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DNNBoT: Deep Neural Network-Based botnet Detection and Classification

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Computers, Materials & Continua 2022年第4期71卷 1729-1750页

作者： Mohd Anul Haq Mohd Abdul Rahim Khan Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah 11952Saudi Arabia

The evolution and expansion of IoT devices reduced human efforts,increased resource utilization, and saved time;however, IoT devices createsignificant challenges such as lack of security and privacy, making them morevulnerable to IoT-based botnet attacks. There is a need to develop efficientand faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, Deep Neural Network(DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-knownIoT botnet attacks such as Mirai and BASHLITE from nine compromisedindustrial-grade IoT devices. The utilization of PCA was made to featureextraction and improve effectual and accurate botnet classification in IoTenvironments. The models were designed based on rigorous hyperparameterstuning with GridsearchCV. Early stopping was utilized to avoid the effects ofoverfitting and underfitting for both DNN models. The in-depth assessmentand evaluation of the developed models demonstrated that accuracy andefficiency are some of the best-performed models. The novelty of the presentinvestigation, with developed models, bridge the gaps by using a real datasetwith high accuracy and a significantly lower false alarm rate. The results wereevaluated based on earlier studies and deemed efficient at detecting botnetattacks using the real dataset.

关键词： botnet network monitoring machine learning deep neural network IoT threat

A Learning Evasive Email-Based P2P-Like botnet

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China Communications 2018年第2期15卷 15-24页

作者： Zhi Wang Meilin Qin Mengqi Chen Chunfu Jia Yong Ma College of Computer and Control Engineering Nankai University Tianjin 300350 China Information Security Evaluation Center of Civil Aviation Civil Aviation University of China Tianjin 300300 China Key Lab. on High Trusted Information System in Hebei Province Baoding 071002 China

Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.

关键词： malware botnet learning evasion command and control

Pattern Analysis and Regressive Linear Measure for botnet Detection

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Computer Systems Science & Engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards botnets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： botnet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

Securing Consumer Internet of Things for botnet Attacks: Deep Learning Approach

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Computers, Materials & Continua 2022年第11期73卷 3199-3217页

作者： Tariq Ahamed Ahanger Abdulaziz Aldaej Mohammed Atiquzzaman Imdad Ullah Mohammed Yousuf Uddin College of Computer Engineering and Science Prince Sattam bin Abdulaziz University Al-Kharj11942Saudi Arabia School of Computer Science University of Oklahoma Norman73019-6151United States

DDoS attacks in the Internet of Things(IoT)technology have increased significantly due to its spread adoption in different industrial *** purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT ***,an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory(BLRNN)is presented using a unique Deep Learning(DL)*** text identification and translation of attack data segments into tokenized form,word embedding is *** performance analysis of the presented technique is performed in comparison to the state-of-the-art DL ***,Accuracy(98.4%),Specificity(98.7%),Sensitivity(99.0%),F-measure(99.0%)and Data loss(92.36%)of the presented BLRNN detection model are determined for identifying 4 attacks over botnet(Mirai).The results show that,although adding cost to each epoch and increasing computation delay,the bidirectional strategy is more superior technique model over different data instances.

关键词： Internet of Things deep learning security DDoS attack botnet

An efficient botnet detection approach based on feature learning and classification

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Journal of Control and Decision 2023年第1期10卷 40-53页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Computer Science and Engineering Easwari Engineering CollegeChennaiIndia DMI College of Engineering ChennaiIndia

Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.

关键词： botnet machine learning AdaBoost grey wolf optimisation exploration exploitation

Detecting Encrypted botnet Traffic Using Spatial-Temporal Correlation

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China Communications 2012年第10期9卷 49-59页

作者： Chen Wei Yu Le Yang Geng College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 P. R. China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003 P. R. China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 P. R. China

In this paper, we to detect encrypted botnet propose a novel method traffic. During the traffic preprocessing stage, the proposed payload extraction method can identify a large amount of encrypted applications traffic. It can filter out a large amount of non-malicious traffic, greatly in, roving the detection efficiency. A Sequential Probability Ratio Test （SPRT）-based method can find spatialtemporal correlations in suspicious botnet traffic and make an accurate judgment. Experimental resuks show that the false positive and false nega- tive rates can be controlled within a certain range.

关键词： botnet encrypted traffic spatial-tenmporal correlation

Hybrid Detection and Tracking of Fast-Flux botnet on Domain Name System Traffic

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China Communications 2013年第11期10卷 81-94页

作者：邹福泰章思宇饶卫雄 School of Information Security Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University

Fast-flux is a Domain Name System(DNS)technique used by botnets to organise compromised hosts into a high-availability,loadbalancing network that is similar to Content Delivery Networks(CDNs).Fast-Flux Service Networks(FFSNs)are usually used as proxies of phishing websites and malwares,and hide upstream servers that host actual *** this paper,by analysing recursive DNS traffic,we develop a fast-flux domain detection method which combines both real-time detection and long-term *** results demonstrate that our solution can achieve significantly higher detection accuracy values than previous flux-score based algorithms,and is light-weight in terms of resource *** evaluate the performance of the proposed fast-flux detection and tracking solution during a 180-day period of deployment on our university’s DNS *** on the tracking results,we successfully identify the changes in the distribution of FFSN and their roles in recent Internet attacks.

关键词： domain name system botnet fast-flux

A Comparative Study of Machine Learning Algorithms and Their Ensembles for botnet Detection

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Journal of Computer and Communications 2018年第5期6卷 119-129页

作者： Songhui Ryu Baijian Yang Purdue University West Lafayette USA

A botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect botnet traffic can be a promising feature of Intrusion Detection System. Although such system has been applying various machine learning techniques, comparison of machine algorithms including their ensembles on botnet detection has not been figured out. In this study, not only the three most popular classification machine learning algorithms—Naive Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen classifier are tested to see if they indeed provide enhanced predictions on botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time of each classifier and its F measure and MCC score.

关键词： Machine Learning Ensemble Method botnet CTU-13

A Hierarchical Hybrid Structure for botnet Control and Command

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A Hierarchical Hybrid Structure for Botnet Control and Comma...