Simulation of Network Security Situation Prediction Model Based on Multi-Objective ant Colony Optimization Algorithm
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On a global scale, incidents of internet intrusion and information theft have occurred from time to time, and even a large area of network paralysis is not rare. NSSP (Network Security Situation Prediction) is to find the threats and risks existing in the network in the future by using certain forecasting methods, so that network managers can effectively protect the network in advance. In this paper, NSSP model simulation based on multi-objective ACO (Ant colony optimization) algorithm is studied. In this paper, the NSSP model based on multi-objective ACO algorithm is adopted, that is, quantum theory is introduced on the basis of ACO, and quantum computing is integrated with ant colony to solve multi-objective problems. The simulation results show that the prediction results of NSSP model based on multi-objective ACO are MAE =0.02547 and MAPE =6.73254%, and the prediction effect is obviously improved compared with that of ordinary prediction model. The NSSP model based on multi-objective ACO proposed in this paper has accurate prediction ability.Cite
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Adaptability
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In response to the growing number of network security threats, this paper gives a new design based on the combination of initiative and passive network intrusion detection system. The system uses initiative expert system for efficient intrusion detection, and integrates honeypot technology to extract and update the attack knowledge base. It possesses a certain autonomous learning and self-adaptation.
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Aiming at some deficiencies of existing network intrusion detection system, the paper proposes a network intrusion detection system model based on data mining, applying data mining technology to network intrusion detection, and constructed the final test results of the system on the basis of Snort design. Experimental results demonstrate that this data mining based on cluster algorithm can effectively establish models of network normal activity and significantly accelerate intrusion detection, whilst its association analyzer can effectively unearth some new intrusion patterns from abnormal logs, and automatically construct intrusion detection rules.
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This paper introduces network security System based on intrusion detection technique. Describe the definition and the classification of intrusion detection introduce the gengric intrusion detection model, Then we design a network security system based on intrusion deception.
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The firewall and intrusion detection (ID) are hot topics in the field of the network security.They protect different aspect in the network.After particular introduction and analysis,this paper draws a conclusion that intrusion detection system (IDS) is irreplaceable role in the network security.
Firewall (physics)
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this article analyze the main security problems of present campus network and put forward the necessity of intrusion detection system.Build the experiment environment to do the analysis of Snort intrusion detection technology and honeypot technology.
Honeypot
Campus network
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Most traditional techniques in intrusion detection are mining the rule patterns of each attacks' features from the data we have known,then match the new data with these rules.However,the main problem of rule based intrusion detection techniques is that the current rule patterns can not effectively manage the new continuously changing intrusion detection attacks.To deal with the problem,data mining based intrusion detection methods have been the hot fields in intrusion detection research.An outlier detection based adaptive intrusion detection framework is proposed in this paper.In the proposed framework,the outliers are firstly detected by similarity coefficient.And then,the clusters are built on the detected outlier data set and the improved association rule algorithm is employed on the clusters.Finally,the rules generated by association rule algorithm will be adaptively added into the current intrusion detection rule base.The experiment platform was based on IDS Snort and IDS Informer was employed to simulate the attack and test.The experiments performed on simulated data and KDD99 from UCI data set have shown the effectiveness of proposed methods.
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Similarity (geometry)
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This paper analyzed the application of data mining in intrusion detection system based on a research on IDS,data mining and the types and limitations of traditional detection methods.According to the characteristic of IDS,it was pointed out that these limitations could be overcome by the data mining technology.The intrusion detection technology improved and optimized the association and cluster rules,which resolved its own disadvantages,including its inability to presage the number of the best cluster and its fine classification.Such an improvement reduces omissions and misstatement,and improves the efficiency of IDS.Experimental results show that this algorithm is feasible.
Realization (probability)
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through the research and analysis of intrusion detection systems and distributed network intrusion detection systems, this paper designs a campus network security system based on distributed network intrusion detection technology, and then implements it. This system can not only lift the user's worries, but also provide a larger extent of security protection for the computer network system, to achieve good security effect.
Campus network
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