communication represents a potential security violation. An alarm may be generated based upon the results of the applied test or tests.

Method and system for encrypted network management and intrusion detection

Hierarchical clustering of mixed data based on distance hierarchy

A parallel genetic local search algorithm for intrusion detection in computer networks

This paper proposes a novel intrusion detection approach by applying ant colony optimization for feature selection and SVM for detection. The intrusion features are represented as graph-ere nodes, with the edges between them denoting the adding of the next feature. Ants traverse through the graph to add nodes until the stopping criterion is satisfied. The fisher discrimination rate is adopted as the heuristic information for ants' traversal. In order to avoid training of a large number of SVM classifier, the least square based SVM estimation is adopted. Initially, the SVM is trained based on grid search method to obtain discrimination function using the training data based on all features available. Then the feature subset produced during the ACO search process is evaluated based on their abilities to reconstruct the reference discriminative function using linear least square estimation. Finally SVM is retrained using the train data based on the obtained optimal feature subset to obtain intrusion detection model. The MIT's KDD Cup 99 dataset is used to evaluate our present method, the results clearly demonstrate that the method can be an effective way for intrusion feature selection and detection.

Ant colony optimization based network intrusion feature selection and detection

Research highlights? We present three kinds of genetic fuzzy systems for intrusion detection problem. ? These IDSs can detect normal and abnormal behaviors in computer networks efficiently. ? Computer simulations demonstrate high performance of the proposed IDSs.? GFSs are able to develop accurate and also interpretable intrusion detection systems. The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in detecting intrusion in a computer network according to three different types of genetic fuzzy systems.

Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

A network intrusion detection model based on artificial immune theory is proposed in this paper. In this model, self patterns and non-self patterns are built upon frequent behaviors sequences, then a simple but efficient algorithm for encoding patterns is proposed. Based on the result of encoding, another algorithm for creating detectors is presented, which integrates a negative selection with the clonal selection. The algorithm performance is analyzed, which shows that this method can shrink each generation scale greatly and create a good niche for patterns evolving.

Artificial immune theory based network intrusion detection system and the algorithms design

Color histograms are widely used for content-based image retrieval because they are trivial and effective to compute, not sensitive to size and orientation, and robustly tolerate movement of objects in the image and changes in camera viewpoint. However, a color histogram only records an image's overall color compositions and no any spatial information is included, and also retrieval precision is limited. In this paper, a color-spatial histogram, which incorporates spatial information with color compositions without sacrificing the robustness of color histograms, is proposed. The purpose is considering the representation role of color compositions and spatial information in the meantime. Each entry in a color-spatial histogram is the color frequency and corresponding position information of the virtual boundary. Experimental evidence suggests that this new color-spatial histogram outperform not only the traditional color histogram method but also the other color-spatial histogram methods for image retrieval.

Integrating color and spatial information for content-based image retrieval in large image database

In this paper, we present a novel and efficient algorithm, WeiSC, for clustering categorical data, which is not only accurate but also displays good scalability. It is mainly based on weighted support, which, as defined by us, calculates the similarity between a new tuple and existing clusters. The new tuple is assigned to the cluster with largest similarity. As an example, we apply this algorithm in IDS and perform some research.

http://ieeexplore.ieee.org/iel5/8324/25997/01176717.pdf

Xiang-Rong Yang

Papers

Artificial immune theory based network intrusion detection system and the algorithms design

Integrating color and spatial information for content-based image retrieval in large image database

A novel clustering algorithm based on weighted support and its application

In-Situ Reduction of Nico Bimetal Anchored on Porous Straw-Derived Biochar Composites for Boosting Microwave Absorption Properties