DDoS Attacks Research Articles

Machine learning-based DDOS attack detection and mitigation in SDNs for IoT environments

Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.

Deep Learning Approach to Mitigate DDoS Attacks in SDN

Deep Learning Approach to Mitigate DDoS Attacks in SDN

A Federated Learning Architecture for Blockchain DDoS Attacks Detection

A Federated Learning Architecture for Blockchain DDoS Attacks Detection

A Structured Lightweight Encryption Architecture for Data Protection in IoT

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks.

Campus Network Traffic Prediction and Anomaly Detection Based on Deep Learning

This paper proposes an intelligent solution for network traffic prediction and anomaly detection in campus networks, addressing the increasingly severe network security challenges. The proposed approach innovatively integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to simultaneously extract local features and capture dynamic temporal dependencies of network traffic, significantly improving prediction accuracy. Based on this, an adaptive threshold anomaly detection algorithm is designed to automatically adjust detection sensitivity according to traffic variations, achieving a better balance between accuracy and recall rates. Additionally, an anomaly visualization scheme is presented, intuitively displaying the spatiotemporal distribution of network anomalies through heatmaps, assisting administrators in decision-making. Large-scale experiments demonstrate that this approach can effectively identify various security threats such as DDoS attacks, scanning probes, and botnets, with an overall detection rate exceeding 90% while maintaining a low false positive rate. Compared to traditional statistical and machine learning methods, the proposed approach exhibits stronger adaptability and generalization capabilities, providing crucial support for building an intelligent, precise, and reliable campus network security protection system. Future work will focus on further improving the real-time performance and robustness of the solution, expanding its application in new network scenarios such as IoT and edge computing.

Distributed denial of service (DDOS) attacks and mitigation method using logistic regression-based GoogLeNet for real time in security games

Distributed denial of service (DDOS) attacks and mitigation method using logistic regression-based GoogLeNet for real time in security games

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach

An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach

A proactive approach to DDoS attack recognition and preclusion in securing m-health systems

A proactive approach to DDoS attack recognition and preclusion in securing m-health systems

Blockchain-based IoT security solutions for IDS research centers

The rapid evolution of the Internet of Things (IoT) has connected real-world objects to the Internet, enhancing digital interaction and introducing critical security vulnerabilities. Intrusion Detection Systems (IDS) are essential in identifying threats and protecting Internet of Things devices from cyberattacks. A secure, decentralized platform enabled by blockchain technology offers the optimal solution for researchers worldwide to collaboratively address the challenges of IoT security effectively. This study introduces a blockchain-based IDS research center to strengthen IoT network security via global collaboration. It demonstrates the benefits of combining IDS with blockchain to improve cybersecurity in research centers, facilitating the efficient sharing of security solutions. By defining various node types on the blockchain, the platform ensures controlled access to services and streamlined network management through specific authorizations. Research centers can contribute to the blockchain with their findings, whereas others utilize the platform to access IDS services. Our work uses machine learning algorithms to achieve promising performance in detecting DDoS attacks, with the XGBoost algorithm demonstrating good results compared to the literature. The findings illustrate how effectively the presented approach works with blockchain technology and the CiCIoT2023 dataset to provide safe and decentralized information sharing.

Enhancing SDN resilience against DDoS attacks through dynamic virtual controller deployment and attack level detection algorithm

Enhancing SDN resilience against DDoS attacks through dynamic virtual controller deployment and attack level detection algorithm

Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Collaborative Federated Learning for 6G With a Deep Reinforcement Learning-Based Controlling Mechanism: A DDoS Attack Detection Scenario

Collaborative Federated Learning for 6G With a Deep Reinforcement Learning-Based Controlling Mechanism: A DDoS Attack Detection Scenario

Improving cyber-physical-power system stability through hardware-in-loop co-simulation platform for real-time cyber attack analysis

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyber-physical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed.

Creation and Training of artifical neural network for Detection and Neutralization of Network DDos–attacks(“Denial of Service”)

Creation and Training of artifical neural network for Detection and Neutralization of Network DDos–attacks(“Denial of Service”)

Enhancing Cybersecurity Against DDOS Attacks Evaluating Supervised Machine Learning Techniques

An individual or group launches a cyber attack when they intentionally try to get into another person's or group's computer system. Typically, the goal of an attacker is to gain an advantage by interfering with the victim's network. Now that COVID-19 has wreaked havoc on businesses throughout the world, it's cybercriminals' ideal storm. When it comes to cyber threats, Distributed Denial-Of-Service attacks (DDoS) are the most common and dangerous for corporate networks, apps, and services. Distributed denial of service attacks aim to flood a server, service, or network with malicious traffic in an effort to interrupt regular traffic. Financial losses, decreased productivity, damaged brands, worse credit and insurance ratings, damaged relationships with suppliers and customers, and IT budget overruns are all possible outcomes. Developing Network Intrusion Detection Systems (NIDSs) that can reliably foretell DDoS attacks is an urgent issue. This study used the CICDDoS2019 dataset to assess supervised Machine Learning (ML) methods. The machine learning algorithms that were assessed include AdaBoost, Naïve Bayes, MLP-ANN, Random Forest, and SVM. We use the assessment metrics: Area Under the Curve (AUC), Accuracy, F-measure, Precision, and Recall. This study demonstrates that of the algorithms tested, AdaBoost shows the highest promise in detecting DDoS attacks

Tinjauan Literatur Manajemen Risiko Cyber dalam Proyek: Identifikasi, Evaluasi, dan Mitigasi Ancaman

In today's digital era, business projects are increasingly vulnerable to cyber threats, which can result in significant financial and reputational losses. This journal discusses an effective cyber risk management approach for projects, encompassing the identification, evaluation, and mitigation of digital threats. The objective is to provide an overview of risk management in projects. This journal explains how to identify risks by thoroughly analyzing various potential cyber threats, such as malware, phishing, and DDoS attacks that could impact the project. Subsequently, risk evaluation is conducted by assessing the vulnerability and potential impact of each identified threat, using a literature review method. The literature review includes the analysis of data obtained from relevant articles and books on the topic. The final stage involves risk mitigation, which includes developing strategies to reduce or eliminate the impact of threats, such as implementing security controls, providing employee education and training, and developing incident response plans. This study emphasizes the importance of a proactive and holistic approach to cyber risk management to ensure the success and sustainability of projects. The journal aims to provide an overview of risk management in projects, demonstrating that the integration of comprehensive cyber risk management practices can significantly enhance the resilience of projects against digital threats.

A comprehensive detection and mitigation mechanism to protect SD-IoV systems against controller-targeted DDoS attacks

A comprehensive detection and mitigation mechanism to protect SD-IoV systems against controller-targeted DDoS attacks

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.

OTI-IoT: A Blockchain-based Operational Threat Intelligence Framework for Multi-vector DDoS Attacks

The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.