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1. Denial of service attacks in edge computing layers: Taxonomy, vulnerabilities, threats and solutions

   https://doi.org/10.1016/j.adhoc.2023.103322  

   Uddin, Ryhan; Kumar; Sathish; Chamola, Vinay (October 2023, Ad hoc networks)

   Edge computing has emerged as the dominant communication technology connecting IoT and cloud, offering reduced latency and harnessing the potential of edge devices. However, its widespread adoption has also introduced various security vulnerabilities, similar to any nascent technology. One notable threat is the denial of service (DoS) attack, including its distributed form, the distributed denial of service (DDoS) attack, which is the primary focus of this research. This paper aims to explore the impact of different types of DoS and DDoS attacks on edge computing layers by examining the vulnerabilities associated with various edge peripherals. Addition ally, existing detection and prevention mechanisms are investigated to address these weaknesses. Furthermore, a theoretical architecture is proposed to mitigate distributed denial of service attacks targeting edge systems. By comprehensively analyzing and addressing the security concerns related to DoS and DDoS attacks in edge computing, this research aims to contribute to the development of robust and secure edge computing systems. 

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2. An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics

   https://doi.org/10.1145/3148055.3148077  

   Villalobos, J. J.; Rodero, Ivan; Parashar, Manish (January 2017, BDCAT '17 Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies)

   A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack. 

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3. Interest Flooding Attacks in Named Data Networking: Survey of Existing Solutions, Open Issues, Requirements and Future Directions

   https://doi.org/10.1145/3539730  

   Benmoussa, Ahmed; Kerrache, Chaker Abdelaziz; Lagraa, Nasreddine; Mastorakis, Spyridon; Lakas, Abderrahmane; Tahari, Abdou el (January 2022, ACM Computing Surveys)

   Named Data Networking (NDN) is a prominent realization of the vision of Information-Centric Networking. The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. Many IFA detection and mitigation solutions have been proposed in the literature. However, there is no comprehensive review study of these solutions that has been proposed so far. Therefore, in this paper, we propose a survey of the various IFAs with a detailed comparative study of all the relevant proposed solutions as counter-measures against IFAs. We also review the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms through a series of attack scenarios. Finally, in this survey, we offer an analysis of the open issues and future research directions regarding IFAs. 

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4. Distributed Denial of Service Attack Detection

   Blue, Travis; Shahriar, Hossain (June 2020, National Cyber Summit)

   Distributed Denial of Service (DDoS) attacks has been a persistent threat for network and applications. Successful attacks can lead to inaccessible service to legitimate users in time and loss of business reputation. In this paper, we explore DDoS attack detection using Term Frequency (TF)-Inverse Document Frequency (IDF) and Latent Semantic Indexing (LSI). We analyzed web server log data generated in a distributed environment. 

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5. Real-time Detection and Localization of Distributed DoS Attacks in NoC based SoCs

   https://doi.org/10.1109/TCAD.2020.2972524  

   Charles, Subodha; Lyu, Yangdi; Mishra, Prabhat (January 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   Network-on-Chip (NoC) is widely employed by multi-core System-on-Chip (SoC) architectures to cater to their communication requirements. Increasing NoC complexity coupled with its widespread usage has made it a focal point of potential security attacks. Distributed Denial-of-Service (DDoS) is one such attack that is caused by malicious intellectual property (IP) cores flooding the network with unnecessary packets causing significant performance degradation through NoC congestion. In this paper, we propose an efficient framework for real-time detection and localization of DDoS attacks. This paper makes three important contributions. We propose a real-time and lightweight DDoS attack detection technique for NoC-based SoCs by monitoring packets to detect any violations. Once a potential attack has been flagged, our approach is also capable of localizing the malicious IPs using the latency data in the NoC routers. The applications are statically profiled during design time to determine communication patterns. These patterns are then used for real-time detection and localization of DDoS attacks. We have evaluated the effectiveness of our approach against different NoC topologies and architecture models using both real benchmarks and synthetic traffic patterns. Our experimental results demonstrate that our proposed approach is capable of real-time detection and localization of DDoS attacks originating from multiple malicious IPs in NoC-based SoCs. 

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