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network security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

Computer Systems Science & Engineering 2023年第10期47卷 993-1021页

作者： Junfeng Sun Chenghai Li Yafei Song Peng Ni Jian Wang College of Air and Missile Defence Air Force Engineering UniversityXi’an710051China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing100076China

The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.

关键词： network security situation prediction SSA IQPSO TCAN-BiGRU

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Use of subword tokenization for domain generation algorithm classification

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Cybersecurity 2024年第2期7卷 1-12页

作者： Sea Ran Cleon Liew Ngai Fong Law University of Waterloo WaterlooCanada Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityKowloonHong Kong

Domain name generation algorithm(DGA)classification is an essential but challenging problem.Both feature-extract-ing machine learning(ML)methods and deep learning(DL)models such as convolutional neural networks and long short-term memory have been developed.However,the performance of these approaches varies with different types of DGAs.Most features in the ML methods can characterize random-looking DGAs better than Word-looking DGAs.To improve the classification performance on word-looking DGAs,subword tokenization is employed for the DL mod-els.Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs.We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs.Results show that the integrated scheme outperformed existing ML and DL methods,and also the subword DL methods.

关键词： Botnet detection Domain names network security Machine learning-based botnet detection

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AWeb Application Fingerprint Recognition Method Based on Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 887-906页

作者： Yanmei Shi Wei Yu Yanxia Zhao Yungang Jia School of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin300382China College of International Business Zhejiang Yuexiu UniversityShaoxing312030China School of Public Administration Zhejiang Gongshang UniversityHangzhou310018China Tianjin Branch of National Computer network Emergency Response Technical Team/Coordination Center of China Tianjin300100China

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

关键词： Web application fingerprint recognition unsupervised learning clustering algorithm feature extraction automated testing network security

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A security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

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Computers, Materials & Continua 2024年第3期78卷 4049-4069页

作者： Bing Liu Zhe Zhang Shengrong Hu Song Sun Dapeng Liu Zhenyu Qiu Zhejiang Institute of Industry and Information Technology Hangzhou310000China Digital Economy Development Center of Zhejiang Hangzhou310000China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Bank of Suzhou Suzhou215000China Hangzhou Hikvision Digital Technology Co. Ltd.Hangzhou310051China

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

关键词： network security Internet of Things data-tampering attack anomaly detection

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Applying an Improved Dung Beetle Optimizer Algorithm to network Traffic Identification

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Computers, Materials & Continua 2024年第3期78卷 4091-4107页

作者： Qinyue Wu Hui Xu Mengran Liu School of Computer Science Hubei University of TechnologyWuhan430068China

network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO a

关键词： network security network traffic identification data analytics feature selection dung beetle optimizer

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Correlation Composition Awareness Model with Pair Collaborative Localization for IoT Authentication and Localization

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Computers, Materials & Continua 2024年第4期79卷 943-961页

作者： Kranthi Alluri S.Gopikrishnan School of Computer Science and Engineering VIT-AP UniversityAmaravathiAndhra Pradesh522241India

Secure authentication and accurate localization among Internet of Things(IoT)sensors are pivotal for the functionality and integrity of IoT networks.IoT authentication and localization are intricate and symbiotic,impacting both the security and operational functionality of IoT systems.Hence,accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges.To overcome these challenges,recent approaches have used encryption techniques with well-known key infrastructures.However,these methods are inefficient due to the increasing number of data breaches in their localization approaches.This proposed research efficiently integrates authentication and localization processes in such a way that they complement each other without compromising on security or accuracy.The proposed framework aims to detect active attacks within IoT networks,precisely localize malicious IoT devices participating in these attacks,and establish dynamic implicit authentication mechanisms.This integrated framework proposes a Correlation Composition Awareness(CCA)model,which explores innovative approaches to device correlations,enhancing the accuracy of attack detection and localization.Additionally,this framework introduces the Pair Collaborative Localization(PCL)technique,facilitating precise identification of the exact locations of malicious IoT devices.To address device authentication,a Behavior and Performance Measurement(BPM)scheme is developed,ensuring that only trusted devices gain access to the network.This work has been evaluated across various environments and compared against existing models.The results prove that the proposed methodology attains 96%attack detection accuracy,84%localization accuracy,and 98%device authentication accuracy.

关键词： Sensor localization IoT authentication network security data accuracy precise location access control security framework

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WNN-Based network security Situation Quantitative Prediction Method and Its Optimization

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Journal of Computer Science & Technology 2008年第2期23卷 222-230页

作者：赖积保王慧强刘效武梁颖郑瑞娟赵国生 College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China

The accurate and real-time prediction of network security situation is the premise and basis of preventing intrusions and attacks in a large-scale network. In order to predict the security situation more accurately, a quantitative prediction method of network security situation based on Wavelet Neural network with Genetic Algorithm （GAWNN） is proposed. After analyzing the past and the current network security situation in detail, we build a network security situation prediction model based on wavelet neural network that is optimized by the improved genetic algorithm and then adopt GAWNN to predict the non-linear time series of network security situation. Simulation experiments prove that the proposed method has advantages over Wavelet Neural network （WNN） method and Back Propagation Neural network （BPNN） method with the same architecture in convergence speed, functional approximation and prediction accuracy. What is more, system security tendency and laws by which security analyzers and administrators can adjust security policies in near real-time are revealed from the prediction results as early as possible.

关键词： network security situation prediction genetic algorithm wavelet analysis neural network

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network security Situation Evaluation Based on Modified D-S Evidence Theory

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Wuhan University Journal of Natural Sciences 2014年第5期19卷 409-416页

作者： WANG Chundong ZHANG YuKey Laboratory of Computer Vision and System Ministry of Education/Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.

关键词： network security situation evaluation informationfusion D-S evidence theory Bayes network theory

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Research on Key Technologies of network security Situational Awareness for Attack Tracking Prediction

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Chinese Journal of Electronics 2019年第1期28卷 162-171页

作者： KOU Guang WANG Shuo TANG Guangming Artificial Intelligence Research Center National Innovation Institute of Defense Technology Information Engineering University

This paper analyzed the existing network security situation evaluation methods and discovered that they cannot accurately reflect the features of large-scale,synergetic, multi-stage gradually shown by network attack behaviors. For this purpose, the association between attack intention and network configuration information was deep analyzed. Then a network security situation evaluation method based on attack intention recognition was proposed. Unlike traditional method, the evaluation method was based on intruder. This method firstly made causal analysis of attack event and discovered and simplified intrusion path to recognize every attack phases, then realized situation evaluation based on the attack phases. Lastly attack intention was recognized and next attack phase was forecasted based on achieved attack phases, combined with vulnerability and network connectivity. A simulation experiments for the proposed network security situation evaluation model is performed by network examples. The experimental results show that this method is more accurate on reflecting the truth of attack. And the method does not need training on the historical sequence, so the method is more effective on situation forecasting.

关键词： Multi-stage attack Situation evaluation network security Intention recognition Causal analysis

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Application of mobile scanning agent in the network security

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Journal of Systems Engineering and Electronics 2004年第3期15卷 371-376页

作者：闵君冯珊唐超梅纲 DepartmentofAutomaticControlEngineering InstituteofSystemsEngineeringHuazhongUniversityofScienceandTechnologyWuhan430074P.R.China

To enhance the security of network systems, puts forward a kind of software agent is put forward, which has the induction ability of network frameworks and the ability of behavior independence. It is mobile scanning agent. More attentions is paid to expound how to design and realize mobile scanning agent. Besides, it is also explained the programs of mobile scanning agent system. In the end, it expects mobile scanning agent.

关键词： agent network security immunity agent transfer protocol

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