Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain threats.This paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT environments.An effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time complexity.By leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world scenarios.Additionally,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack detection.The experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

Distributed denial-of-service(DDoS)is a rapidly growing problem with the fast development of the Internet.There are multitude DDoS detection approaches,however,three major problems about DDoS attack detection appear in the big data environment.Firstly,to shorten the respond time of the DDoS attack detector;secondly,to reduce the required compute resources;lastly,to achieve a high detection rate with low false alarm rate.In the paper,we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems.We define a network flow abnormal index as PDRA with the percentage of old IP addresses,the increment of the new IP addresses,the ratio of new IP addresses to the old IP addresses and average accessing rate of each new IP address.We design an IP address database using sequential storage model which has a constant time complexity.The autoregressive integrated moving average(ARIMA)trending prediction module will be started if and only if the number of continuous PDRA sequence value,which all exceed an PDRA abnormal threshold(PAT),reaches a certain preset threshold.And then calculate the probability that is the percentage of forecasting PDRA sequence value which exceed the PAT.Finally we identify the DDoS attack based on the abnormal probability of the forecasting PDRA sequence.Both theorem and experiment show that the method we proposed can effectively reduce the compute resources consumption,identify DDoS attack at its initial stage with higher detection rate and lower false alarm rate.

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

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

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.

Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.

Cooperative Detection Method for DDoS Attacks Based on Blockchain

Distributed Denial of Service(DDoS)attacks is always one of the major problems for service providers.Using blockchain to detect DDoS attacks is one of the current popular methods.However,the problems of high time overhead and cost exist in the most of the blockchain methods for detecting DDoS attacks.This paper proposes a blockchain-based collaborative detection method for DDoS attacks.First,the trained DDoS attack detection model is encrypted by the Intel Software Guard Extensions(SGX),which provides high security for uploading the DDoS attack detection model to the blockchain.Secondly,the service provider uploads the encrypted model to Inter Planetary File System(IPFS)and then a corresponding Content-ID(CID)is generated by IPFS which greatly saves the cost of uploading encrypted models to the blockchain.In addition,due to the small amount of model data,the time cost of uploading the DDoS attack detection model is greatly reduced.Finally,through the blockchain and smart contracts,the CID is distributed to other service providers,who can use the CID to download the corresponding DDoS attack detection model from IPFS.Blockchain provides a decentralized,trusted and tamper-proof environment for service providers.Besides,smart contracts and IPFS greatly improve the distribution efficiency of the model,while the distribution of CID greatly improves the efficiency of the transmission on the blockchain.In this way,the purpose of collaborative detection can be achieved,and the time cost of transmission on blockchain and IPFS can be considerably saved.We designed a blockchain-based DDoS attack collaborative detection framework to improve the data transmission efficiency on the blockchain,and use IPFS to greatly reduce the cost of the distribution model.In the experiment,compared with most blockchain-based method for DDoS attack detection,the proposed model using blockchain distribution shows the advantages of low cost and latency.The remote authentication mechanism of Intel SGX provides high security and integrity,and ensures the availability of distributed models.

Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

Optimization of Stealthwatch Network Security System for the Detection and Mitigation of Distributed Denial of Service (DDoS) Attack: Application to Smart Grid System

The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.

A Resource-Based Pricing Collaborative Approach for Mitigating DDoS Attack in Mobile Edge Computing

Resource-constrainted and located closer to users,edge servers are more vulnerable to Distributed Denial of Service(DDoS)attacks.In order to mitigate the impact of DDoS attacks on benign users,this paper designed a Resource-based Pricing Collaborative approach(RPC)in mobile edge computing.By introducing the influence of resource prices on requester in economics,a collaboration model based on resource pricing was established,and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in network.The article theoretically proved the existence and rationality of the Nash equilibrium.Finally,simulation results verified the effectiveness and feasibility of the proposed approach in two experimental scenes.Experimental results shows that RPC can effectively improve the network ability to mitigate DDoS attacks,and alleviate the adverse effects of server attacks under delay constraints.

Anti-D Chain:A Lightweight DDoS Attack Detection Scheme Based on Heterogeneous Ensemble Learning in Blockchain

With rapid development of blockchain technology,blockchain and its security theory research and practical application have become crucial.At present,a new DDoS attack has arisen,and it is the DDoS attack in blockchain network.The attack is harmful for blockchain technology and many application scenarios.However,the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution.Aiming at the above problem,the paper proposes the virtual reality parallel anti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning.Here,Ada Boost and Random Forest are used as our ensemble learning strategy,and some different lightweight classifiers are integrated into the same ensemble learning algorithm,such as CART and ID3.Our detection framework in blockchain scene has much stronger generalization performance,universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network.Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators(such as Precision,Recall,F-Score,True Positive Rate,False Positive Rate,and ROC curve).

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire network.Nevertheless,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack straightforwardly.Furthermore,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been detected.However,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem.Moreover,recently the high frequencies of DDoS attacks have increased dramatically.In this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed.Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence.The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.In this paper,we propose a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network(CNN).According to the different characteristics of the attack flow and the normal flow in the IP protocol,the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary.Based on the network flow grayscale matrix feature(GMF),the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation,global features and local features of the network flow are extracted.A DDoS attack classifier based on multi-scale convolution neural network is constructed.Experiments show that compared with correlation methods,this method can improve the robustness of the classifier,reduce the false alarm rate and the missing alarm rate.

A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data

Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the problems,this paper proposes a DDoS attack information fusion method based on CNN for multi-element data.Firstly,according to the distribution,concentration and high traffic abruptness of DDoS attacks,this paper defines six features which are respectively obtained from the elements of source IP address,destination IP address,source port,destination port,packet size and the number of IP packets.Then,we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment.The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features,and obtain multi-element fusion feature(MEFF)value.Finally,the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series.Experimental results show that the information fusion method proposed can effectively fuse multi-element data,reduce the missing rate and total error rate,memory resource consumption,running time,and improve the detection rate.

DDoS Attack Detection Method for Space-Based Network Based on SDN Architecture

With the development of satellite communications,the number of satellite nodes is constantly increasing,which undoubtedly increases the difficulty of maintaining network security.Combining software defined network(SDN) with traditional space-based networks provides a new class of ideas for solving this problem.However,because of the highly centralized network management of the SDN controller,once the SDN controller is destroyed by network attacks,the network it manages will be paralyzed due to loss of control.One of the main security threats to SDN controllers is Distributed Denial of Service(DDoS) attacks,so how to detect DDoS attacks scientifically has become a hot topic among SDN security management.This paper proposes a DDoS attack detection method for space-based networks based on SDN architecture.This attack detection method combines the optimized Long Short-Term Memory(LSTM) deep learning model and Support Vector Machine(SVM),which can not only make classification judgments on the time series,but also achieve the purpose of detecting and judging through the flow characteristics of a period of time.In addition,it can reduce the detection time as well as the system burden.

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

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.

Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home

Smart home devices are vulnerable to a variety of attacks.The matter gets more complicated when a number of devices collaborate to launch a colluding attack(e.g.,Distributed-Denial-of-Service(DDoS))in a network(e.g.,Smart home).To handle these attacks,most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices,especially during Device-to-Device(D2D)interactions.Tapping into the potential of Ethereum blockchain and smart contracts,this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home.The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system.The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system.The simulation was conducted twenty times,each with varying number of devices chosen at random(ranging from 1 to 30).Each requester device sends an arbitrary request with a unique resource requirement at a time.This is done to measure the system's consistency across a variety of device capabilities.The experimental results show that the proposed protocol not only prevents colluding attacks,but also outperforms the benchmark protocols in terms of computational cost,message processing,and response times.

Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection

Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service(DDoS)attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security.Existing detection methods cannot effectively detect early attacks.In this paper,we propose a detection method of DDoS attacks based on generalized multiple kernel learning(GMKL)combining with the constructed parameter R.The super-fusion feature value(SFV)and comprehensive degree of feature(CDF)are defined to describe the characteristic of attack flow and normal flow.A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm.A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter.The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection,and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.

Blue Screen of Death Observed for Microsoft Windows Server 2012 R2 under DDoS Security Attack

Microsoft server Operating Systems are considered to have in-built, host based security features that should provide some protection against Distributed Denial of Service (DDoS) attacks. In this paper, we presented results of experiments that were conducted to test the security capability of the latest server Operating System from Microsoft Inc., namely Windows Server 2012 R2. Experiments were designed to evaluate its in-built security features in defending against a common Distributed Denial of Service (DDoS) attack, namely the TCP-SYN based DDoS attack. Surprisingly, it was found that the Windows Server 2012 R2 OS lacked sufficient host-based protection and was found to be unable to defend against even a medium intensity3.1 Gbps-magnitude of TCP-SYN attack traffic. The server was found to crash within minutes after displaying a Blue Screen of Death (BSoD) under such security attacks.

The History, Trend, Types, and Mitigation of Distributed Denial of Service Attacks

Over time, the world has transformed digitally and there is total dependence on the internet. Many more gadgets are continuously interconnected in the internet ecosystem. This fact has made the Internet a global information source for every being. Despite all this, attacker knowledge by cybercriminals has advanced and resulted in different attack methodologies on the internet and its data stores. This paper will discuss the origin and significance of Denial of Service (DoS) and Distributed Denial of Service (DDoS). These kinds of attacks remain the most effective methods used by the bad guys to cause substantial damage in terms of operational, reputational, and financial damage to organizations globally. These kinds of attacks have hindered network performance and availability. The victim’s network is flooded with massive illegal traffic hence, denying genuine traffic from passing through for authorized users. The paper will explore detection mechanisms, and mitigation techniques for this network threat.