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"Denial of service attacks"
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Distributed denial of service (DDoS) attacks : classification, attacks, challenges, and countermeasures
The complexity and severity of the Distributed Denial of Service (DDoS) attacks are increasing day-by-day. The Internet has a highly inconsistent structure in terms of resource distribution. Numerous technical solutions are available, but those involving economic aspects have not been given much consideration. The book, DDoS Attacks - Classification, Attacks, Challenges, and Countermeasures, provides an overview of both types of defensive solutions proposed so far, exploring different dimensions that would mitigate the DDoS effectively and show the implications associated with them. Features: Covers topics that describe taxonomies of the DDoS attacks in detail, recent trends and classification of defensive mechanisms on the basis of deployment location, the types of defensive action, and the solutions offering economic incentives. Introduces chapters discussing the various types of DDoS attack associated with different layers of security, an attacker's motivations, and the importance of incentives and liabilities in any defensive solution. Illustrates the role of fair resource-allocation schemes, separate payment mechanisms for attackers and legitimate users, negotiation models on cost and types of resources, and risk assessments and transfer mechanisms. DDoS Attacks - Classification, Attacks, Challenges, and Countermeasures is designed for the readers who have an interest in the cybersecurity domain, including students and researchers who are exploring different dimensions associated with the DDoS attack, developers and security professionals who are focusing on developing defensive schemes and applications for detecting or mitigating the DDoS attacks, and faculty members across different universities.
Book
Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm
Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.
Journal Article
Deep learning approaches for detecting DDoS attacks: a systematic review
In today’s world, technology has become an inevitable part of human life. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutes has shifted from offline to online. It leads to exponential increase in intrusions and attacks over the Internet-based technologies. One of the lethal threat surfacing is the Distributed Denial of Service (DDoS) attack that can cripple down Internet-based services and applications in no time. The attackers are updating their skill strategies continuously and hence elude the existing detection mechanisms. Since the volume of data generated and stored has increased manifolds, the traditional detection mechanisms are not appropriate for detecting novel DDoS attacks. This paper systematically reviews the prominent literature specifically in deep learning to detect DDoS. The authors have explored four extensively used digital libraries (IEEE, ACM, ScienceDirect, Springer) and one scholarly search engine (Google scholar) for searching the recent literature. We have analyzed the relevant studies and the results of the SLR are categorized into five main research areas: (i) the different types of DDoS attack detection deep learning approaches, (ii) the methodologies, strengths, and weaknesses of existing deep learning approaches for DDoS attacks detection (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature, and (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature (v) the research gaps, and future directions.
Journal Article
The coming swarm : DDoS actions, hacktivism, and civil disobedience on the Internet
\"This book examines the history, development, theory, and practice of distributed denial of service actions as a tactic of political activism. The internet is a vital arena of communication, self expression, and interpersonal organizing. When there is a message to convey, words to get out, people to organize, many will turn to the internet as a theater for that activity. As familiar and widely accepted activist tools--petitions, fundraisers, mass letter-writing, call-in campaigns and others--find equivalent practices in the online space, is there also room for the tactics of disruption and civil disobedience that are equally familiar from the realm of street marches, occupations, and sit-ins? Grounding the analysis historically, focusing on early deployments of activist DDOS as well as modern instances to trace its development over time, this book uses activist DDOS actions as the foundation of a larger analysis of the practice of disruptive civil disobedience on the internet\"-- Provided by publisher.
Book
Data‐Driven Dual‐Channel Dynamic Event‐Triggered Load Frequency Control for Multiarea Power Systems Under DoS Attacks
System dynamics uncertainties and cyberattacks pose significant challenges to load frequency control in power systems. This paper presents a data‐driven load frequency control strategy for interconnected multi‐area power systems subject to denial‐of‐service attacks that disrupt both feedforward and feedback communication channels. A dynamic linearization method is employed to construct an equivalent data model of the power system. To enhance control performance, the proposed controller integrates proportional, differential, and quadratic difference terms. Additionally, a dynamic dual event‐triggered mechanism is designed to improve resource efficiency and reduce computational overhead. The proposed approach also compensates for DoS attacks affecting both feedback and feedforward channels. Simulation results demonstrate that the method operates without requiring prior system model information, relying solely on control input and output data. Extensive simulations validate the effectiveness and robustness of the proposed control strategy.
Journal Article
Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks
The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.
Journal Article
Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in Various Web-Enabled Computing Platforms: Issues, Challenges, and Future Research Directions
The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.
Journal Article
Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways
The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.
Journal Article
Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey
Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.
Journal Article