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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

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 researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,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 model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards botnets detection.The 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 on.Here,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 reliability.The 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 domains.The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments.Conspicuously,an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory(BLRNN)is presented using a unique Deep Learning(DL)technique.For text identification and translation of attack data segments into tokenized form,word embedding is employed.The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques.Specifically,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

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基于改进YOLOv8的火灾目标检测系统

人工智能与机器人研究 2024年第1期13卷 56-65页

作者：马冉顾宏天津工业大学物理科学与技术学院天津

火灾发生初期,烟雾状态变化多端火焰的尺寸大小也非常小,现有的目标检测算法面对这复杂情况下会出现检测速度慢、检测准确率低。针对类似这样的问题,本文提出了基于改进YOLOv8的火灾目标检测系统。在YOLOv8的骨干网络末端添加botnet结构... 详细信息

火灾发生初期,烟雾状态变化多端火焰的尺寸大小也非常小,现有的目标检测算法面对这复杂情况下会出现检测速度慢、检测准确率低。针对类似这样的问题,本文提出了基于改进YOLOv8的火灾目标检测系统。在YOLOv8的骨干网络末端添加botnet结构,用来增强网络对火灾的特征提取,在YOLOv8的头部末端引入EMA注意力机制防止权重剧烈变化。改进的YOLOv8模型提高了目标检测的精确度。实验的结果表明,改进的YOLOv8模型与YOLOv8模型对比,改进的YOLOv8模型在mAP上提高了2.3%、火灾与烟雾的预测准确率也分别提高了1.4%和1%,进一步说明改进的YOLOv8模型可以满足对火灾的目标检测。

关键词：目标检测 YOLOv8 botnet EMA

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基于改进YOLOv5的草莓病害检测

云南民族大学学报(自然科学版) 2024年

作者：杨宇游潘文林云南民族大学电气信息工程学院

为解决草莓病害识别技术落后，识别精度不高的问题，本研究在YOLOv5的基础上，提出一种改进YOLOv5的草莓病害检测算法。针对草莓病害特征引入了botnet模块，并将原有的NMS替换为GIoU-NMS，提升了对草莓病害的检测精度。改进后的YOLOv5... 详细信息

为解决草莓病害识别技术落后，识别精度不高的问题，本研究在YOLOv5的基础上，提出一种改进YOLOv5的草莓病害检测算法。针对草莓病害特征引入了botnet模块，并将原有的NMS替换为GIoU-NMS，提升了对草莓病害的检测精度。改进后的YOLOv5算法相较于原算法，精确度提升了2.1个百分点，平均精度AP上升了1.2个百分点。实验结果表明，改进后的YOLOv5草莓病害检测算法提升了算法的效率和性能，检测效果优于传统的YOLOv5s算法。

关键词：目标检测 YOLOv5 botnet GIoU-NMS 病害检测

DGA-Based botnet Detection Toward Imbalanced Multiclass Learning

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Tsinghua Science and Technology 2021年第4期26卷 387-402页

作者： Yijing Chen Bo Pang Guolin Shao Guozhu Wen Xingshu Chen College of Cybersecurity Sichuan UniversityChengdu 610065China Cybersecurity Research Institute Sichuan UniversityChengdu 610065China

botnets based on the Domain Generation Algorithm(DGA) mechanism pose great challenges to the main current detection methods because of their strong concealment and robustness. However, the complexity of the DGA family and the imbalance of samples continue to impede research on DGA detection. In the existing work, the sample size of each DGA family is regarded as the most important determinant of the resampling proportion;thus,differences in the characteristics of various samples are ignored, and the optimal resampling effect is not achieved.In this paper, a Long Short-Term Memory-based Property and Quantity Dependent Optimization(LSTM.PQDO)method is proposed. This method takes advantage of LSTM to automatically mine the comprehensive features of DGA domain names. It iterates the resampling proportion with the optimal solution based on a comprehensive consideration of the original number and characteristics of the samples to heuristically search for a better solution around the initial solution in the right direction;thus, dynamic optimization of the resampling proportion is realized.The experimental results show that the LSTM.PQDO method can achieve better performance compared with existing models to overcome the difficulties of unbalanced datasets;moreover, it can function as a reference for sample resampling tasks in similar scenarios.

关键词： botnet Domain Generation Algorithm(DGA) multiclass imbalance resampling

A Machine Learning-Based botnet Malicious Domain Detection Technique for New Business

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国际计算机前沿大会会议论文集 2023年第2期 191-201页

作者： Aohan Mei Zekun Chen Jing Zhao Dequan Yang School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Continuing Education Beijing Institute of TechnologyBeijing 100081China Network Information Technology Center Beijing Institute of TechnologyBeijing 100081China

In the new network business,the danger of botnets should not be underestimated.botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receive commands from the botmaster,carrying out further attack activities.Therefore,a system based onmachine learning to dichotomizeDNSdomain access is designed,which can instantly detectDGAdomain names and thus quickly dispose of infected computers to avoid spreading the virus and further damage.In the comparison,the bidirectional LSTM model slightly outperformed the unidirectional LSTM network and achieved 99%accuracy in the open dataset classification task.

关键词： botnet Machine Learning LSTM Domain Generation Algorithm Detection

MBB-IoT:Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

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Computers, Materials & Continua 2023年第8期76卷 2095-2119页

作者： Yi Qing Xiangyu Liu Yanhui Du Department of Artificial Intelligence People’s Public Security University of ChinaBeijing100038China Department of Cybersecurity People’s Public Security University of ChinaBeijing100038China

Distributed Denial of Service(DDoS)attacks have always been a major concern in the security field.With the release of malware source codes such as BASHLITE and Mirai,Internet of Things(IoT)devices have become the new source of DDoS attacks against many Internet applications.Although there are many datasets in the field of IoT intrusion detection,such as Bot-IoT,ConstrainedApplication Protocol–Denial of Service(CoAPDoS),and LATAM-DDoS-IoT(some of the names of DDoS datasets),which mainly focus on DDoS attacks,the datasets describing new IoT DDoS attack scenarios are extremely rare,and only N-BaIoT and IoT-23 datasets used IoT devices as DDoS attackers in the construction process,while they did not use Internet applications as victims either.To supplement the description of the new trend of DDoS attacks in the dataset,we built an IoT environment with mainstream DDoS attack tools such as Mirai and BASHLITE being used to infect IoT devices and implement DDoS attacks against WEB servers.Then,data aggregated into a dataset namedMBB-IoTwere captured atWEBservers and IoT nodes.After the MBB-IoT dataset was split into a training set and a test set,it was applied to the training and testing of the Random Forests classification algorithm.The multi-class classification metrics were good and all above 90%.Secondly,in a cross-evaluation experiment based on Support Vector Machine(SVM),Light Gradient Boosting Machine(LightGBM),and Long Short Term Memory networks(LSTM)classification algorithms,the training set and test set were derived from different datasets(MBB-IoT or IoT-23),and the test performance is better when MBB-IoT is used as the training set.

关键词： Intrusion detection IoT malware botnet DDoS dataset

Monitoring Peer-to-Peer botnets:Requirements,Challenges,and Future Works

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Computers, Materials & Continua 2023年第5期75卷 3375-3398页

作者： Arkan Hammoodi Hasan Kabla Mohammed Anbar Selvakumar Manickam Alwan Ahmed Abdulrahman Alwan Shankar Karuppayah National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPulau Pinang11800Malaysia

The cyber-criminal compromises end-hosts(bots)to configure a network of bots(botnet).The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer(P2P)networks.The P2P botnets leverage the privileges of the decentralized nature of P2P networks.Consequently,the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures.Some P2P botnets are smarter to be stealthy in their Commandand-Control mechanisms(C2)and elude the standard discovery mechanisms.Therefore,the other side of this cyberwar is the monitor.The P2P botnet monitoring is an exacting mission because the monitoring must care about many aspects simultaneously.Some aspects pertain to the existing monitoring approaches,some pertain to the nature of P2P networks,and some to counter the botnets,i.e.,the anti-monitoring mechanisms.All these challenges should be considered in P2P botnet monitoring.To begin with,this paper provides an anatomy of P2P botnets.Thereafter,this paper exhaustively reviews the existing monitoring approaches of P2P botnets and thoroughly discusses each to reveal its advantages and disadvantages.In addition,this paper groups the monitoring approaches into three groups:passive,active,and hybrid monitoring approaches.Furthermore,this paper also discusses the functional and non-functional requirements of advanced monitoring.In conclusion,this paper ends by epitomizing the challenges of various aspects and gives future avenues for better monitoring of P2P botnets.

关键词： P2P networks botnet P2P botnet botnet monitoring honeypot crawlers