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A Machine Learning-Based distributed denial of service Detection Approach for Early Warning in Internet Exchange Points

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Computers, Materials & Continua 2023年第8期76卷 2235-2259页

作者： Salem Alhayani Diane R.Murphy School of Technology and Innovation College of Business InnovationLeadershipand Technology(BILT)Marymount UniversityArlingtonVirginia22207USA

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld *** exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

关键词： Internet exchange point Saudi Arabia IXP(SAIXP) distributed denial of service chi-square feature selection machine learning

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Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of distributed denial-of-service(DDoS)Attacks in Cloud

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Computer Systems Science & Engineering 2023年第10期47卷 1109-1123页

作者： S.Sureshkumar G.K.D.Prasanna Venkatesan R.Santhosh Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatore641021India Dean Engineering Faculty EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

关键词： Cloud computing distributed denial of service intrusion detection system adaptive butterfly optimization algorithm deep neural network

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DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

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Computers, Materials & Continua 2023年第5期75卷 3571-3588页

作者： Yousef Sanjalawe Turke Althobaiti Cybersecurity Department Faculty of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan Computer Science Department PY CollageNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Computer Science Department Faculty of ScienceNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Remote Sensing Northern Border University(NBU) 9280 NBUAr’arSaudi Arabia

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,distributed denial of service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

关键词： CIC IDS 2017 cloud computing distributed denial of service ensemble feature selection intrusion detection system

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Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

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Computers, Materials & Continua 2022年第10期73卷 1403-1416页

作者： Mohammad Adnan Aladaileh Mohammed Anbar Iznan H.Hasbullah Abdullah Ahmed Bahashwan Shadi Al-Sarawn National Advanced IPv6 Centre of Excellence Universiti Sains MalaysiaPenangMalaysia

The emergence of a new network architecture,known as Software Defined Networking(SDN),in the last two decades has overcome some drawbacks of traditional networks in terms of performance,scalability,reliability,security,and network management.However,the SDN is vulnerable to security threats that target its controller,such as low-rate distributed denial of service(DDoS)attacks,The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component.Therefore,there is an urgent need to propose a detection approach for this type of attack with a high detection rate and low false-positive rates.Thus,this paper proposes an approach to detect low-rate DDoS attacks on the SDN controller by adapting a dynamic threshold.The proposed approach has been evaluated using four simulation scenarios covering a combination of low-rate DDoS attacks against the SDN controller involving(i)a single host attack targeting a single victim;(ii)a single host attack targeting multiple victims;(iii)multiple hosts attack targeting a single victim;and(iv)multiple hosts attack targeting multiple victims.The proposed approach’s average detection rates are 96.65%,91.83%,96.17%,and 95.33%for the above scenarios,respectively;and its average false-positive rates are 3.33%,8.17%,3.83%,and 4.67%for similar scenarios,respectively.The comparison between the proposed approach and two existing approaches showed that it outperformed them in both categories.

关键词： Attack detection controller dynamic threshold entropy algorithm distributed denial of service software defined networking static threshold

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Smart Contract Based DDoS Attack Traceability Audit Mechanism in Intelligent IoT

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China Communications 2023年第8期20卷 54-64页

作者： Zhuohao Wang Weiting Zhang Runhu Wang Ying Liu Chenyang Xu Chengxiao Yu Institute of Scientific and Technical Information of China Beijing 100038China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

In this paper,we focus on providing data provenance auditing schemes for distributed denial of service(DDoS)defense in intelligent internet of things(IoT).To achieve effective DDoS defense,we introduce a two-layer collaborative blockchain framework to support data auditing.Specifically,using data scattered among intelligent IoT devices,switch gateways self-assemble a layer of blockchain in the local autonomous system(AS),and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle,to obtain a global security model.To optimize the processing delay of the security model,we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements.Since the flood of identity spoofing packets,it is difficult to solve the identity consistency of data with traditional detection methods,and accountability cannot be pursued afterwards.Thus,we proposed a Packet Traceback Telemetry(PTT)scheme,based on in-band telemetry,to solve the problem.Specifically,the PTT scheme is executed on the distributed switch side,the controller to schedule and select routing policies.Moreover,a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources.Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path,reduce the resource consumption compared with existing tracing scheme.Data tracing audit method has fine-grained detection and feasible performance.

关键词： smart contract Internet of Things distributed denial of service telemetry audit

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Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

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Computers, Materials & Continua 2023年第10期77卷 491-515页

作者： Shadi Al-Sarawi Mohammed Anbar Basim Ahmad Alabsi Mohammad Adnan Aladaileh Shaza Dawood Ahmed Rihan National Advanced IPv6 Centre(NAv6) University Sains MalaysiaGelugorPenang11800Malaysia Applied College Najran UniversityKing Abdulaziz StreetP.O.Box 1988NajranSaudi Arabia Cybersecurity Department School of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

关键词： Internet of Things IPv6 over low power wireless personal area networks Routing Protocol for Low-Power and Lossy Networks Internet Protocol Version 6 distributed denial of service wireless sensor networks

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A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

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Computer Systems Science & Engineering 2023年第6期45卷 2553-2567页

作者： Anitha Thangasamy Bose Sundan Logeswari Govindaraj Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai-600025TamilndauIndia

The recent development of cloud computing offers various services on demand for organization and individual users,such as storage,shared computing space,networking,etc.Although Cloud Computing provides various advantages for users,it remains vulnerable to many types of attacks that attract cyber criminals.distributed denial of service(DDoS)is the most common type of attack on cloud computing.Consequently,Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks.Since DDoS attacks have become increasingly widespread,it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks.Further,the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent.In this research work,DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory(LSTM)model have been proposed with NSL-KDD dataset.In Hybrid LSTM method,the Particle Swarm Optimization(PSO)technique,which is combined to optimize the weights of the LSTM neural network,reduces the prediction error.This deep belief network method is used to extract the features of IP packets,and it identifies DDoS attacks based on PSO-LSTM model.Moreover,it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks.The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine(SVM)and LSTM in terms of attack detection performance along with the results of the measurement of accuracy,recall,f-measure,precision.

关键词： Cloud computing distributed denial of service particle swarm optimization long short-term memory attack detection

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Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

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Computer Systems Science & Engineering 2023年第11期47卷 2201-2217页

作者： Muhammad Waqas Nadeem Hock Guan Goh Yichiet Aun Vasaki Ponnusamy Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparPerakMalaysia Higher Colleges of Technology Fujairah Men’s CampusFujairahUAE

Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.

关键词： Botnet attack deep learning distributed denial of service machine learning network security software-defined network

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Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

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Computers, Materials & Continua 2021年第10期69卷 373-391页

作者： Mohammad Aladaileh Mohammed Anbar Iznan H.Hasbullah Yousef K.Sanjalawe Yung-Wey Chong National Advanced IPv6 Centre of Excellence Universiti Sains MalaysiaPenangMalaysia Department of Computer Sciences Northern Border UniversityAr’arKingdom of Saudi Arabia

The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A distributed denial of service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.

关键词： Software-defined networking DDoS attack distributed denial of service Rényi joint entropy

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Detecting DDoS Attacks against Web Server Using Time Series Analysis

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 175-180页

作者： WU Qing-tao SHAO Zhi-qing Department of Computer Science and Engineering East China University of Science and Technology Shanghai 200237 China

distributed denial of service （DDoS） attack is a major threat to the availability of Web service. The inherent presence of self-similarity in Web traffic motivates the applicability of time series analysis in the study of the burst feature of DDoS attack. This paper presents a method of detecting DDoS attacks against Web server by analyzing the abrupt change of time series data obtained from Web traffic. Time series data are specified in reference sliding window and test sliding window, and the abrupt change is modeled using Auto-Regressive （AR） process. By comparing two adjacent nonoverlapping windows of the time series, the attack traffic could be detected at a time point. Combined with alarm correlation and location correlation, not only the presence of DDoS attack, but also its occurring time and location can be deter mined. The experimental results in a test environment are illustrated to justify our method.

关键词： distributed denial of service auto-regressive model time series Web server

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