More Like this

1. 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. 

   more » « less
2. 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. 

   more » « less
3. PERSIA: a PuzzlE-based InteReSt FloodIng Attack Countermeasure

   https://doi.org/10.1145/3405656.3418709  

   Tourani, Reza; Torres, George; Misra, Satyajayant (September 2020, Proceedings of the 7th ACM Conference on Information-Centric Networking)

   null (Ed.)

   With the proliferation of smart and connected mobile, wireless devices at the edge, Distributed Denial of Service (DDoS) attacks are increasing. Weak security, improper commissioning, and the fast, non-standardized growth of the IoT industry are the major contributors to the recent DDoS attacks, e.g., Mirai Botnet attack on Dyn and Memcached attack on GitHub. Similar to UDP/TCP flooding (common DDoS attack vector), request flooding attack is the primary DDoS vulnerability in the Named-Data Networking (NDN) architecture.In this paper, we propose PERSIA, a distributed request flooding prevention and mitigation framework for NDN-enabled ISPs, to ward-off attacks at the edge. PERSIA's edge-centric attack prevention mechanism eliminates the possibility of successful attacks from malicious end hosts. In the presence of compromised infrastructure (routers), PERSIA dynamically deploys an in-network mitigation strategy to minimize the attack's magnitude. Our experimentation demonstrates PERSIA's resiliency and effectiveness in preventing and mitigating DDoS attacks while maintaining legitimate users' quality of experience (> 99.92% successful packet delivery rate). 

   more » « less
4. Elastically Augmenting the Control-path Throughput in SDN to Deal with Internet DDoS Attacks

   https://doi.org/10.1145/3559759  

   Dai, Yuanjun; Wang, An; Guo, Yang; Chen, Songqing (February 2023, ACM Transactions on Internet Technology)

   Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, calledScotch, to enable the network to scale up its capability and handle the DDoS attack traffic. In our design, the congestion works as an indicator to trigger the mitigation mechanism.Scotchelastically scales up the control plane capacity by using an Open vSwitch-based overlay.Scotchtakes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under abnormal (e.g., DDoS attacks) traffic surges. We have implemented a prototype and experimentally evaluatedScotch. Our experiments in the small-scale lab environment and large-scale GENI testbed demonstrate thatScotchcan elastically scale up the control channel bandwidth upon attacks. 

   more » « less
5. An SVM Based DDoS Attack Detection Method for Ryu SDN Controller

   https://doi.org/10.1145/3360468.3368183  

   Mehr, Shideh Yavary; Ramamurthy, Byrav (December 2019, CoNEXT ’19 Companion)

   Software-Defined Networking (SDN) is a dynamic, and manageable network architecture which is more cost-effective than existing network architectures. The idea behind this architecture is to centralize intelligence from the network hardware and funnel this intelligence to the management system (controller) [2]-[4]. Since the centralized SDN controller controls the entire network and manages policies and the flow of the traffic throughout the network, it can be considered as the single point of failure [1]. It is important to find some ways to identify different types of attacks on the SDN controller [8]. Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks on SDN controller. In this work, we implement DDoS attack on the Ryu controller in a tree network topology using Mininet emulator. Also, we use a machine learning method, Vector Machines (SVM) to detect DDoS attack. We propose to install flows in switches, and we consider time attack pattern of the DDoS attack for detection. Simulation results show the effects of DDoS attacks on the Ryu controller is reduced by 36% using our detection method. 

   more » « less