Distributed Denial of Service Attacks in Cloud Computing

Abusaimeh, Hesham

doi:10.14569/ijacsa.2020.0110621

Cited by 13 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reducing DDoS attacks is essential to keeping cloud services stable, organized, and available [30]. These attacks send traffic to the system and block actual users.…”

Section: Mitigation Strategies For Ddos Disruptionsmentioning

confidence: 99%

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Ahmadi

2024

JIS

View full text Add to dashboard Cite

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

show abstract

“…Reducing DDoS attacks is essential to keeping cloud services stable, organized, and available [30]. These attacks send traffic to the system and block actual users.…”

Section: Mitigation Strategies For Ddos Disruptionsmentioning

confidence: 99%

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Ahmadi

2024

JIS

View full text Add to dashboard Cite

show abstract

“…Several different methods for detecting distributed denial of service attacks have been developed [9] as a result of the growing frequency and intensity of these attacks. The use of time series analysis is one of the most promising methods for recognising distributed denial of service attacks [10]. Discovering dynamic patterns and trends in network traffic is something that may be accomplished with the help of this technology.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

Qasim,

NSAIF

2024

BJN

View full text Add to dashboard Cite

Distributed denial-of-service assaults, often known as DDoS attacks, pose a significant danger to the stability and security of the internet, particularly in light of the increasing number of devices that are linked to the internet. Intelligent detection systems are absolutely necessary in order to lessen the impact of distributed denial of service assaults. In this study, a comprehensive overview of recent research on intelligent approaches, such as Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI), is presented. The review focuses on the application of these techniques in the detection of Distributed Denial of Service (DDoS) assaults. In addition to providing a taxonomy and conceptual framework for DDoS mitigation, the study places particular emphasis on the application of time series data analysis for the detection of distributed denial of service attacks. A number of different intelligent techniques are investigated in this paper. Some of these techniques include clustering, deep reinforcement learning, graph neural networks, support vector machines, and others. For the purpose of performance evaluation, real datasets are utilized, and prospective future research areas in this area are explored.

show abstract

“…Due to these services' vulnerability and high value, attacks on infrastructures that provide these services are favored by hackers. One of the most common attacks to block these services is the DDoS attack [ 1 ]. How to deal with DDoS attacks to ensure network smoothness has become a research hotspot [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion

Guo¹,

Qiu²,

Liu³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and deep learning methods to improve classification performance. However, most classifiers are trained with statistical flow features as input, ignoring topological connection changes. This one-sidedness affects the detection accuracy and cannot provide a basis for the distribution of attack sources for defense deployment. In this study, we propose a topological and flow feature-based deep learning method (GLD-Net), which simultaneously extracts flow and topological features from time-series flow data and exploits graph attention network (GAT) to mine correlations between non-Euclidean features to fuse flow and topological features. The long short-term memory (LSTM) network connected behind GAT obtains the node neighborhood relationship, and the fully connected layer is utilized to achieve feature dimension reduction and traffic type mapping. Experiments on the NSL-KDD2009 and CIC-IDS2017 datasets show that the detection accuracy of the GLD-Net method for two classifications (normal and DDoS flow) and three classifications (normal, fast DDoS flow, and slow DDoS flow) reaches 0.993 and 0.942, respectively. Compared with the existing DDoS attack detection methods, its average improvement is 0.11 and 0.081, respectively. In addition, the correlation coefficient between the detection accuracy of attack flow and the four source distribution indicators ranges from 0.7 to 0.83, which lays a foundation for the inference of attack source distribution. Notably, we are the first to fuse topology and flow features and achieve high-performance DDoS attack intrusion detection through graph-style neural networks. This study has important implications for related research and development of network security systems in other fields.

show abstract

Distributed Denial of Service Attacks in Cloud Computing

Cited by 13 publications

References 9 publications

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion

Contact Info

Product

Resources

About