Relevant bibliographies by topics / Link Flooding Attacks

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Link Flooding Attacks'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Link Flooding Attacks"

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Hsieh, Chih-Hsiang, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai, and Yi-Bing Lin. "Efficient Detection of Link-Flooding Attacks with Deep Learning." Sustainability 13, no. 22 (November 12, 2021): 12514. http://dx.doi.org/10.3390/su132212514.

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The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and implement our ideas with a deep learning approach to improve the performance of the previous work. Through rerouting techniques and monitoring network traffic, our system can detect a malicious attack from the adversary. A CNN architecture is combined to assist in finding an appropriate rerouting path that can shorten the reaction time for detecting DDoS attacks. Therefore, the proposed method can efficiently distinguish the difference between benign traffic and malicious traffic and prevent attackers from carrying out link-flooding attacks through bots.
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Wang, Xin, Xiaobo Ma, Jiahao Peng, Jianfeng Li, Lei Xue, Wenjun Hu, and Li Feng. "On Modeling Link Flooding Attacks and Defenses." IEEE Access 9 (2021): 159198–217. http://dx.doi.org/10.1109/access.2021.3131503.

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Xue, Lei, Xiaobo Ma, Xiapu Luo, Edmond W. W. Chan, Tony T. N. Miu, and Guofei Gu. "LinkScope: Toward Detecting Target Link Flooding Attacks." IEEE Transactions on Information Forensics and Security 13, no. 10 (October 2018): 2423–38. http://dx.doi.org/10.1109/tifs.2018.2815555.

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Chou, Li-Der, Chien-Chang Liu, Meng-Sheng Lai, Kai-Cheng Chiu, Hsuan-Hao Tu, Sen Su, Chun-Lin Lai, Chia-Kuan Yen, and Wei-Hsiang Tsai. "Behavior Anomaly Detection in SDN Control Plane: A Case Study of Topology Discovery Attacks." Wireless Communications and Mobile Computing 2020 (November 20, 2020): 1–16. http://dx.doi.org/10.1155/2020/8898949.

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Software-defined networking controllers use the OpenFlow discovery protocol (OFDP) to collect network topology status. The OFDP detects the link between switches by generating link layer discovery protocol (LLDP) packets. However, OFDP is not a security protocol. Attackers can use it to perform topology discovery via injection, man-in-the-middle, and flooding attacks to confuse the network topology. This study proposes a correlation-based topology anomaly detection mechanism. Spearman’s rank correlation is used to analyze the network traffic between links and measure the round-trip time of each LLDP frame to determine whether a topology discovery via man-in-the-middle attack exists. This study also adds a dynamic authentication key and counting mechanism in the LLDP frame to prevent attackers from using topology discovery via injection attack to generate fake links and topology discovery via flooding attack to cause network routing or switching abnormalities.
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Wang, Lei, Qing Li, Yong Jiang, Xuya Jia, and Jianping Wu. "Woodpecker: Detecting and mitigating link-flooding attacks via SDN." Computer Networks 147 (December 2018): 1–13. http://dx.doi.org/10.1016/j.comnet.2018.09.021.

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Wang, Juan, Ru Wen, Jiangqi Li, Fei Yan, Bo Zhao, and Fajiang Yu. "Detecting and Mitigating Target Link-Flooding Attacks Using SDN." IEEE Transactions on Dependable and Secure Computing 16, no. 6 (November 1, 2019): 944–56. http://dx.doi.org/10.1109/tdsc.2018.2822275.

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Lu, Ning, Junwei Zhang, Ximeng Liu, Wenbo Shi, and Jianfeng Ma. "STOP: A Service Oriented Internet Purification Against Link Flooding Attacks." IEEE Transactions on Information Forensics and Security 17 (2022): 938–53. http://dx.doi.org/10.1109/tifs.2022.3152406.

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Gkounis, Dimitrios, Vasileios Kotronis, Christos Liaskos, and Xenofontas Dimitropoulos. "On the Interplay of Link-Flooding Attacks and Traffic Engineering." ACM SIGCOMM Computer Communication Review 46, no. 2 (April 9, 2016): 5–11. http://dx.doi.org/10.1145/2935634.2935636.

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Aydeger, Abdullah, Mohammad Hossein Manshaei, Mohammad Ashiqur Rahman, and Kemal Akkaya. "Strategic Defense Against Stealthy Link Flooding Attacks: A Signaling Game Approach." IEEE Transactions on Network Science and Engineering 8, no. 1 (January 1, 2021): 751–64. http://dx.doi.org/10.1109/tnse.2021.3052090.

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Rasool, Raihan ur, Hua Wang, Usman Ashraf, Khandakar Ahmed, Zahid Anwar, and Wajid Rafique. "A survey of link flooding attacks in software defined network ecosystems." Journal of Network and Computer Applications 172 (December 2020): 102803. http://dx.doi.org/10.1016/j.jnca.2020.102803.

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Dissertations / Theses on the topic "Link Flooding Attacks"

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Kang, Min Suk. "Handling Large-Scale Link-Flooding Attacks in the Internet." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/833.

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Link-flooding attacks in which an adversary coordinates botnet messages to exhaust the bandwidth of selected network links in the core of the Internet (e.g., Tier-1 or Tier-2 networks) have been a powerful means of denial of service. In the past few years, these attacks have moved from the realm of academic curiosities to real-world incidents. Unfortunately, we have had a limited understanding of this type of attacks and effective countermeasures in the current Internet. In this dissertation, we address this gap in our understanding of link-flooding attacks and propose a two-tier defense approach. We begin by identifying routing bottlenecks as the major cause of the Internet vulnerability to link-flooding attacks. A routing bottleneck is a small set of links whose congestion disrupts the majority of routes taken towards a given set of destination hosts. These bottlenecks appear despite physicalpath diversity and sufficient bandwidth provisioning in normal (i.e., nonattack) mode of operation, and are an undesirable artifact of the current Internet design. We illustrate their pervasiveness for adversary-chosen sets of hosts in various cities and countries around the world via experimental measurements. We then present a real-time adaptive attack for persistent flooding of chosen links in the discovered routing bottlenecks using attack flows that are indistinguishable from legitimate traffic. We demonstrate the feasibility of these strategies and show that disruptions can scale from targeted hosts of a single organization to those of a country. To counter the link-flooding attacks defined in this dissertation, one could remove their root cause, namely the routing bottlenecks. However, this would affect the cost-minimizing policy that underlies the current Internet, change its routing architecture, and possibly affect communication costs. Instead, we propose an attack-deterrence mechanism that represents a first line of defense against link-flooding attacks by cost-sensitive adversaries. In the proposed defense, most link-flooding attacks are handled by the low-cost, single-domain based mechanism. As a second line of defense, which targets cost-insensitive adversaries that are undeterred, we propose the use of a multi-domain coordinated defense mechanism that is harder to orchestrate in the current Internet.
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Lee, Soo Bum. "Localizing the effects of link flooding attacks in the internet." College Park, Md. : University of Maryland, 2009. http://hdl.handle.net/1903/10052.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Rasool, Raihan Ur. "CyberPulse: A Security Framework for Software-Defined Networks." Thesis, 2020. https://vuir.vu.edu.au/42172/.

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Software-Defined Networking (SDN) technology provides a new perspective in traditional network management by separating infrastructure plane from the control plane which facilitates a higher level of programmability and management. While centralized control provides lucrative benefits, the control channel becomes a bottleneck and home to numerous attacks. We conduct a detailed study and find that crossfire Link Flooding Attacks (LFA) are one of the most lethal attacks for SDN due to the utilization of low-rate traffic and persistent attacking nature. LFAs can be launched by the malicious adversaries to congest the control plane with low-rate traffic which can obstruct the flow rule installation and can ultimately bring down the whole network. Similarly, the adversary can employ bots to generate low-rate traffic to congest the control channel, and ultimately bring down the control plane and data plane connection causing service disruption. We present a systematic and comparative study on the vulnerabilities of LFAs on all the SDN planes, elaborate in detail the LFA types, techniques, and their behavior in all the variant of SDN. We then illustrate the importance of a defense mechanism employing a distributed strategy against LFAs and propose a Machine Learning (ML) based framework namely CyberPulse. Its detailed design, components, and their interaction, working principles, implementation, and in-depth evaluation are presented subsequently. This research presents a novel approach to write anomaly patterns and makes a significant contribution by developing a pattern-matching engine as the first line of defense against known attacks at a line-speed. The second important contribution is the effective detection and mitigation of LFAs in SDN through deep learning techniques. We perform twofold experiments to classify and mitigate LFAs. In the initial experimental setup, we utilize Artificial Neural Networks backward propagation technique to effectively classify the incoming traffic. In the second set of experiments, we employ a holistic approach in which CyberPulse demonstrates algorithm agnostic behavior and employs a pre-trained ML repository for precise classification. As an important scientific contribution, CyberPulse framework has been developed ground up using modern software engineering principles and hence provides very limited bandwidth and computational overhead. It has several useful features such as large-scale network-level monitoring, real-time network status information, and support for a wide variety of ML algorithms. An extensive evaluation is performed using Floodlight open-source controller which shows that CyberPulse offers limited bandwidth and computational overhead and proactively detect and defend against LFA in real-time. This thesis contributes to the state-of-the-art by presenting a novel framework for the defense, detection, and mitigation of LFA in SDN by utilizing ML-based classification techniques. Existing solutions in the area mandate complex hardware for detection and defense, but our presented solution offers a unique advantage in the sense that it operates on real-time traffic scenario as well as it utilizes multiple ML classification algorithms for LFA traffic classification without necessitating complex and expensive hardware. In the future, we plan to implement it on a large testbed and extend it by training on multiple datasets for multiple types of attacks.
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Book chapters on the topic "Link Flooding Attacks"

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Cui, Yong, Lingjian Song, and Ke Xu. "RCS: A Distributed Mechanism Against Link Flooding DDoS Attacks." In Information Networking. Advances in Data Communications and Wireless Networks, 764–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919568_76.

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Kang, Min Suk, Virgil D. Gligor, and Vyas Sekar. "Defending Against Evolving DDoS Attacks: A Case Study Using Link Flooding Incidents." In Security Protocols XXIV, 47–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62033-6_7.

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Rezazad, Mostafa, Matthias R. Brust, Mohammad Akbari, Pascal Bouvry, and Ngai-Man Cheung. "Detecting Target-Area Link-Flooding DDoS Attacks Using Traffic Analysis and Supervised Learning." In Advances in Intelligent Systems and Computing, 180–202. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03405-4_12.

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Gligor, Virgil D. "Defending Against Evolving DDoS Attacks: A Case Study Using Link Flooding Incidents (Transcript of Discussion)." In Security Protocols XXIV, 58–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62033-6_8.

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Peng, Jiahao, Xiaobo Ma, Jianfeng Li, Lei Xue, and Wenjun Hu. "Shoot at a Pigeon and Kill a Crow: On Strike Precision of Link Flooding Attacks." In Network and System Security, 436–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02744-5_32.

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Yang, Guosong, and João P. Hespanha. "Modeling and Mitigating Link-Flooding Distributed Denial-of-Service Attacks via Learning in Stackelberg Games." In Handbook of Reinforcement Learning and Control, 433–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60990-0_15.

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Nalayini, C. M., and Jeevaa Katiravan. "Block Link Flooding Algorithm for TCP SYN Flooding Attack." In International Conference on Computer Networks and Communication Technologies, 895–905. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8681-6_83.

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Xie, Lixia, Ying Ding, and Hongyu Yang. "Mitigating Link-Flooding Attack with Segment Rerouting in SDN." In Cyberspace Safety and Security, 57–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37337-5_6.

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Ponnusamy, Vasaki, Naveena Devi Regunathan, Pardeep Kumar, Robithoh Annur, and Khalid Rafique. "A Review of Attacks and Countermeasures in Internet of Things and Cyber Physical Systems." In Industrial Internet of Things and Cyber-Physical Systems, 1–24. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2803-7.ch001.

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The internet usage for commercial and public services has significantly increased over these decades to where security of information is becoming a more important issue to society. At the same time, the number of network attacks in IoT is increasing. These include distributed denial of service (DDOS) attack, phishing, trojan, and others that will cause the network information to not be secure. With the revolution in Industry 4.0 and IoT being the main asset in the Fourth Industrial Revolution, many companies spend thousands or millions to protect their networks and servers. Unfortunately, the success rate to prevent network attack is still not welcoming. The attacks on physical layers, such as jamming, node tampering; link layer, such as collision, unfairness, battery exhaustion; network layer, such as spoofing, hello flood, Sybil attack, wormhole, DOS, DDOS; transport layer, such as flooding, de-synchronization; application layer, such as flooding, are alarming. This chapter reviews attacks and countermeasures.
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Ganesan, Sangeetha, Vijayalakshmi Muthuswamy, Ganapathy Sannasi, and Kannan Arputharaj. "A Comprehensive Analysis of Congestion Control Models in Wireless Sensor Networks." In Sensor Technology, 1194–214. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch057.

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Congestion control is an important factor for performance improvement in wireless sensor networks (WSNs). Congestion occurs due to various reasons including a variation in the data rate between incoming and outgoing links, buffer size, flooding attacks and multiple inputs and minimum output capability. Various outcomes of congestion in sensor networks include immense packet loss or packet drop, fast energy depletion, unfairness across the network, reduced node performance and increased delay in packet delivery. Hence, there is an extreme need to check channel congestion in order to enhance the performance with better congestion management. The job of choosing a suitable congestion control technique is a challenging task for the network designer. In this article, the authors traverse through the underlying conceptual ideas on congestion control schemes which come under six unique models. This article highlights a survey on the existing works done so far on congestion control domains in sensor networks. A comparative analysis based on Quality of Service parameters has been discussed.
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Conference papers on the topic "Link Flooding Attacks"

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Kim, Jinwoo, and Seungwon Shin. "Software-Defined HoneyNet: Towards Mitigating Link Flooding Attacks." In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2017. http://dx.doi.org/10.1109/dsn-w.2017.10.

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Belabed, Dallal, Mathieu Bouet, and Vania Conan. "Centralized Defense Using Smart Routing Against Link-Flooding Attacks." In 2018 2nd Cyber Security in Networking Conference (CSNet). IEEE, 2018. http://dx.doi.org/10.1109/csnet.2018.8602966.

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Wang, Zhaoxu, Huachun Zhou, Bohao Feng, Wei Quan, and Shui Yu. "MTF: Mitigating Link Flooding Attacks in Delay Tolerant Networks." In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2018. http://dx.doi.org/10.1109/smartworld.2018.00264.

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Ding, Xuyang, Feng Xiao, Man Zhou, and Zhibo Wang. "Active Link Obfuscation to Thwart Link-flooding Attacks for Internet of Things." In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2020. http://dx.doi.org/10.1109/trustcom50675.2020.00040.

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Kang, Min Suk, Virgil D. Gligor, and Vyas Sekar. "SPIFFY: Inducing Cost-Detectability Tradeoffs for Persistent Link-Flooding Attacks." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2016. http://dx.doi.org/10.14722/ndss.2016.23147.

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Lee, Soo Bum, and Virgil D. Gligor. "FLoc : Dependable Link Access for Legitimate Traffic in Flooding Attacks." In 2010 IEEE 30th International Conference on Distributed Computing Systems. IEEE, 2010. http://dx.doi.org/10.1109/icdcs.2010.78.

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Liaskos, Christos, Vasileios Kotronis, and Xenofontas Dimitropoulos. "A novel framework for modeling and mitigating distributed link flooding attacks." In IEEE INFOCOM 2016 - IEEE Conference on Computer Communications. IEEE, 2016. http://dx.doi.org/10.1109/infocom.2016.7524507.

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Chen, Xu, Wei Feng, Ning Ge, and Xianbin Wang. "Defending Link Flooding Attacks under Incomplete Information: A Bayesian Game Approach." In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE, 2020. http://dx.doi.org/10.1109/icc40277.2020.9148653.

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Aydeger, Abdullah, Nico Saputro, and Kemal Akkaya. "Utilizing NFV for Effective Moving Target Defense Against Link Flooding Reconnaissance Attacks." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599803.

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Xing, Junchi, Jingjing Cai, Boyang Zhou, and Chunming Wu. "A Deep ConvNet-Based Countermeasure to Mitigate Link Flooding Attacks Using Software-Defined Networks." In 2019 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2019. http://dx.doi.org/10.1109/iscc47284.2019.8969595.

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Reports on the topic "Link Flooding Attacks"

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Lee, Soo B., and Virgil D. Gligor. FLoc : Dependable Link Access for Legitimate Traffic in Flooding Attacks. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada580050.

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Lee, Soo B., and Virgil D. Gligor. FLoc: Dependable Link Access for Legitimate Traffic in Flooding Attacks. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada582042.

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Ramanujan, Ranga S., Doug Harper, Maher Kaddoura, David Baca, John Wu, and Kevin Millikin. Organic Techniques for Protecting Virtual Private Network (VPN) Services from Access Link Flooding Attacks. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada436292.

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