Mitigate DDoS Attacks Research Articles

Mitigating DDOS Attacks from IP-Rotating Bots Using Token-Based Access Control

Mitigating DDOS Attacks from IP-Rotating Bots Using Token-Based Access Control

SD-MDN-TM: A traceback and mitigation integrated mechanism against DDoS attacks with IP spoofing

Traceback has been very attractive against DDoS attacks with IP spoofing instead of traditional mitigation methods because attacks require removal near attackers, resulting in affecting legitimate traffic as little as possible. There have been some approaches dedicated to achieving effective traceback. However, existing approaches often modify protocols to apply to multi-domain scenarios, and the implemented mitigation usually lags behind traceback. Therefore, this paper proposes the Software-Defined Multi-Domain Network Tracer and Mitigator (SD-MDN-TM) to traceback and mitigate DDoS attacks with IP spoofing in multi-domain SDN scenarios. We apply systematic sampling and flow feature extraction based on the Count-Min Sketch data structure for the lightweight statistics collection of massive DDoS attack traffic. We also design the TracebackTree data structure to construct the traceback paths of attackers of distributed attack sources. The Border Switch Trigger Mechanism is proposed to overcome the drawbacks of the commonly-used packet marking in cross-domain traceback information transfer, achieving no modification of existing network protocols and independent traceback among multiple domains. Mitigation is integrated with traceback for faster removal of attacks from the network. The proposed scheme can traceback DDoS attack sources both inside and outside domains accurately and effectively by constructing traceback path. It can be implemented without modifying the existing protocols, therefore achieving direct application to the existing network architecture. Furthermore, the traceback of attack sources outside domains can maintain independence in multi-domain scenarios. Mitigation integrated with traceback can achieve less impact on legitimate traffic and faster removal of attacks from the network.

Deep Learning Approach to Mitigate DDoS Attacks in SDN

Deep Learning Approach to Mitigate DDoS Attacks in SDN

Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN

Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation method for DDoS attacks in SDN based on multi-dimensional characteristics. Firstly, an analysis of the traffic statistics from the SDN switch ports is performed, which aids in conducting a coarse-grained detection of DDoS attacks within the network. Subsequently, a Multi-Dimensional Deep Convolutional Classifier (MDDCC) is constructed using wavelet decomposition and convolutional neural networks to extract multi-dimensional characteristics from the traffic data passing through suspicious switches. Based on these extracted multi-dimensional characteristics, a simple classifier can be employed to accurately detect attack samples. Finally, by integrating graph theory with restrictive strategies, the source of attacks in SDN networks can be effectively traced and isolated. The experimental results indicate that the proposed method, which utilizes a minimal amount of statistical information, can quickly and accurately detect attacks within the SDN network. It demonstrates superior accuracy and generalization capabilities compared to traditional detection methods, especially when tested on both simulated and public datasets. Furthermore, by isolating the affected nodes, the method effectively mitigates the impact of the attacks, ensuring the normal transmission of legitimate traffic during network attacks. This approach not only enhances the detection capabilities but also provides a robust mechanism for containing the spread of cyber threats, thereby safeguarding the integrity and performance of the network.

Mitigating DDoS Attacks on IoT Networks: Strategies and Solutions

Mitigating DDoS Attacks on IoT Networks: Strategies and Solutions

Surveying Emerging Trends in DDoS Defense

This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.

Predictive optimization of DDoS attack mitigation in distributed systems using machine learning

In recent years, cloud computing has been widely used. This paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

A Multi-Layered Defence Strategy against DDoS Attacks in SDN/NFV-Based 5G Mobile Networks

Software-defined networking (SDN) and network functions virtualisation (NFV) are crucial technologies for integration in the fifth generation of cellular networks (5G). However, they also pose new security challenges, and a timely research subject is working on intrusion detection systems (IDSs) for 5G networks. Current IDSs suffer from several limitations, resulting in a waste of resources and some security threats. This work proposes a new three-layered solution that includes forwarding and data transport, management and control, and virtualisation layers, emphasising distributed controllers in the management and control layer. The proposed solution uses entropy detection to classify arriving packets as normal or suspicious and then forwards the suspicious packets to a centralised controller for further processing using a self-organising map (SOM). A dynamic OpenFlow switch relocation method is introduced based on deep reinforcement learning to address the unbalanced burden among controllers and the static allocation of OpenFlow switches. The proposed system is analysed using the Markov decision process, and a Double Deep Q-Network (DDQN) is used to train the system. The experimental results demonstrate the effectiveness of the proposed approach in mitigating DDoS attacks, efficiently balancing controller workloads, and reducing the duration of the balancing process in 5G networks.

SDN-based detection and mitigation of DDoS attacks on smart homes

The adoption of the Internet of Things (IoT) has proliferated across various domains, where everyday objects like refrigerators and washing machines are now equipped with sensors and connected to the internet. Undeniably, the security of such devices, which were not primarily designed for internet connectivity, is of utmost importance but has been largely neglected. In this paper, we propose a framework for the real-time DDoS attack detection and mitigation in SDN-enabled smart home networks. We capture network traffic during regular operations and during DDoS attacks. This captured traffic is used to train several machine learning (ML) models, including Support Vector Machine (SVM), Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) algorithms. These trained models are executed as SDN controller applications and subsequently employed for real-time attack detection. While we utilize ML techniques to protect IoT devices, we propose the use of SNORT, a signature-based detection technique, to secure the SDN controller itself. Real-world experiments demonstrate that without SNORT, the SDN controller goes offline shortly after an attack, resulting in a 100% packet loss. Furthermore, we show that ML algorithms can efficiently classify traffic into benign and attack traffic, with the Decision Tree algorithm outperforming others with an accuracy of 99%.

Voting Model Strategies for Reliable Categorical IoT-DDoS Attack Prediction

This research focuses on developing reliable categorical IoT-DDoS attack prediction models using ensemble voting strategies. The study explores various machine learning algorithms suitable for categorical data analysis, employing feature engineering techniques to preprocess IoT data. Ensemble learning methodologies, including bagging, boosting, and stacking, are then utilized to build robust prediction models. Evaluation metrics such as precision, recall, F1-score, and AUC-ROC are used to assess model performance, demonstrating the effectiveness of ensemble voting models in reliably predicting IoT-DDoS attacks. Comparative analyses with individual classifiers highlight the advantages of ensemble approaches in terms of predictive accuracy and robustness against data imbalances and noise. This work contributes to advancing IoT security by providing a practical framework for deploying predictive models that aid in early detection and mitigation of DDoS attacks, enhancing overall resilience against cyber threats in IoT ecosystems.

Detection and mitigation of link flooding-based DDoS attacks on a software defined network using network function virtualisation

Detection and mitigation of link flooding-based DDoS attacks on a software defined network using network function virtualisation

Detection and Mitigation of DDOS Attack in SDN Environment Using Hybrid CNN-LSTM

DDoS attacks, powered by botnets to flood network resources, pose a significant threat to traditional network setups. Software-Defined Networking (SDN) boosts network adaptability and programmability by separating the control and data planes. However, the centralized control in SDN can be a vulnerability, allowing attackers to exploit security flaws and launch DDoS attacks. These attacks overwhelm network controllers and switches, consuming bandwidth and server resources, and disrupting regular user access. In response to the threat, we've implemented an online SDN defence system designed to detect and counter such attacks. This system includes modules for both spotting anomalies and handling them. The anomaly detection model combines Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) techniques to effectively spot irregular traffic patterns. For mitigation, the model identifies abnormal traffic by implementing flow rule orders from the controller and traces back to the attacker via IP tracing. To measure our approach's effectiveness, we used various evaluation metrics like Accuracy, F-measure, Precision, Recall, ROC Curve, and Precision-Recall Curve. Our methodology displayed impressive results, achieving a 99.83% accuracy in multiclass classification and 99.17% accuracy in binary classification. In comparison with existing DDoS detection systems, our AI-driven mitigation techniques demonstrated their superiority. Overall, our research aims to streamline the detection and mitigation of DDoS attacks.

Understanding and Mitigating Cloud Computing Security Attacks: A Case of DDoS

Although cloud computing is considered the most widespread technology. Despite of what it offers, the end users still suffers many challenges, especially related to its security issues such as vulnerability to malicious attacks such as Distributed Denial of Service attack (DDoS), that prevents accessing the internet. This paper is design to bring understanding and ways of mitigating DDoS attacks in cloud computing environment by leveraging from the pool of diverse solutions proffered that detect and prevent such attacks from harming network communication. Keywords: Cloudflare, Firewall, Gateway, Webserver and Zombies

Cost-Efficient Method for Detecting and Mitigating DDOS Attacks in SDN Based Networks

Cost-Efficient Method for Detecting and Mitigating DDOS Attacks in SDN Based Networks

Enhancing Mitigation of Volumetric DDoS Attacks: A Hybrid FPGA/Software Filtering Datapath.

The increasing network speeds of today's Internet require high-performance, high-throughput network devices. However, the lack of affordable, flexible, and readily available devices poses a challenge for packet classification and filtering. This problem is exacerbated by the increase in volumetric Distributed Denial-of-Service (DDoS) attacks, which require efficient packet processing and filtering. To meet the demands of high-speed networks and configurable network processing devices, this paper investigates a hybrid hardware/software packet filter prototype that combines reconfigurable FPGA technology and high-speed software filtering on commodity hardware. It uses a novel approach that offloads filtering rules to the hardware and employs a Longest Prefix Matching (LPM) algorithm and allowlists/blocklists based on millions of IP prefixes. The hybrid filter demonstrates improvements over software-only filtering, achieving performance gains of nearly 30%, depending on the rulesets, offloading methods, and traffic types. The significance of this research lies in developing a cost-effective alternative to more-expensive or less-effective filters, providing high-speed DDoS packet filtering for IPv4 traffic, as it still dominates over IPv6. Deploying these filters on commodity hardware at the edge of the network can mitigate the impact of DDoS attacks on protected networks, enhancing the security of all devices on the network, including Internet of Things (IoT) devices.

SDN/NFV-based framework for autonomous defense against slow-rate DDoS attacks by using reinforcement learning

The unforeseen and skyrocketed shift in the number of connections to the Internet during the last years has created vast and critical vulnerabilities in networks that cybercriminals have quickly seized to launch high-volume DDoS attacks. Existing tools, such as advanced firewalls or intrusion prevention systems (IPS), cannot handle such an elevated volume of attacks because these solutions are dependent on humans. Therefore, adaptation of the current network security solutions to automated ones is more significant than ever to foster the development of the zero-touch networks and service management (ZSM) paradigm. Building on our preliminary work in this field, in this study, we provide a software-defined networking (SDN)-based framework that automates the detection and mitigation of slow-rate DDoS attacks. The framework uses deep learning (DL) to detect attacks and reinforcement learning (RL) to mitigate them. Furthermore, a network function virtualization (NFV)-assisted moving target defense (MTD) mechanism is included to amplify the effectiveness and flexibility of the solution. The framework is tested on a simulated network using open-source tools, namely Open Network Operating System (ONOS), Containernet, Apache Web Server, and Docker. The source code of a prototype of the framework is shared, which can be used and improved by interested researchers. Finally, the experimental results demonstrate that RL agents learn optimal DDoS mitigation policies in different scenarios and that they quickly adapt to new conditions that vary in short periods of time.

Machine Learning Based Classification Model for Network Traffic Anomaly Detection

In current days, cloud environments are facing a huge challenge from the attackers in terms of various attacks thrown to the cloud service providers. In both industry and academics, the problem of detection and mitigation of DDoS attacks is now a challenging issue. Detecting Distributed Denial of Service (DDos) threats is mainly a classification problem that can be addressed using data mining, machine learning and deep learning techniques. DDoS attacks can occur in any of the seven-layer OSI model's network. Hence, detecting the DDoS attacks is an important task for cloud service providers to overcome dangerous attacks and loss incurred to stake holders and also the provider.

Mitigation of DDoS Attack Using Moving Target Defense in SDN

Mitigation of DDoS Attack Using Moving Target Defense in SDN

A crowd cooperative defense model for mitigating DDoS attacks in Mobile Crowdsensing networks

Mobile Crowdsensing (MCS) is rapidly growing up while encountering severe security threats simultaneously. DDoS attacks tend to disrupt large-scale sensing tasks to the MCS and reduce the availability of MCS. However, the existing centralized defense technology has the problem of high cost and the existing research lacks the application of distributed cooperative characteristics of MCS. A crowd cooperative defense model for mitigating DDoS attacks in MCS is proposed based on these. Firstly, the crowdsensing networks are formally described. The cooperative mechanism of sensing nodes participating in overlapping sensing coalition is generalized, which breaks the independence between the distributed sensing nodes and enables the sensing nodes to cooperate against DDoS attacks. Then, the crowd cooperative defense problem is transformed into a multi-objective combinatorial optimization problem. A multi-objective discrete Harris Hawk optimization (MODHHO) algorithm is designed. The algorithm applies non-dominated sorting and crowding strategy to evaluate the advantages and disadvantages of each cooperative defense solution on the cooperative defense objectives. In addition, the global exploration, local exploitation, and besiege phases are used to continuously update the cooperative defense solutions, and finally obtains the global optimal cooperative defense solution after a certain number of iterations. The simulation results show that the proposed model can minimize the defense cost compared to non-cooperative and non-overlapping sensing coalition methods. Compared with centralized defense, the loss cost of the proposed model is reduced by 61.8%. In addition, the average throughput of the sensing network is increased by 35.5%, which enhances the availability of the sensing network.

Mitigation and Detection of DDOS attacks using Software Defined Network (SDN) and Machine Learning

Abstract: An emerging networking paradigm called Software Defined Networking (SDN) enables network control to be restricted to a logically centralized controller. This makes network visibility and management simpler on a global scale. SDN is a suitable network model for extensive deployment in real-world settings since it can be programmed using high-level programming languages. SDN is still vulnerable to a number of network attacks, the most notable of which being the DDoS (distributed denial of service) attack. A DDoS attack has the potential to render the controller, the brain of SDN, inoperable. SDN security is still in its infancy, and both business and academics have done a lot of research in this area. The SDN DDoS mitigation strategy utilizing machine learning (ML) is the main topic of this study. This work also surveys network traffic aspects for DDoS detection