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Today's world is made of electronic networks. Everyday huge amount of sensitive data are passed through these networks. These networks are the backbones of the industries like banking, transportation, healthcare, defense, communication etc. So securing the data passed through these networks is essential. Organizations are investing more and more money to secure their data from the attackers. On the...
Due to on-demand, ubiquitous and shared resources facility, the cloud computing attract more user towards its services to use it. Cloud services are provided through the Internet, so there is possibility of attacks over the Internet. User to root, Denial of service(DoS) and Port scanning are the possible attacks over the Internet. These types of attacks are example of network intrusion. There is a...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
The parameters of Support Vector Machine (SVM) are optimized using heuristic genetic algorithm and then to detect the network intrusion behavior. The heuristic real-coded genetic algorithm is used to optimize the best parameters of SVM with Gauss kernel aimed at the classification accuracy of the model. The classification accuracy is largely improved. Experimental results show that this method has...
In Stream data classification intrusion detection happens when a completely new kind of attack occurs in the traffic. Novel class detection approach solves the problem of intrusion detection based on ensemble technique of clustering and classification on feature evaluation technique. Feature evolution process faced a problem of exact selection of cluster midpoint for the process of clusters which...
Host Intrusion detection systems (HIDS) are increasingly emerging techniques for information security on host based applications. These systems should be designed to prevent unauthorized access of system resources and data. Many intelligent learning techniques are currently being applied to the large volumes of data for the construction of an efficient host intrusion detection system. This paper represents...
In this work, we consider network intrusion detection using fuzzy genetic algorithm to classify network attack data. Fuzzy rule is a machine learning algorithm that can classify network attack data, while a genetic algorithm is an optimization algorithm that can help finding appropriate fuzzy rule and give the best/optimal solution. In this paper, we consider both well-known KDD99 dataset and our...
Based on the advantages and disadvantages of the improved GA and LM algorithm, in this paper, the Hybrid Neural Network Algorithm (HNNA) is presented. Firstly, the algorithms use the advantage of the improved GA with strong whole searching capacity to search global optimal point in the whole question domain. Then, it adopts the strongpoint of the LM algorithm with fast local searching to fine search...
A intrusion detection system model based on particle swarm reduction was proposed in this paper. Though the experiment of this model, it turns out that the improved algorithm of quantum particle swarm can get the minimal reduction, improve particle convergence and make particles trapped into local minima more difficult. The algorithm is faster than GA and has a high rate of network intrusion detection.
The thesis proposes a hybrid intrusion detection model based on the parallel genetic algorithm and the rough set theory. Due to the difficult for the status of intrusion detection rules. This model, taking the advantage of rough set's streamline the edge to data and genetic algorithm's high parallelism, succeeds in introducing the genetic-rough set theory to the instrusion detection. The application...
In the Network Intrusion Detection, the large number of features increases the time and space cost, besides the irrelative redundant characteristics make the detection accuracy dropped. In order to improve detection accuracy and efficiency, a new Feature Selection method based on Rough Sets and improved Genetic Algorithms is proposed for Network Intrusion Detection. Firstly, the features are filtered...
The use of computer networks has increased significantly in recent years. This proliferation, in combination with the interconnection of networks via the Internet, has drastically increased their vulnerability to attack by malicious agents. The wide variety of attack modes has exacerbated the problem in detecting attacks. Many current intrusion detection systems (IDS) are unable to identify unknown...
To make an immune-inspired network intrusion detection system (IDS) effective, this paper proposes a new framework, which includes our avidity-model based clonal selection (AMCS) algorithm as core element. The AMCS algorithm uses an improved representation for antigens (corresponding to network access patterns) and detectors (corresponding to detection rules). In particular, a bio-inspired technique...
In this paper, a new intrusion detection method based on support vector machines improved by artificial immunization algorithm is presented. Support vector machines (SVM) has been well recognized as a powerful computational tool for problems with nonlinearity had high dimensionalities. Right setting parameters are very crucial to learning results and generalization ability of SVM. But empirical parameters...
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...
Features extraction in NIDS is a NP-hard problem. To improve the search speed and avoid local minimal, immune is induced into features extraction in NIDS. Similar degree and chroma are defined. Relationship based on NIDS feature code and immune operators are constructed to avoid local minimal and improve speed and quality of the found solution. Experiments are based on standard data set and use genetic...
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 technique has become increasingly important in the area of network security research. It is innovative that various soft computing approaches have been applied to the intrusion detection field. This paper presents an intelligent intrusion detection system which incorporates several soft computing techniques to implement either misuse or anomaly detection. Genetic algorithm is used...
A community intrusion detection system based on classify support vector machine (SVM) is presented in this paper. This system is composed of ARM (advanced RISC machines) data acquisition nodes, wireless mesh network and control centre. The data acquisition node uses sensors to collect information and processes them by image detection algorithm, and then transmits information to control centre with...
With the growing rate of network attacks, intelligent methods for detecting new attacks have attracted increasing interest. This paper presents an approach incorporating several soft computing techniques to construct a hierarchical neuro-fuzzy inference intrusion detection system which can implement either misuse or anomaly detection. In the proposed system principal component analysis neural network...
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