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Journal articles on the topic 'Target attack'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Zhao, Xia, Guowei Liu, and Lei Li. "Importance-driven denial of service attack strategy design against remote state estimation in multi-agent intelligent power systems." Intelligence & Robotics 4, no. 3 (2024): 244–55. http://dx.doi.org/10.20517/ir.2024.16.

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This paper introduces a novel importance-driven denial of service (IDoS) attack strategy aimed at impairing the quality of remote estimators for target agents within multi-agent intelligent power systems. The strategy features two key aspects. Firstly, the IDoS attack strategy concentrates on target agents, enabling attackers to determine the voltage sensitivity of each agent based on limited information. By utilizing these sensitivities, the proposed strategy selectively targets agents with high sensitivity to amplify disruption on the target agent. Secondly, unlike most existing denial of se

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2

Catellani, Patrizia, and Mauro Bertolotti. "The Effects of Counterfactual Attacks on Social Judgments." Social Psychology 45, no. 5 (2014): 371–81. http://dx.doi.org/10.1027/1864-9335/a000195.

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Two experiments were conducted to compare the effects of different styles of verbal criticism (factual vs. counterfactual) on the perceptions of target, source, and quality of the attack. Counterfactual attacks resulted in more negative overall judgment of the target and ratings of the target’s morality than either factual attacks or no attack. Counterfactual attacks were also rated more positively than factual attacks, and the source of the counterfactual attack was rated as being less biased against the target. Regression analyses confirmed that the observed effect on overall judgment was me

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Kaghazgaran, Parisa, Majid Alfifi, and James Caverlee. "TOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 302–12. http://dx.doi.org/10.1609/icwsm.v13i01.3231.

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Online platforms like Amazon, Yelp, and Regulations.gov give a voice to masses of users through reviews, comments, and ratings. However, this crowd-based feedback is susceptible to manipulation. To tackle this problem, most previous efforts have only indirectly sought to uncover targets of attacks by focusing on manipulation at the review or user level. Instead, this paper focuses on the challenge of countering target-oriented crowd attacks. We introduce a unique ground truth dataset of Amazon products that have been targeted for attack and identify two target-oriented attack patterns: (i) pro

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Santifort, Charlinda, Todd Sandler, and Patrick T. Brandt. "Terrorist attack and target diversity." Journal of Peace Research 50, no. 1 (2012): 75–90. http://dx.doi.org/10.1177/0022343312445651.

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Terrorists choose from a wide variety of targets and attack methods. Unlike past literature, this article investigates how diversity in target choice and attack modes among domestic and transnational terrorists has evolved and changed over the past 40 years. Changes in the practice of homeland security, which affects the marginal costs of target–attack combinations, and changes in the dominant terrorist influence at the global level, which affects the marginal benefits of target–attack combinations, drive the changepoints. Our empirical analysis relies on count data drawn from the Global Terro

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Yang, Rong, Fang Ming Huang, and Hua Jun Gong. "Best Attack Position Model for BVR Multi-Target Air Combat." Advanced Materials Research 1016 (August 2014): 511–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.511.

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Refer to the characteristics of BVR air combat and multi-target attack for the Fourth Generation Fighters, this paper constructs and computes the model of probability distributions in multi-target kill zone, the model of the best attack path/attack position of multi-target attack. The model of probability distributions in multi-target kill zone considers heading angle and approaching angle of target, distance between fighter and target, maximum off-boresight launching angle and killing angle. The model of the best attack path/attack position considers damage probability to targets by missiles,

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Zhang, Mingjiang, Junfei Bi, Tao Xie, Na Zhang, and Weihu Zhao. "Adversarial Patch Attack Method for Vehicle Target Detection." Journal of Physics: Conference Series 2717, no. 1 (2024): 012026. http://dx.doi.org/10.1088/1742-6596/2717/1/012026.

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Abstract Due to the susceptibility of intelligent recognition systems to adversarial sample attacks, research on adversarial sample attack methods for target detection is of great significance for improving the safety of vehicle autonomous driving. By studying the transfer-based self-ensemble attack adversarial sample generation algorithm, and based on the trained Faster R-CNN and YOLOX white box target detectors, a cross-model and cross-instance vehicle target detection attack method was designed. Through training, a universal adversarial patch was obtained, which achieved recognition attacks

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K., Dharani, Rajesh M., and T. Senthil Prakash Dr. "DEDUCTION ATTACK ON BROWSING HISTORY IN TWITTER USING PUBLIC CLICK ANALYTIC AND METADATA." International Journal of Computational Research and Development 1, no. 2 (2017): 107–10. https://doi.org/10.5281/zenodo.438971.

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Our attacks rely on the combination of publicly available information: click analytics from URL shortening services and metadata from Twitter. The goal of the attacks is to know which URLs are clicked on by target users. We introduce two different attack methods: (i) an attack to know who click on the URLs updated by target users and (ii) an attack to know which URLs are clicked on by target users. To perform the first attack, we find a number of Twitter users who frequently distribute shortened URLs, and investigate the click analytics of the distributed shortened URLs and the metadata of the

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Gao, Wei, Yunqing Liu, Yi Zeng, Quanyang Liu, and Qi Li. "SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research." Sensors 23, no. 4 (2023): 2266. http://dx.doi.org/10.3390/s23042266.

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The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algor

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9

Zeman, Tomáš. "Soft Targets: Definition and Identification." Academic and Applied Research in Military and Public 19, no. 1 (2020): 109–19. http://dx.doi.org/10.32565/aarms.2020.1.10.

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Available definitions describe a soft target as a location with high vulnerability, but a low level of protection. However, such general definitions can hardly be used in the process of soft targets identification. The aim of the article is to create a temporary specific definition that could be utilised for this purpose. The suggested definition of a soft target is based on performed statistical analysis of 275 cases of terrorist attacks aimed against soft targets in the European Union from 2000 to 2015. In the definition, a soft target is characterised based on the probability of a terrorist

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10

Riyadh, Rahef Nuiaa, Manickam Selvakumar, and Hakem Alsaeedi Ali. "Distributed reflection denial of service attack: A critical review." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5327–41. https://doi.org/10.11591/ijece.v11i6.pp5327-5341.

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As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as d

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11

Shui, Yongtao, Yu Wang, Yu Li, Yongzhi Shan, Naigang Cui, and Baojun Pang. "Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network." Applied Sciences 11, no. 10 (2021): 4564. http://dx.doi.org/10.3390/app11104564.

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For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is

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Zeng, Meifang, Ke Li, Bingchuan Jiang, Liujuan Cao, and Hui Li. "Practical Cross-System Shilling Attacks with Limited Access to Data." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4864–74. http://dx.doi.org/10.1609/aaai.v37i4.25612.

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In shilling attacks, an adversarial party injects a few fake user profiles into a Recommender System (RS) so that the target item can be promoted or demoted. Although much effort has been devoted to developing shilling attack methods, we find that existing approaches are still far from practical. In this paper, we analyze the properties a practical shilling attack method should have and propose a new concept of Cross-system Attack. With the idea of Cross-system Attack, we design a Practical Cross-system Shilling Attack (PC-Attack) framework that requires little information about the victim RS

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13

Chen, Jing. "DDoS Attack Target Detection based on AM+BPNN." Scientific Journal of Technology 4, no. 8 (2022): 45–49. http://dx.doi.org/10.54691/sjt.v4i8.1647.

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The computer has developed from a single machine to a multi-machine network. The development of the network has penetrated into people's life, and similarly, the problem of network security has also followed. Distributed Denial of Service (DDoS) attack is one of the most popular network attacks at present. How to effectively detect DDoS attack targets and take urgent protective measures has become one of the difficulties in the research community. In this paper, a method of detecting DDoS attacks by using CNN neural network with attention mechanism (AM) is proposed by using the characteristics

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Mehrabi, Ninareh, Muhammad Naveed, Fred Morstatter, and Aram Galstyan. "Exacerbating Algorithmic Bias through Fairness Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8930–38. http://dx.doi.org/10.1609/aaai.v35i10.17080.

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Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairn

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Choi, Jinhyeok, Heehyeon Kim, and Joyce Jiyoung Whang. "Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16028–36. https://doi.org/10.1609/aaai.v39i15.33760.

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Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-based fraud detectors and their risks have rarely been studied, thereby leaving potential threats unaddressed. Recent findings suggest that frauds are increasingly organized as gangs or groups. In this work, we design attack scenarios where fraud gangs aim to make their fraud nodes misclassified as benign by camouflaging their illicit activities in collusion. Based on these scenarios, we study adversarial attacks agai

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Li, Xinzhan, Yang Zhou, Xin Li, Lijuan Xu, and Dawei Zhao. "Protection Strategy Selection Model Based on Genetic Ant Colony Optimization Algorithm." Mathematics 10, no. 21 (2022): 3938. http://dx.doi.org/10.3390/math10213938.

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Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the

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17

Yang, Xiong, Wang, and Zhang. "Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks." Sensors 19, no. 15 (2019): 3436. http://dx.doi.org/10.3390/s19153436.

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Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer–Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion

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18

Zhu, Rui, Shiping Ma, Linyuan He, and Wei Ge. "FFA: Foreground Feature Approximation Digitally against Remote Sensing Object Detection." Remote Sensing 16, no. 17 (2024): 3194. http://dx.doi.org/10.3390/rs16173194.

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In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) to achieve the attack. These methods do not discover the common vulnerabilities of the ODs and, thus, the transferability is weak. Based on this, this paper proposes a foreground feature approximation (FFA) method to generate adversarial examples (AEs) that discover the common vulnerabilities of t

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Schmeitz, Cindy T. J., Dennis G. Barten, Kevin W. Y. van Barneveld, et al. "Terrorist Attacks Against Emergency Medical Services: Secondary Attacks are an Emerging Risk." Prehospital and Disaster Medicine 37, no. 2 (2022): 185–91. http://dx.doi.org/10.1017/s1049023x22000140.

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AbstractIntroduction:Terrorists increasingly aim at so-called soft targets, such as hospitals. However, little is known about terrorist attacks against Emergency Medical Services (EMS).Objective:This study aims to review all documented terrorist attacks against EMS that occurred world-wide from 1970-2019 using the Global Terrorism Database (GTD).Methods:Reports of terrorist attacks against EMS were extracted from the GTD from 1970-2019. Data collection included temporal factors, attack and weapon type, number of casualties, and if it was a primary or secondary attack (secondary attack: deliber

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20

Pan, Xiaohui, and Tao Zhang. "Machine learning-based target prediction for terrorist attacks." Journal of Physics: Conference Series 2577, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2577/1/012007.

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Abstract To address the problem that terrorist attacks are frequent and attack targets are widely difficult to predict, we use multidimensional features of terrorist attacks to predict terrorist targets based on a quantitative statistical analysis of data in the global terrorism database (GTD) from 1970 to 2019. In this paper, a machine learning-based prediction model (i.e., a classifier framework) is proposed. The model pre-processes GTD data and uses the OneHotEncoder and KBinsDiscretizer methods for data category transformation, classifies terrorist targets by four algorithms—SVM, decision

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Devi, B. S. Kiruthika, T. Subbulakshmi, and KV Mahesh Babu. "A Survey of Security Models Using Effective Moving Target Defenses." International Journal of Information Security and Privacy 12, no. 3 (2018): 123–40. http://dx.doi.org/10.4018/ijisp.2018070107.

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This article describes how nowadays, attackers are targeting valuable assets and infrastructures in networked systems causing an impact on enterprises and individuals. By implementing moving target defenses helps to prevent cyber-attacks by changing the attack surface. Some security models like Attack Graph (A.G) and Attack Tree (A.T) provide a formal method to access and compare the effectiveness of them. So, in this article, the authors incorporate moving target defenses in a security model, using a Hierarchical Attack Representation Model (HARM), to compare and access the effectiveness of t

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Xu, Xiaoyu, Hao Hu, and Xianwei Zhu. "Compressing Deep Learning Model for Agile Moving Target Defense." Journal of Physics: Conference Series 2171, no. 1 (2022): 012032. http://dx.doi.org/10.1088/1742-6596/2171/1/012032.

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Abstract The moving target defense achieves the effect of defending against network attacks by constantly changing the attack surface. IP hopping defense is a typical representative of network layer moving target defense technology. It has been verified to show considerable defense effect against DDoS attacks and scanning attacks. Aiming at the problems that the system resource overhead of the existing IP hopping defense technology is too large, an agile IP hopping defense technology based on compressed neural network is proposed in this paper. A convolutional neural network (CNN) is deployed

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DOLEV, SHLOMI, YUVAL ELOVICI, ALEX KESSELMAN, and POLINA ZILBERMAN. "TRAWLING TRAFFIC UNDER ATTACK OVERCOMING DDoS ATTACKS BY TARGET-CONTROLLED TRAFFIC FILTERING." International Journal of Foundations of Computer Science 22, no. 05 (2011): 1073–98. http://dx.doi.org/10.1142/s012905411100857x.

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As more and more services are provided by servers via the Internet, Denial-of-Service (DoS) attacks pose an increasing threat to the Internet community. A DoS attack overloads the target server with a large volume of adverse requests, thereby rendering the server unavailable to "well-behaved" users. In this paper, we propose two algorithms that allow attack targets to dynamically filter their incoming traffic based on a distributed policy. The proposed algorithms defend the target against DoS and distributed DoS (DDoS) attacks and simultaneously ensure that it continues to serve "well-behaved"

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Zimek, Ondřej. "Risk analysis of the soft targets according to the methodology." MATEC Web of Conferences 292 (2019): 01039. http://dx.doi.org/10.1051/matecconf/201929201039.

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The article deals with the fact that religiously motivated terrorism has become more common in Europe, and more than 50% of the attacks have been focused on so-called Soft Targets. The purpose of this paper is to make a risk analysis of a particular Soft Target by the Ministry of the Interior methodology. The Tax Office of the Zlin region was chosen as a Soft Target. Auxiliary tables – Likelihood and Impact are necessary for this methodology. Performing an attack, Place of attack, and Time of attack are very important too. The Degree of Threat is determined as the product of Total Likelihood a

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Cui, Pengshuai, Peidong Zhu, Peng Xun, and Chengcheng Shao. "Robustness of Cyber-Physical Systems against Simultaneous, Sequential and Composite Attack." Electronics 7, no. 9 (2018): 196. http://dx.doi.org/10.3390/electronics7090196.

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In this paper, a failure model of Cyber-Physical systems and an attack model are proposed. We divide the attacks into three kinds: simultaneous attack, sequential attack and composite attack. Through numerical simulations, we find that: (1) the sequential attack may bring more damage in single physical systems; (2) the coupling process of cyber system and physical systems makes it possible that sequential attack causes more damage than simultaneous attacks when the attackers only attack the cyber system; (3) with some target sets, composite attack leads to more failures than both simultaneous

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Jeong, Kwangyul, Adin Ming Tan, Takeshi Asai, Kunihide Koda, and Franz Konstantin Fuss. "Pressure Sensors for Measuring the Grip Pressure during Kendo Attacks: Assessment of Laterality and Evidence of the Five Phases of Attack." Sensors 23, no. 3 (2023): 1189. http://dx.doi.org/10.3390/s23031189.

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In Kendo, there is no consensus as to which hand should produce more pressure when attacking the opponent with the bamboo sword, let alone how to teach the pressure distribution during coaching. There is the theory that a Kendo attack can be divided into five phases, which has not entered the coaching practice, either. The aim of this study was to measure the grip pressure during Kendo attacks, investigate the pressure distribution between the two hands, and find evidence for the existence of the alleged five attack phases. We instrumented a bamboo sword with grip pressure sensors and investig

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Akhtar, Muhammad Moin, Yong Li, Wei Cheng, Limeng Dong, Yumei Tan, and Langhuan Geng. "AOHDL: Adversarial Optimized Hybrid Deep Learning Design for Preventing Attack in Radar Target Detection." Remote Sensing 16, no. 16 (2024): 3109. http://dx.doi.org/10.3390/rs16163109.

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In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for

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Rădulescu, Anca, and Marios O. Choudary. "Side-Channel Attacks on Masked Bitsliced Implementations of AES." Cryptography 6, no. 3 (2022): 31. http://dx.doi.org/10.3390/cryptography6030031.

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In this paper, we provide a detailed analysis of CPA and Template Attacks on masked implementations of bitsliced AES, targeting a 32-bit platform through the ChipWhisperer side-channel acquisition tool. Our results show that Template Attacks can recover the full AES key successfully within 300 attack traces even on the masked implementation when using a first-order attack (no pre-processing). Furthermore, we confirm that the SubBytes operation is overall a better target for Template Attacks due to its non-linearity, even in the case of bitsliced implementations, where we can only use two bits

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DeTrano, Alexander, Naghmeh Karimi, Ramesh Karri, Xiaofei Guo, Claude Carlet, and Sylvain Guilley. "Exploiting Small Leakages in Masks to Turn a Second-Order Attack into a First-Order Attack and Improved Rotating Substitution Box Masking with Linear Code Cosets." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/743618.

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Masking countermeasures, used to thwart side-channel attacks, have been shown to be vulnerable to mask-extraction attacks. State-of-the-art mask-extraction attacks on the Advanced Encryption Standard (AES) algorithm target S-Box recomputation schemes but have not been applied to scenarios where S-Boxes are precomputed offline. We propose an attack targeting precomputed S-Boxes stored in nonvolatile memory. Our attack targets AES implemented in software protected by a low entropy masking scheme and recovers the masks with 91% success rate. Recovering the secret key requires fewer power traces (

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Du, Chuan, and Lei Zhang. "Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network." Remote Sensing 13, no. 21 (2021): 4358. http://dx.doi.org/10.3390/rs13214358.

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Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry wea

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Zhang, Yipeng, Min Li, Xiaoming Zhang, Yueying He, and Zhoujun Li. "Defeat Magic with Magic: A Novel Ransomware Attack Method to Dynamically Generate Malicious Payloads Based on PLC Control Logic." Applied Sciences 12, no. 17 (2022): 8408. http://dx.doi.org/10.3390/app12178408.

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The Industrial Control System (ICS) is a public facility that provides services to lots of users; thus, its security has always been a critical factor in measuring its availability. Recently, a new type of attack on ICS has occurred frequently, which realizes the extortion of users by invading the information domain and destroying the physical domain. However, due to the diversity and unavailability of an ICS control logic, the targets of such attacks are usually limited to PCs and servers, leaving more disruptive attack methods unexplored. To contribute more possible attack methods to strengt

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Davydovich, I., V. Zurakhov, and I. Ushakov. "Methodology for conducting stress testing on a target web server." Telecom IT 9, no. 1 (2021): 79–86. http://dx.doi.org/10.31854/2307-1303-2021-9-1-79-86.

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This article discusses the methods of an attacker to conduct an attack on a web server and how to pro-tect against them. This methodology contains a description of the stages of preparing an attack and a description of how to protect against them. The aim of the work is to draw up such a methodology, with the help of which it is possible to prevent the majority of hacker attacks. Since Internet technologies have been developing at a rapid pace lately, and companies are moving their businesses to the Internet, the issue of information security is more urgent than ever. One of the methods to def

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Hariprasad, Sivaraman. "Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 5 (2019): 1–6. https://doi.org/10.5281/zenodo.14259482.

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With the rise of hybrid cloud environments comes new security concerns, including sophisticated Distributed Denial of Service (DDoS) attacks that continuously increase in scale and severity. Multi-vector attacks use different attack vectors, unlike traditional single-vector DDoS attacks that can be released at the same target in a coordinated manner or at several targets, but only using one method of attack; multi-layered attack methods which hit systems at all levels of the network stack. The paper proposes a behavior-based DDoS detection system using machine learning that continuously learns

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Nuiaa, Riyadh Rahef, Selvakumar Manickam, and Ali Hakem Alsaeedi. "Distributed reflection denial of service attack: A critical review." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5327. http://dx.doi.org/10.11591/ijece.v11i6.pp5327-5341.

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As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed r

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Xu, Xiaoyu, Hao Hu, Yuling Liu, Hongqi Zhang, and Dexian Chang. "An Adaptive IP Hopping Approach for Moving Target Defense Using a Light-Weight CNN Detector." Security and Communication Networks 2021 (August 10, 2021): 1–17. http://dx.doi.org/10.1155/2021/8848473.

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Scanning attack is normally the first step of many other network attacks such as DDoS and propagation worm. Because of easy implementation and high returns, scanning attack especially cooperative scanning attack is widely used by hackers, which has become a serious threat to network security. In order to defend against scanning attack, this paper proposes an adaptive IP hopping in software defined network for moving target defense (MTD). In order to accurately respond to attacker’s behavior in real time, a light-weight convolutional neural network (CNN) detector composed of three convolutional

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Yan, Yao, and Rui Xu. "DDoS Attacks for Ad Hoc Network Based on Attack Cluster." Advanced Materials Research 546-547 (July 2012): 1371–76. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1371.

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Show the definition of Ad Hoc network attack cluster, and propose a new method of DDoS attacks for Ad Hoc Networks, which can accurately attack the target node, demonstrate no redundant aggressive behavior compared with the traditional attack and reduce the detection rate of aggressive behavior. Use NS2 simulation platform to build Ad Hoc network simulation scenarios with dynamic topology, and simulate DDoS attacks in this environment; The simulation results show that the new DDoS attack method can effectively reduce the communication ability of the Ad Hoc network, and increasing the attack no

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Zhang, Yang, Yourong Chen, Kelei Miao, Tiaojuan Ren, Changchun Yang, and Meng Han. "A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems." Sensors 22, no. 23 (2022): 9125. http://dx.doi.org/10.3390/s22239125.

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In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the

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ASLAM, FAHEEM, AMIR RAFIQUE, ANEEL SALMAN, HYOUNG-GOO KANG, and WAHBEEAH MOHTI. "THE IMPACT OF TERRORISM ON FINANCIAL MARKETS: EVIDENCE FROM ASIA." Singapore Economic Review 63, no. 05 (2018): 1183–204. http://dx.doi.org/10.1142/s0217590815501118.

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This paper examines the impact of 410 terrorist attacks on the performance of five Asian stock markets. The empirical findings indicate that terrorism has a significant impact on the stock markets. Furthermore, the magnitude of these effects varies with respect to country, attack type, target type and severity of the attacks. In target type, terrorist attacks on business sector and security forces are particularly destructive for the stock markets. Likewise, in attack type, suicide attacks and bomb blasts particularly generate a significant downward movement in the stock markets. Furthermore,

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Susanto, Andy, and Wahyu Kusuma Raharja. "Simulation and Analysis of Network Security Performance Using Attack Vector Method for Public Wifi Communication." IJICS (International Journal of Informatics and Computer Science) 5, no. 1 (2021): 7. http://dx.doi.org/10.30865/ijics.v5i1.2764.

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The use of wifi networks in public spaces has a risk of robbery of user access data in cyberspace, such as banking transactions, social media and other online access. The threat of Man In The Middle Attack (MITM) attacks is carried out on public wifi networks to gain access to user information by illegal means. The attack vector simulation process is carried out on the site access exampleriset.com/dvwa/login.php. ARP Poisoning attack with Ettercap device performs interception and manipulation which provides 08: 00: 27: 25: 22: 99 MAC address information to the target. Session Hijacking attack

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Huang, Yudi, Yilei Lin, and Ting He. "Optimized Cross-Path Attacks via Adversarial Reconnaissance." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 3 (2023): 1–30. http://dx.doi.org/10.1145/3626789.

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While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack path

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Huang, Yudi, Yilei Lin, and Ting He. "Optimized Cross-Path Attacks via Adversarial Reconnaissance." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 51–52. http://dx.doi.org/10.1145/3673660.3655075.

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While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack path

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42

Dou, Jihua. "Air Target Attack Intention Recognition Analysis." Advances in Engineering Technology Research 7, no. 1 (2023): 629. http://dx.doi.org/10.56028/aetr.7.1.629.2023.

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Aimed at the problem of air target attack intention recognition, based on the analysis of the influencing factors of air target attack intention, the common methods of air target attack intention recognition were analyzed , the air target attack intention recognition method based on fuzzy similarity was proposed, the basic steps of air target attack intention recognition based on fuzzy similarity were proposed, and the establishment method of membership function was analyzed.

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Li, Haoyang, Shimin Di, Calvin Hong Yi Li, Lei Chen, and Xiaofang Zhou. "Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense." Proceedings of the VLDB Endowment 17, no. 8 (2024): 2050–63. http://dx.doi.org/10.14778/3659437.3659457.

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Graph neural networks (GNNs) have achieved great success on various graph tasks. However, recent studies have revealed that GNNs are vulnerable to injective attacks. Due to the openness of platforms, attackers can inject malicious nodes with carefully designed edges and node features, making GNNs misclassify the labels of target nodes. To resist such adversarial attacks, recent researchers propose GNN defenders. They assume that the attack patterns have been known, e.g., attackers tend to add edges between dissimilar nodes. Then, they remove edges between dissimilar nodes from attacked graphs,

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Han, Jaeseung, and Dong-Guk Han. "Improved Side-Channel Attack on CTR DRBG Using a Clustering Algorithm." Sensors 25, no. 13 (2025): 4170. https://doi.org/10.3390/s25134170.

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Deterministic random bit generators (DRBG) play a crucial role in device security because they generate secret information cryptographic systems, e.g., secret keys and parameters. Thus, attacks on DRBGs can result in the exposure of important secret values, which can threaten the entire cryptographic system of the target Internet of Things (IoT) equipment and smart devices. In 2020, Meyer proposed a side-channel attack (SCA) method that recovers the output random bits by analyzing the power consumption traces of the NIST standard AES CTR DRBG. In addition, most algorithmic countermeasures agai

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Junaedi, Mufid, Ahmad Fachrurozi, Mochammad Rizky Kusumayudha, and Windu Gata. "Analysis of the classification of terrorist attacks in Indonesia." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (2020): 57–66. http://dx.doi.org/10.31289/jite.v4i1.3788.

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Terrorist attacks are now being global issue both in developing and developed countries. There are more than 180,000 terrorist attacks in 1970-2017. Indonesia is one of the countries attacked by terorist. Bombings and firearms cause fatalities. Classification of terorist attacks can be performed based on either the attack succed or not. Succed attack is defined as an unavoided action that caused fatalities. There are seven attributes studied in this paper: year, month, attack type, terorist name, target attack, city, and weapon type uses to attack. Evaluations shows that k-NN classiefier exert

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Wu, Young, Jeremy McMahan, Xiaojin Zhu, and Qiaomin Xie. "Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10426–34. http://dx.doi.org/10.1609/aaai.v37i9.26240.

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In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker modifies the rewards in the dataset before the agents see the dataset. The attacker wants to guide each agent into a nefarious target policy while minimizing the Lp norm of the reward modification. Unlike attacks on single-agent RL, we show that the attacker can install the target policy as a Markov Perfect Dominant Strategy Equilibrium (MPDSE), which rational agents are guaranteed to follow. This attack can be signif

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Park, Hosung, Gwonsang Ryu, and Daeseon Choi. "Partial Retraining Substitute Model for Query-Limited Black-Box Attacks." Applied Sciences 10, no. 20 (2020): 7168. http://dx.doi.org/10.3390/app10207168.

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Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks. In black-box environments, adversaries have limited knowledge regarding the target model. This makes it difficult to estimate gradients for crafting adversarial examples, such that powerful white-box algorithms cannot be directly applied to black-box attacks. Therefore, a well-known black-box attack strategy creates local DNNs, called substitute models, to emulate the target model. The adversaries then craf

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He, Jiaming, Wenbo Jiang, Guanyu Hou, Wenshu Fan, Rui Zhang, and Hongwei Li. "Watch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26220–28. https://doi.org/10.1609/aaai.v39i25.34819.

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Mainstream backdoor attacks on large language models (LLMs) typically set a fixed trigger in the input instance and specific responses for triggered queries. However, the fixed trigger setting (e.g., unusual words) may be easily detected by human detection, limiting the effectiveness and practicality in real-world scenarios. To enhance the stealthiness of backdoor activation, we present a new poisoning paradigm against LLMs triggered by specifying generation conditions, which are commonly adopted strategies by users during model inference. The poisoned model performs normally for output under

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Li, Yunpeng, and Xi Li. "Research on Multi-Target Network Security Assessment with Attack Graph Expert System Model." Scientific Programming 2021 (May 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/9921731.

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With the rapid development of the Internet, network attacks often occur, and network security is widely concerned. Searching for practical security risk assessment methods is a research hotspot in the field of network security. Network attack graph model is an active detection technology for the attack path. From the perspective of the attacker, it simulated the whole network attack scenario and then presented the dependency among the vulnerabilities in the target network in the way of directed graph. It is an effective tool for analyzing network vulnerability. This paper describes in detail t

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Zhao, Zheng, Fenlin Liu, and Daofu Gong. "An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks." Security and Communication Networks 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1560594.

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Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are anal

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