In-Network Volumetric DDoS Victim Identification Using Programmable Commodity Switches

a serious nuisance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of the technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of a period. Analogous to a DDoS attack is the Flash Crowd (FC), in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. Many researchers have put effort to differentiate between these two kinds of traffic, but none could come up with an effective solution. In this study, we propose an adaptive agent-based model, known as an adaptive application layer flooding protection (AALFP) model, for DDoS and FC flooding attacks. The AALFP model is aimed to protect the network application layer (NAL) against such attacks. Importantly, the model distinguishes and separates normal from abnormal traffic, and then blocks illegitimate traffic. Note that, to operate two traffic filters, the agent relies on the following three parameters of normal traffic intensity, traffic attack behaviour, and IP address history log. An example scenario is provided to simulate the model's application.

Distributed Denial of Service (DDoS) attack is a serious threat to commercial service network. DDoS attack has been studied for years. However, detecting and relieving DDoS attacks are still a problem. Especially, nowadays more and more DDoS attacks produce heavy network traffic, it is hard to response rapidly because that needs high processing performance to process massive traffic data. With big data technology, volumes of network traffic data can be processed much faster. Apache Spark can process a great amount of data in a reasonable time so that DDoS attack can be detected in time. Besides, it is difficult to modify the network configuration in traditional network. With Software-Defined Networking (SDN), a new paradigm in networking, networking can be controlled by programs, which makes modifying the network configuration easier. In this paper, a DDoS detection and mitigation system framework in SDN is introduced, a framework that can control network based on analyzing the network traffic data. Comparing to the traditional defense methods of DDoS attack, the framework can response to DDoS attack by rules automatically.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

The detection of distributed denial of service (DDoS) attacks is one of the hardest problems confronted by the network security researchers. Flash event (FE), which is caused by a large number of legitimate requests, has similar characteristics to those of DDoS attacks. Moreover DDoS attacks and FEs require altogether different handling procedures. So discriminating DDoS attacks from FEs is very important. But the research involving DDoS detection has not laid enough emphasis on including FEs scenarios in the experiments. In this paper, we are using traffic cluster entropy as detection metric not only to detect DDoS attacks but also to distinguish DDoS attacks from FEs. We have validated our approach on cyber-defense technology experimental research laboratory (DETER) testbed. Different emulation scenarios are created on DETER using mix of legitimate, flash, and different types of attacks at varying strengths. It is found that, when flash event is triggered, source address entropy increases but the corresponding traffic cluster entropy does not increase. However, when DDoS attack is launched, traffic cluster entropy also increases along with source address entropy. An analysis of live traces on DETER testbed clearly manifests supremacy of our approach.

Modern methods of analyzing and protecting network infrastructure against DDoS (Distributed Denial of Service) attacks are discussed. A DDoS detection model has been developed using statistical techniques, which highlights the main stages of the attacks and key characteristics of network traffic that are crucial for detecting an attack. Potential and attack power are introduced as main concepts in assessing DDoS activity. To identify the type of attack, it is suggested to increase the sensitivity of the model by identifying key characteristics that distinguish between different attack stages. The features of various DDoS attack types, such as UDP Flood, UDP Reflection/Amplification, and TCP SYN Flood, are considered. A framework for modeling DDoS network attacks has been created. DDoS attacks including UDP Flood, UDP Reflection/Amplification and TCP SYN Flood were simulated using traffic data collected via the NetFlow protocol. The proposed attack characteristics, including speed, flow volume, and flow rate, allowed us to evaluate the attack's power and consider how to change the key characteristics of network traffic.

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 - 95% and a bandwidth overhead of less than 1%.

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

The software defined network (SDN) collects network traffic data and proactively manages networks. SDN’s programmability makes it excellent for developing distributed applications, cybersecurity, and decentralized network control in multitenant data centers. This exceptional architecture is vulnerable to security concerns, such as distributed denial of service (DDoS) attacks. DDoS attacks can be very serious due to the fact that they prevent authentic users from accessing, temporarily or indefinitely, resources they would normally expect to have. Moreover, there are continuous efforts from attackers to produce new techniques to avoid detection. Furthermore, many existing DDoS detection methods now in use have a high potential for producing false positives. This motivates us to provide an overview of the research studies that have already been conducted in this area and point out the strengths and weaknesses of each of those approaches. Hence, adopting an optimal detection method is necessary to overcome these issues. Thus, it is crucial to accurately detect abnormal flows to maintain the availability and security of the network. In this work, we propose hybrid deep learning algorithms, which are the long short-term memory network (LSTM) and convolutional neural network (CNN) with a stack autoencoder for DDoS attack detection and checkpoint network, which is a fault tolerance strategy for long-running processes. The proposed approach is trained and tested with the aid of two DDoS attack datasets in the SDN environment: the DDoS attack SDN dataset and Botnet dataset. The results show that the proposed model achieves a very high accuracy, reaching 99.99% in training, 99.92% in validation, and 100% in precision, recall, and F1 score with the DDoS attack SDN dataset. Also, it achieves 100% in all metrics with the Botnet dataset. Experimental results reveal that our proposed model has a high feature extraction ability and high performance in detecting attacks. All performance metrics indicate that the proposed approach is appropriate for a real-world flow detection environment.

Software Defined Network (SDN) is a new network construction. But due to its construction, SDN is vulnerable to be attacked by Distributed Denial of Service (DDoS) attack. So it is important to detect DDoS attack in SDN network. This paper presents a DDoS detection scheme based on k-means algorithm in SDN environment. The establishment of this scheme is based on the two hypotheses that the daily network works normally most of the time, and there is a significant difference between the data characteristics of normal situation and abnormal situation. At the same time, these two hypotheses are also true to the daily network condition. After demonstrating the validity of k-means clustering algorithm, the paper proposes 5 flow table features that can be used to detect DDoS attacks. Finally, the DDoS detection scheme was tested by simulation experiment. The test results showed that the method proposed by the author could effectively detect DDoS, with an average success rate of 97.78%.

Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.

Notice of Violation of IEEE Publication Principles<br><br>"An Effective Defense Against Distributed Denial of Service in Grid"<br>by Mohan H.S. and A. Raji Reddy,<br>in the Proceedings of the First International Conference on Integrated Intelligent Computing, August 2010, pp. 84-89<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"An Effective Defense Against Distributed Denial of Service in Grid"<br>by N. Venkatesu, V. Deepan Chakravarthy, D. Sathya<br> in the Proceedings of the First International Conference on Emerging Trends in Engineering and Technology, July 2008, pp. 373-378<br><br> <br/> IP spoofing has been exploited by Distributed Denial of Service (DDoS) attacks to conceal flooding sources and localities in flooding traffic, and prevent legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victims is essential to their own protection as well as to their avoidance of becoming congestion and involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he or she cannot falsify the number of hops an IP packet takes to reach its destination. This hop count information can be inferred from the Time- to-Live (TTL) value in the IP header. Using a mapping between IP addresses and their hop-counts to an Internet server, the server can distinguish spoofed IP packets from legitimate ones. Based on this observation, we present a novel filtering technique that is immediately deployable to weed out spoofed IP packets. We have an IP to Hop Count Mapping Table (IP2HC) to store the Hop Count Values. We implement HCF in the Linux kernel, demonstrating its benefits using experimental measurements. We deploy the security mechanism in Globus Toolkit (GT4) to ensure that HCF can identify the spoofed packet in a Grid Environment.

Distributed Denial of Service (DDoS) attacks continue to be one of the most severe threats in the Internet. The intrinsic challenge in preventing DDoS attacks is to distinguish them from legitimate flash crowds since two have many traffic characteristics in common. Today most DDoS detection techniques focus on finding parametric differences between the patterns in attack and legitimate traffic. However, such techniques are very sensitive to the threshold values set on the parameters and more importantly legitimate traffic features might be mimicked by smart attackers to generate requests that look like flash crowds. In this paper, we propose a framework for training networks for such smart attacks. Our framework is based on Deep Generative Network models and our contributions are two-fold.We first show that legitimate traffic features can be mimicked without explicitly modeling their distributions. Second, we introduce the concept of context-aware DDoS attacks. We show that an attacker can generate traffic that looks similar to flash crowds to be undetected for long periods of time. However, the ability of generating such attacks is constrained by the budget of the attacker. A context-aware attacker is the one that can intelligently use its budget to maximize the damage in the victim network. Our study provides a framework for training networks for such DDoS attack scenarios.

Software-Defined Networking (SDN) offers centralized control and programmability, transforming network management but also introducing vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the control plane and disrupt network functionality. Traditional DDoS detection methods, including rule-based systems and conventional machine learning models, often fall short in SDN due to high false-positive rates and limited adaptability to evolving network traffic. While recent deep learning approaches show promise, they continue to face challenges with real-time adaptability and scalability in SDN environments. In this study, we propose Attention-Enhanced Cross-Entropy (AECE), a novel Deep Neural Network (DNN)-based DDoS detection model that integrates attention mechanisms to prioritize critical features in network traffic data, allowing the model to focus on patterns indicative of DDoS attacks. A core innovation in AECE is the F-Balanced Cross-Entropy (FBCE) Loss function, which combines cross-entropy with an F1-score-based component to balance precision and recall, effectively reducing both false positives and false negatives. Additionally, AECE incorporates ReLU and GELU activations, batch normalization, dropout, and the Adamax optimizer to enhance learning stability and computational efficiency. Experimental results demonstrate that the proposed system achieves high detection accuracy, significantly outperforming existing DDoS detection methods and providing a robust, low-latency solution to safeguard SDN infrastructures against evolving DDoS threats.

Distributed Denial of Service (DDoS) attacks have emerged as one of the most pervasive and damaging threats to network security, disrupting services and incurring substantial financial costs. Machine learning (ML) has been widely explored as a potential solution to enhance DDoS detection and mitigation. This systematic review evaluates the effectiveness of ML techniques in detecting DDoS attacks, synthesizing findings from studies published between 2004 and 2024. The review analyzes models such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (K-NN) based on key performance metrics like accuracy, precision, recall, and F1-score. A comprehensive search of multiple databases, including Web of Science, IEEE, Scopus, ScienceDirect, and Google Scholar, resulted in 19 studies that met inclusion criteria. The findings show that ensemble methods, particularly Random Forest, consistently outperformed other models in terms of detection rates, mainly due to their ability to handle large feature sets and reduce overfitting. Support Vector Machines also performed well in specific scenarios. However, their effectiveness was sometimes limited by computational complexity and the dataset size. K-Nearest Neighbors showed mixed results, depending on the nature of the attack patterns. This review emphasizes the potential of ensemble learning approaches for DDoS detection, demonstrating their robustness in dynamic environments. The review identifies key gaps in the existing research, including the need for better feature selection and exploring deep learning techniques to enhance DDoS detection accuracy and adaptability further. This study contributes valuable insights into the strengths and limitations of ML-based DDoS detection models, offering a foundation for future advancements in the field. It underscores the importance of continued research into hybrid and deep learning models to address the evolving and increasingly sophisticated nature of DDoS attacks in real-world applications.

Volumetric Distributed Denial of Service (DDoS) attacks have been a severe threat to the Internet for more than two decades. Some success in mitigation has been achieved based on numerous defensive techniques created by the research community, implemented by the industry, and deployed by network operators. However, evolution is not a privilege of mitigations, and DDoS attackers have found better strategies and continue to cause harm. A key challenge in winning this race is understanding the various characteristics of DDoS attacks in network traffic at scale and in a realistic manner. In this paper, we propose DDoS2Vec, a novel approach to characterise DDoS attacks in real-world Internet traffic using Natural Language Processing (NLP) techniques. DDoS2Vec is a domain-specific application of Latent Semantic Analysis that learns vector representations of potential DDoS attacks. We look into the link between natural language and computer network communication in a way that has not been previously studied. Our approach is evaluated on a large-scale dataset of flow samples collected from an Internet eXchange Point (IXP) in one year. We evaluate the performance of DDoS2Vec via multi-label classification in a Machine Learning (ML) scenario. DDoS2Vec characterises DDoS attacks more clearly than other baselines - including NLP-based approaches inspired by recent networks research and a basic non-NLP solution.