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Journal articles on the topic 'Link Flooding Attacks'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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6

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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7

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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8

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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9

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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10

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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11

Ma, Xiaobo, Jianfeng Li, Yajuan Tang, Bo An, and Xiaohong Guan. "Protecting internet infrastructure against link flooding attacks: A techno-economic perspective." Information Sciences 479 (April 2019): 486–502. http://dx.doi.org/10.1016/j.ins.2018.04.050.

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12

Chen, Yen-Hung, Yuan-Cheng Lai, Pi-Tzong Jan, and Ting-Yi Tsai. "A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network." Sensors 21, no. 4 (February 3, 2021): 1027. http://dx.doi.org/10.3390/s21041027.

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(1) Background: Link flooding attacks (LFA) are a spatiotemporal attack pattern of distributed denial-of-service (DDoS) that arranges bots to send low-speed traffic to backbone links and paralyze servers in the target area. (2) Problem: The traditional methods to defend against LFA are heuristic and cannot reflect the changing characteristics of LFA over time; the AI-based methods only detect the presence of LFA without considering the spatiotemporal series attack pattern and defense suggestion. (3) Methods: This study designs a deep ensemble learning model (Stacking-based integrated Convolutional neural network–Long short term memory model, SCL) to defend against LFA: (a) combining continuous network status as an input to represent “continuous/combination attacking action” and to help CNN operation to extract features of spatiotemporal attack pattern; (b) applying LSTM to periodically review the current evolved LFA patterns and drop the obsolete ones to ensure decision accuracy and confidence; (c) stacking System Detector and LFA Mitigator module instead of only one module to couple with LFA detection and mediation at the same time. (4) Results: The simulation results show that the accuracy rate of SCL successfully blocking LFA is 92.95%, which is 60.81% higher than the traditional method. (5) Outcomes: This study demonstrates the potential and suggested development trait of deep ensemble learning on network security.

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13

Wang, Jiushuang, Ying Liu, Weiting Zhang, Xincheng Yan, Na Zhou, and Zhihong Jiang. "ReLFA: Resist link flooding attacks via renyi entropy and deep reinforcement learning in SDN-IoT." China Communications 19, no. 7 (July 2022): 157–71. http://dx.doi.org/10.23919/jcc.2022.07.013.

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14

Rasool, Raihan ur, Khandakar Ahmed, Zahid Anwar, Hua Wang, Usman Ashraf, and Wajid Rafique. "CyberPulse++: A machine learning‐based security framework for detecting link flooding attacks in software defined networks." International Journal of Intelligent Systems 36, no. 8 (May 2, 2021): 3852–79. http://dx.doi.org/10.1002/int.22442.

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15

Cervera, Gimer, Michel Barbeau, Joaquin Garcia-Alfaro, and Evangelos Kranakis. "A multipath routing strategy to prevent flooding disruption attacks in link state routing protocols for MANETs." Journal of Network and Computer Applications 36, no. 2 (March 2013): 744–55. http://dx.doi.org/10.1016/j.jnca.2012.12.013.

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16

Jaiswal, Ms Rashmi, and Ms Chandramala Amarji. "A Distributed Intrusion Detection System for AODV Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 1576–86. http://dx.doi.org/10.22214/ijraset.2022.46247.

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Abstract: The Ad hoc On-Demand Distance Vector (AODV) routing protocol, designed for mobile ad hoc networks, offers quick adaptation to dynamic link conditions, low processing and memory overhead, and low network utilization. However, without keeping in mind the security issues in the protocol design, AODV is vulnerable to various kinds of attacks. This thesis analyzes some of the vulnerabilities, specifically discussing attacks against AODV that manipulate the routing messages. We propose a solution based on specification-based intrusion detection to detect attacks on AODV. Briefly, our approach involves the use of finite state machines for specifying correct AODV routing behavior and distributed network monitors for detecting run-time violation of the specifications. In addition, one additional field in the protocol message is proposed to enable the monitoring. We illustrate that our algorithm, which employs a tree data structure, can effectively detect most of the serious attacks in real time and with minimum overhead. Routing attacks will have distressing effects over the network and bequest a significant challenge once planning strong security mechanisms for vehicular communication. In this paper, we examine the effect and malicious activities of a number of the foremost common attacks and also mention some security schemes against some major attacks in VANET. The attacker's aim is only to modify the actual route or provides the false data about the route to the sender and also some attackers are only flooding unwanted packets to consume resources in available network. Various routing approaches are also mentioned in the paper because the routing of data is very important to deliver the traffic information to leading vehicles. It's advised that a number of the ways that to approach this made field of analysis issues in VANET might be to fastidiously design new secure routing protocols in which attacks are often rendered meaningless and because of the inherent constraints found in the network, there's a desire for light-weight and sturdy security mechanisms.

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17

Defibaugh, Stephen, and Donna Schaeffer. "Can Attrition Theory Provide Insight for Cyber Warfare?" International Conference on Cyber Warfare and Security 17, no. 1 (March 2, 2022): 55–62. http://dx.doi.org/10.34190/iccws.17.1.9.

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This paper explores the notion that cyber-adversaries can use classic attrition tactics to cause weakness to address follow-on attacks. We conducted a grounded theory study that reviewed historic literature to identify parallels between past attrition tactics and cyber warfare. From historical examples, we see the possibility of an adversary conducting an asymmetric campaign by flooding the adversary with false-positive attacks in order to have them drain resources. For a modern perspective, we interviewed subject-matter experts from a US military command. Thematic analysis demonstrates a link between attrition and cyber-maneuver warfare. One significant finding is that most subject-matter experts agreed a culture of compliance, which encourages a full resources response to security events given full resources, can reduce the ability to maneuver appropriately and takes away from the focus on critical mission functions that cyber security is actually in place to protect. Other common themes that surfaced include that some interviewees believed their organizations were not prepared for cyber war nor are they resourced adequately to respond to a state of cyber war. Issues that need further study are the need to compare and correlate telemetry and metrics of incident responses and better tracking of the dollar-cost value of incident response and cyber tactics.

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18

Thanigaivelan, Nanda Kumar, Ethiopia Nigussie, Seppo Virtanen, and Jouni Isoaho. "Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation." Security and Communication Networks 2018 (August 15, 2018): 1–15. http://dx.doi.org/10.1155/2018/3672698.

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We present a hybrid internal anomaly detection system that shares detection tasks between router and nodes. It allows nodes to react instinctively against the anomaly node by enforcing temporary communication ban on it. Each node monitors its own neighbors and if abnormal behavior is detected, the node blocks the packets of the anomaly node at link layer and reports the incident to its parent node. A novel RPL control message, Distress Propagation Object (DPO), is formulated and used for reporting the anomaly and network activities to the parent node and subsequently to the router. The system has configurable profile settings and is able to learn and differentiate between the nodes normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases that are distributed in both the nodes and router, which act on data link and network layers. The system uses network fingerprinting to be aware of changes in network topology and approximate threat locations without any assistance from a positioning subsystem. The developed system was evaluated using test-bed consisting of Zolertia nodes and in-house developed PandaBoard based gateway as well as emulation environment of Cooja. The evaluation revealed that the system has low energy consumption overhead and fast response. The system occupies 3.3 KB of ROM and 0.86 KB of RAM for its operations. Security analysis confirms nodes reaction against abnormal nodes and successful detection of packet flooding, selective forwarding, and clone attacks. The system’s false positive rate evaluation demonstrates that the proposed system exhibited 5% to 10% lower false positive rate compared to simple detection system.

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19

Ma, Xiaobo, Bo An, Mengchen Zhao, Xiapu Luo, Lei Xue, Zhenhua Li, Tony T. N. Miu, and Xiaohong Guan. "Randomized Security Patrolling for Link Flooding Attack Detection." IEEE Transactions on Dependable and Secure Computing 17, no. 4 (July 1, 2020): 795–812. http://dx.doi.org/10.1109/tdsc.2019.2892370.

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20

Ravi, Nagarathna, S. Mercy Shalinie, and D. Danyson Jose Theres. "BALANCE: Link Flooding Attack Detection and Mitigation via Hybrid-SDN." IEEE Transactions on Network and Service Management 17, no. 3 (September 2020): 1715–29. http://dx.doi.org/10.1109/tnsm.2020.2997734.

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21

Rasool, Raihan Ur, Usman Ashraf, Khandakar Ahmed, Hua Wang, Wajid Rafique, and Zahid Anwar. "Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks." IEEE Access 7 (2019): 34885–99. http://dx.doi.org/10.1109/access.2019.2904236.

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22

Asahina, Hiromu, Kei Sakuma, Shuichiro Haruta, Hiroya Kato, and Iwao Sasase. "Traceroute-based target link flooding attack detection scheme by analyzing hop count to the destination." IEICE Communications Express 8, no. 7 (2019): 251–56. http://dx.doi.org/10.1587/comex.2019xbl0054.

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23

Deliwala, Priyanshi, Rutvij H. Jhaveri, and Sagar Ramani. "Machine learning in SDN networks for secure industrial cyber physical systems: a case of detecting link flooding attack." International Journal of Engineering Systems Modelling and Simulation 13, no. 1 (2022): 76. http://dx.doi.org/10.1504/ijesms.2022.122730.

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24

Ramani, Sagar, Rutvij H. Jhaveri, and Priyanshi Deliwala. "Machine learning in SDN networks for secure industrial cyber physical systems: a case of detecting link flooding attack." International Journal of Engineering Systems Modelling and Simulation 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijesms.2021.10040840.

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25

Urtāns, Juris. "KOKNESE FRONT FORTIFICATIONS. TOO BIG TO BE SEEN." Culture Crossroads 19 (October 11, 2022): 200–217. http://dx.doi.org/10.55877/cc.vol19.42.

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The article is focused on the history of Koknese Fortress Front fortifications which were built from September 1700 to May 1701 in order to enhance the defence power of Koknese Fortress. The total length of the defence line exceeded 4 km. The line contained 25 redoubts. After the loss of Spilve battle close to Riga, the Saxon troops retreated from Koknese, on 25 July 1701 Koknese Fortress was blown up and after that was not used for military purposes anymore. The outer defence line of Koknese Fortress never faced military attacks and after 1701 was abandoned, partially levelled by agriculture work, destroyed by activities of the First and the Second World Wars, building of houses, roads and motorway, establishing a cemetery on one of the earthworks, flooding by Pļaviņas hydroelectric power station, etc. At present the front defence system of Koknese Fortress has partially survived, but until the last years the particular system of defence line had not been clearly identified. Now it has been done comparing an image of Koknese from 1701 with the results of aerial and traditional reconnaissance. Koknese front fortification line is a unique monument under circumstances of Latvia.

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26

Chittibabu, Y., CH Anuradha, and Sri Rama Chandra P. Murty. "Fuzzy Trust Based Energy Aware Multipath Secure Data Collection in Wireless Sensor Network." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 669–75. http://dx.doi.org/10.1166/jctn.2019.7788.

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Wireless Sensor Network (WSN) is the most promising inventions that can find its application in diverse fields such as army surveillance and forest fire detection. Multi-hop routing is followed in WSN, and the greatest security with effect to identity deception is produced through replaying routing information. An inevitable role is played by trust in the sensor network in case of military and other applications. Serious research work is being conducted on secured data aggregation. Longestablished cryptographic trust-aware routing protocols which are being used currently have become outdated, and their proficiency in tackling the situation is not much satisfactory. This ultimately results in increased complexity, poor link quality and high overhead when it comes to a number of cryptographic methods. This work deals with fuzzy logic based trust evaluation technique that can acquire secured routing. Direct Random Propagation (DRP) protocol and fuzzy logic are used in calculating the trust of the nodes. Flooding attack and black hole are proposed, and it eliminates the attack. The threshold value is compared with trust value. The ultimate result shows that the method that is proposed provides lesser interception probability, packet loss and end-to-end delay.

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27

Sara, Jarso, Yusuf Haji, and Achamyelesh Gebretsadik. "Scabies Outbreak Investigation and Risk Factors in East Badewacho District, Southern Ethiopia: Unmatched Case Control Study." Dermatology Research and Practice 2018 (June 26, 2018): 1–10. http://dx.doi.org/10.1155/2018/7276938.

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Introduction. Scabies is one of the common public health problem but neglected parasitic diseases caused bySarcoptes scabieivar.hominis.Global scabies prevalence in both sexes was 204 million. In Ethiopia, scabies is also a common public health issue but there is lack of studies regarding outbreak investigation and risk factors in the study area. This study was aimed to investigate the scabies suspected outbreak and risk factors in East Badewacho District, Southern Ethiopia, 2016.Methods. A community-based unmatched case control (1 : 2 ratios) study was conducted in East Badewacho District, using collected scabies line listed data and face-to-face interview to assess risk factors during October 23–30, 2016. The data were collected using structured questionnaire, and then the data were coded, entered, cleaned, and analyzed using SPSS statistical software, whereas, line listed data was entered into Microsoft excel for descriptive analyses. Odds ratios (OR) and 95% confidence interval (CI) were computed to determine associated factors.Results. A total of 4,532 scabies cases line listed with overall attack rate of 110/1,000 population. The mean age was 12 years, and most affected age group was 5–14 years. Independent risk factors found to be statistically associated with scabies infestation were age less than 15 years (AOR = 2.62, 95% CI: 1.31–5.22), family size greater than 5 members (AOR = 2.63, 95% CI: 1.10–6.27), bed sharing with scabies cases (AOR = 12.47, 95% CI: 3.05–50.94), and home being affected by flooding (AOR = 22.32, 95% CI: 8.46–58.90).Conclusion. Outbreak of scabies occurred in East Badewacho District. Age less than 15 years, family size greater than five members, sleeping with others, and home being affected by flooding are the risk factors. Providing risk factors related health education on prevention and controls especially, at community level and schools, is recommended.

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28

Jingjing, Zhou, Yang Tongyu, Zhang Jilin, Zhang Guohao, Li Xuefeng, and Pan Xiang. "Intrusion Detection Model for Wireless Sensor Networks Based on MC-GRU." Wireless Communications and Mobile Computing 2022 (September 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/2448010.

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A crucial line of defense for the security of wireless sensor network (WSN) is intrusion detection. This research offers a new MC-GRU WSN intrusion detection model based on convolutional neural networks (CNN) and gated recurrent unit (GRU) to solve the issues of low detection accuracy and poor real-time detection in existing WSN intrusion detection algorithms. MC-GRU uses multiple convolutions to extract network data traffic features and uses the high-level features output after convolution operations as input parameters of the GRU network, which strengthens the learning of spatial and time series features of traffic data and improves the detection performance of the model. The experiment results based on the WSN-DS dataset show that the overall detection accuracy of the four types of attack of black hole, gray hole, flooding, and scheduling and normal behaviors reaches 99.57%, and it is also better than the existing WSN intrusion detection algorithms in real-time performance and classification ability.

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29

Chen, Xu, Wei Feng, Yantian Luo, Meng Shen, Ning Ge, and Xianbin Wang. "Defending Against Link Flooding Attacks in Internet of Things: A Bayesian Game Approach." IEEE Internet of Things Journal, 2021, 1. http://dx.doi.org/10.1109/jiot.2021.3093538.

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30

Rezapour, Amir, and Wen-Guey Tzeng. "RL-Shield: Mitigating Target Link-Flooding Attacks using SDN and Deep Reinforcement Learning Routing Algorithm." IEEE Transactions on Dependable and Secure Computing, 2021, 1. http://dx.doi.org/10.1109/tdsc.2021.3118081.

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31

Mehrtens, Björn, Oliver Lojek, Viktoria Kosmalla, Thea Bölker, and Nils Goseberg. "Foredune growth and storm surge protection potential at the Eiderstedt Peninsula, Germany." Frontiers in Marine Science 9 (January 9, 2023). http://dx.doi.org/10.3389/fmars.2022.1020351.

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In the context of climate change and associated sea level rise, coastal dunes can provide an essential contribution to coastal protection against wave attack and flooding. Since dunes are highly dynamic systems, their potential safety levels are related to their long-term development, varying in time and space, however pertinent research that ties those aspects together are generally scarce. The objective of this study is to analyze the long-term development of a young coastal foredune at the Eiderstedt peninsula, Germany and assess its coastal protection potential. This research presents (i) a novel semi-automated Dune Toe Tracking (DTT) method to systematically extract dune toes from cross-shore elevation profiles; (ii) established tools to derive the extraction of characteristic dune parameters and (iii) a newly defined Critical Storm Surge Level (CSSL) to relate spatio-temporal dune growth with coastal storm surge protection. Based on geospatial survey data, initial dune formation was identified in the 1980s. By 2015, the foredune had developed over a 6.5 km coastal stretch with a mean annual growth of 7.4m³/m. During the course of dune evolution, the seaward dune toe shifted seaward by an average of 2.3m/yr, while simultaneously increasing in height by an average of 1.1 cm/yr. Overall, the foredune formation established a new line of defense in front of an existing dike/dune line that provides spatially varying protection against a mean CSSL of 3.4m + NHN and can serve as an additional buffer against wave attack during severe storm events.

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32

Abrahamse, Jaap Evert, and Erik Schmitz. "‘Batavische constantie’." Bulletin KNOB, September 20, 2022, 1–21. http://dx.doi.org/10.48003/knob.121.2022.3.757.

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Based on archival research, this article describes the actions taken by the city government to put Amsterdam into a state of defence during 1672, the so-called Disaster Year. Particular attention is paid to the spatial consequences of these measures. In the spring of 1672, the Dutch Republic was attacked by an alliance between France, England, Cologne and Münster. The French army’s advance was eventually halted on the border of the province of Holland by dint of flooding the polders. In 1673, the tide of the war turned in the Republic’s favour, and hostilities ceased in 1674. In 1659, Amsterdam had embarked on a series of major urban expansion works between the Leidsegracht canal and the IJ inlet. On 10 June 1672, all city works were halted except those on the fortifications. Priority was given to the restoration of the city wall, which had been weakened by subsidence. Outside the wall, a free field of fire was created, and measures were taken to defend the unfortified IJ shore. The city militia was also reorganized. From June 1672, a semi-circle of low-lying polders around Amsterdam were flooded by opening sluices and breaching dykes. This was done step by step, in a form of dynamic water management that was constantly adapted to the changing circumstances in order to maximize the defensive potential and to minimize the damage. Waterways were blocked off and defended by armed ships. Six fortifications were built on the higher access roads in the immediate vicinity of the city, often close to one of the inundation openings. These were permanently manned. The city government also arranged for the construction of outposts further away, such as in Uithoorn, which were crucial to maintaining the flooding operations. With the river Vecht acting as the first line of defence – the ‘outer wall’ of Amsterdam as it were – Muiden, Weesp, the Hinderdam and Nieuwersluis were also reinforced with fortifications. After the recapture of Naarden in 1673, the first steps were taken to return to normality and in 1674-1675 all temporary fortifications were demolished. All defensive structures disappeared from the landscape around Amsterdam. From this point of view, the spatial consequences seem to have been short-lived. However, the 1672 defence concept served as a model for all later defence lines around Amsterdam, the last one being the Stelling van Amsterdam, or Amsterdam Defence Line, in which the capital city functioned as a ‘national redoubt’. In this respect the spatial consequences of the Disaster Year cannot be underestimated.

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