Journal ArticleDOI
Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges
Neha Agrawal,Shashikala Tapaswi +1 more
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This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms with a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment.Abstract:
The salient features of cloud computing (such as on-demand self-service, resource pooling, broad network access, rapid elasticity, and measured service) are being exploited by attackers to launch the severe Distributed Denial of Service (DDoS) attack. Generally, the DDoS attacks in such an environment have been implemented by flooding a huge volume (high-rate) of malicious traffic to exhaust the victim servers’ resources. Due to this huge volume of malicious traffic, such attacks can be easily detected. Thus, attackers are getting attracted towards the low-rate DDoS attacks, slowly. Low-rate DDoS attacks are difficult to detect due to their stealthy and low-rate traffic. In the recent years, many efforts have been devoted to defend against the low-rate DDoS attacks. By utilizing the salient features of cloud computing, it becomes easy for an attacker to launch sophisticated low-rate DDoS attacks. Thus, the study of various DDoS attacks and their corresponding defense approaches becomes essential to protect the cloud infrastructure from fatal effects of DDoS attacks. This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms. The paper provides a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment. The purpose of this survey paper is to excite the cloud security researchers to develop effective defense solutions against the various DDoS attacks. The research gaps and challenges are found, and described in the paper while future research directions are outlined.read more
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Journal ArticleDOI
Blockchain envisioned UAV networks: Challenges, solutions, and comparisons
TL;DR: A Blockchain (BC)-based security solution and a summary of research challenges in the integration of BC with 5G-enabled UAV networks are presented and a case study of implementing BC with UAVs to secure industrial applications is presented.
Journal ArticleDOI
A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning
TL;DR: A flexible modular architecture that allows the identification and mitigation of LR-DDoS attacks in software-defined network (SDN) settings and achieves a detection rate of 95%, despite the difficulty in detecting LR-DoS attacks.
Journal ArticleDOI
Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method
TL;DR: A data-driven IDS is designed by analyzing the link load behaviors of the Road Side Unit in the IoV against various attacks leading to the irregular fluctuations of traffic flows and a deep learning architecture based on the Convolutional Neural Network is designed to extract the features of link loads, and detect the intrusion aiming at RSUs.
Journal ArticleDOI
Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
TL;DR: A distributed denial of service (DDoS) attack represents a major threat to service providers as discussed by the authors, where a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests.
Journal ArticleDOI
Soft computing based autonomous low rate ddosattack detection and security for cloud computing
TL;DR: The paper uses the soft computing based autonomous detection for the Low rate-DDOS attacks in the cloud architecture and utilizes the hidden Markov Model for observing the flow in the network and the Random forest in classifying the detected attacks from the normal flow.
References
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Journal Article
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Journal ArticleDOI
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TL;DR: A survey of the different security risks that pose a threat to the cloud is presented and a new model targeting at improving features of an existing model must not risk or threaten other important features of the current model.
Journal ArticleDOI
A taxonomy of DDoS attack and DDoS defense mechanisms
Jelena Mirkovic,Peter Reiher +1 more
TL;DR: This paper presents two taxonomies for classifying attacks and defenses in distributed denial-of-service (DDoS) and provides researchers with a better understanding of the problem and the current solution space.