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Review:botnet detection techniques: review, future trends, and issue

Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第11期15卷 943-983页

作者： Ahmad KARIM Rosli Bin SALLEH Muhammad SHIRAZ Syed Adeel Ali SHAH Irfan AWAN Nor Badrul ANUAR Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science University of Bradford Bradford BD7 1DP UK

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service(DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

关键词： botnet detection Anomaly detection Network security Attack Defense Taxonomy

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The Machine Learning Ensemble for Analyzing Internet of Things Networks:botnet detection and Device Identification

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Computer Modeling in Engineering & Sciences 2024年第11期141卷 1495-1518页

作者： Seung-Ju Han Seong-Su Yoon Ieck-Chae Euom System Security Research Center Chonnam National UniversityGwangju61186Republic of Korea

The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various ***,this advancement has also resulted in a notable surge in cyberattacks,notably *** a result,research on network analysis has become *** learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based *** this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed *** results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to single-algorithm *** results also suggest that the proposed framework can flexibly adapt to general IoT network analysis *** existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues.

关键词： Internet of Things machine learning traffic analysis botnet detection device identification

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An Optimized Approach to Deep Learning for botnet detection and Classification for Cybersecurity in Internet of Things Environment

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Computers, Materials & Continua 2024年第8期80卷 2331-2349页

作者： Abdulrahman Alzahrani Department of Computer Science and Engineering College of Computer Science and EngineeringUniversity of Hafr Al BatinP.O.Box 1803Hafr Al Batin39524Saudi Arabia

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS *** detection of botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected *** detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual *** learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device *** addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to *** ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT *** overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)*** GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT *** data preprocessing,the min-max data normalization approach is primarily *** GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature ***,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet ***,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM *** experimental validation of the GTODL-BADC technique can be tested on a benchmark *** simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

关键词： botnet detection internet of things gorilla troops optimizer hyperparameter tuning intrusion detection system

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BotGuard： Lightweight Real-Time botnet detection in Software Defined Networks

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Wuhan University Journal of Natural Sciences 2017年第2期22卷 103-113页

作者： CHEN Jing CHENG Xi DU Ruiying HU Li WANG Chiheng State Key Laboratory of Software Engineering/School of Computer Wuhan University Wuhan 430072 Hubei China

The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network （SDN） to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph （CLI-graph） to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental resuits prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.

关键词： botnet detection software defined network graph theory

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Double DQN Method For botnet Traffic detection System

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Computers, Materials & Continua 2024年第4期79卷 509-530页

作者： Yutao Hu Yuntao Zhao Yongxin Feng Xiangyu Ma School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China Graduate School Shenyang Ligong UniversityShenyang110159China

In the face of the increasingly severe botnet problem on the Internet,how to effectively detect botnet traffic in realtime has become a critical *** the existing deepQnetwork(DQN)algorithminDeep reinforcement learning can solve the problem of real-time updating,its prediction results are always higher than the actual *** botnet traffic detection,although it performs well in the training set,the accuracy rate of predicting traffic is as high as%;however,in the test set,its accuracy has declined,and it is impossible to adjust its prediction strategy on time based on new data ***,in the new dataset,its accuracy has declined ***,this paper proposes a botnet traffic detection system based on double-layer DQN(DDQN).Two Q-values are designed to adjust the model in policy and action,respectively,to achieve real-time model updates and improve the universality and robustness of the model under different data *** show that compared with the DQN model,when using DDQN,the Q-value is not too high,and the detectionmodel has improved the accuracy and precision of botnet ***,when using botnet data sets other than the test set,the accuracy and precision of theDDQNmodel are still higher than DQN.

关键词： DQN DDQN deep reinforcement learning botnet detection feature classification

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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 *** feature-extract-ing machine learning(ML)methods and deep learning(DL)models such as convolutional neural networks and long short-term memory have been ***,the performance of these approaches varies with different types of *** features in the ML methods can characterize random-looking DGAs better than Word-looking *** improve the classification performance on word-looking DGAs,subword tokenization is employed for the DL *** experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking *** then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the *** 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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Analysis on the time-domain characteristics of botnets control traffic

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The Journal of China Universities of Posts and Telecommunications 2011年第2期18卷 106-113页

作者： LI Wei-min MIAO Chen LIU Fang LEI Zhen-ming School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

botnets are networks composed with malware-infect ed *** are designed and organized to be controlled by an *** victims are infected through their inappropriate network behaviors in most cases,the Internet protocol（IP） addresses of infected bots are ***,a bot can get an IP address through dynamic host configuration protocol（DHCP）,so they need to get in touch with the controller initiatively and they should attempt continuously because a controller can＇t be always *** whole process is carried out under the command and control（C＆C） *** goal is to characterize the network traffic under the C＆C channel on the time *** analysis draws upon massive data obtained from honeynet and a large Internet service provider（ISP） *** extract and summarize fingerprints of the bots collected in our ***,with the fingerprints,we use deep packet inspection（DPI） Technology to search active bots and controllers in the ***,we gather and analyze flow records reported from network traffic monitoring *** this paper,we propose a flow record interval analysis on the time domain characteristics of botnets control traffic,and we propose the algorithm to identify the communications in the C＆C channel based on our *** that,we evaluate our approach with a 3.4 GB flow record trace and the result is *** addition,we believe that our work is also useful information in the design of botnet detection schemes with the deep flow inspection（DFI） technology.

关键词： botnet detection netflow record time domain analysis deep flow inspection

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BotSward: Centrality Measures for Graph-Based Bot detection Using Machine Learning

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Computers, Materials & Continua 2023年第1期74卷 693-714页

作者： Khlood Shinan Khalid Alsubhi M.Usman Ashraf Department of Computer Science College Computer Science in Al-LeithUmm Al-Qura UniversityMecca 21421Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science GC Women University SialkotPakistan

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the *** detection using machine learning(ML)with flow-based features has been extensively studied in the *** flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious ***,Graph-Based Bot detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network *** purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and *** propose BotSward,a graph-based bot detection system that is based on *** apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the *** efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,*** is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art *** proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.

关键词： Network security botnet detection graph-based features machine learning measure centrality

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A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

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Computer Systems Science & Engineering 2022年第12期43卷 915-930页

作者： Attia Qammar Ahmad Karim Yasser Alharbi Mohammad Alsaffar Abdullah Alharbi Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan College of Computer Science and Engineering University of HailHa’il81451Saudi Arabia

Smartphone devices particularly Android devices are in use by billions of people everywhere in the ***,this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control(C&C)method to expand malicious *** present,mobile botnet attacks launched the Distributed denial of services(DDoS)that causes to steal of sensitive data,remote access,and spam generation,***quently,various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic *** this paper,a novel hybrid model,the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is ***,results are evaluated using machine learning *** Random Forest(RF)classifier outperform as compared to other ML techniques i.e.,Naïve Bayes(NB),Support Vector Machine(SVM),and Simple Logistic(SL).Our proposed framework achieved 97.48%accuracy in the detection of botnet ***,some future research directions are highlighted regarding botnet attacks detection for the entire community.

关键词： Android botnet botnet detection hybrid analysis machine learning classifiers mobile malware

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