Ddos Attacks
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DDoS Attacks
Author: Dhruba Kumar Bhattacharyya
language: en
Publisher: CRC Press
Release Date: 2016-04-27
DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance discusses the evolution of distributed denial-of-service (DDoS) attacks, how to detect a DDoS attack when one is mounted, how to prevent such attacks from taking place, and how to react when a DDoS attack is in progress, with the goal of tolerating the attack. It introduces types and characteristics of DDoS attacks, reasons why such attacks are often successful, what aspects of the network infrastructure are usual targets, and methods used to launch attacks. The book elaborates upon the emerging botnet technology, current trends in the evolution and use of botnet technology, its role in facilitating the launching of DDoS attacks, and challenges in countering the role of botnets in the proliferation of DDoS attacks. It introduces statistical and machine learning methods applied in the detection and prevention of DDoS attacks in order to provide a clear understanding of the state of the art. It presents DDoS reaction and tolerance mechanisms with a view to studying their effectiveness in protecting network resources without compromising the quality of services. To practically understand how attackers plan and mount DDoS attacks, the authors discuss the development of a testbed that can be used to perform experiments such as attack launching, monitoring of network traffic, and detection of attacks, as well as for testing strategies for prevention, reaction, and mitigation. Finally, the authors address current issues and challenges that need to be overcome to provide even better defense against DDoS attacks.
Distributed Denial of Service (DDoS) Attacks
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.
Analysing Cloud DDoS Attacks Using Supervised Machine Learning
Author: Chisom Elizabeth Alozie
language: en
Publisher: Deep Science Publishing
Release Date: 2025-02-02
Cloud computing in its simplest form refers to the provision of hardware and software to deliver a service over an internet network. However, Cloud Computing has numerous issues, such as security attacks and distributed denial of service (DDoS). A DDoS attack is defined as a method of attack in which numerous computer systems are allowed to attack a target, such as a server, any resource, or website, resulting in a denial of service for the resource's intended users. This research analysed the normal traffic and DDoS attack traffic from cloud environments using machine learning technology to detect DDoS attacks. This work’s main contribution is the extraction of dataset features and the discovery of new flow features for DDoS attack detection. To create the dataset, novel features are stored in a CSV file using the CICFlowMeter tool. Features were selected using a correlation coefficient to get better model accuracy. Machine learning algorithms were trained on the resulting cloud dataset. The existing work reviews for detection of DDoS attacks either used a cloud dataset or another network data set, or the research findings were kept confidential. The methodology used to solve this problem is the CRISP-DM methodology. The proposed solution deployed a brand-new dataset with five machine-learning models for classification. The findings of this study help to improve knowledge of the ability of DDoS datasets to detect intrusions. Five performance metrics—accuracy, precision, recall, F1-score, and computation time were used to analyse the datasets. Based on the results achieved with the new dataset, the Random Forest, Support Vector Machine, Decision Tree, and K-NN achieved a 100% rate of 100% on the accuracy, precision, recall, and F1 score in a shorter computation time. With the open-source dataset, Random Forest, Decision Tree, and K-Nearest Neighbor achieved 100% accuracy.