New perspectives on the optimal placement of detectors for suicide bombers using metaheuristics

• URL: http://arxiv.org/abs/2405.19060v1
• Date: Wed, 29 May 2024 13:06:10 GMT
• Title: New perspectives on the optimal placement of detectors for suicide bombers using metaheuristics
• Authors: Carlos Cotta, José E. Gallardo,
• Abstract summary: We consider an operational model of suicide bombing attacks against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. We consider four different algorithms, namely a greedy hill climber, a tabu search and an evolutionary algorithm. It is shown that the adversarial scenario is harder for all techniques, and that the evolutionary algorithm seems to adapt better to the complexity of the resulting search landscape.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider an operational model of suicide bombing attacks -- an increasingly prevalent form of terrorism -- against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. These detectors have to be carefully located in order to minimize the expected number of casualties or the economic damage suffered, resulting in a hard optimization problem for which different metaheuristics have been proposed. Rather than assuming random decisions by the attacker, the problem is approached by considering different models of the latter, whereby he takes informed decisions on which objective must be targeted and through which path it has to be reached based on knowledge on the importance or value of the objectives or on the defensive strategy of the defender (a scenario that can be regarded as an adversarial game). We consider four different algorithms, namely a greedy heuristic, a hill climber, tabu search and an evolutionary algorithm, and study their performance on a broad collection of problem instances trying to resemble different realistic settings such as a coastal area, a modern urban area, and the historic core of an old town. It is shown that the adversarial scenario is harder for all techniques, and that the evolutionary algorithm seems to adapt better to the complexity of the resulting search landscape.

Related papers

• Metaheuristic approaches to the placement of suicide bomber detectors [0.0]
  Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. To this end, different metaheuristic approaches based on local search and on population-based search are considered and benchmarked against a powerful greedy algorithm.
  arXiv  Detail & Related papers  (2024-05-28T21:14:01Z)
• A Proactive Decoy Selection Scheme for Cyber Deception using MITRE ATT&CK [0.9831489366502301]
  Cyber deception allows compensating the late response of defenders to the ever evolving tactics, techniques, and procedures (TTPs) of attackers. In this work, we design a decoy selection scheme that is supported by an adversarial modeling based on empirical observation of real-world attackers. Results reveal that the proposed scheme provides the highest interception rate of attack paths using the lowest amount of decoys.
  arXiv  Detail & Related papers  (2024-04-19T10:45:05Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game [153.74942025516853]
  The intrinsic vulnerability of the rank aggregation methods is not well studied in the literature. In this paper, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data. The effectiveness of the suggested target attack strategies is demonstrated by a series of toy simulations and several real-world data experiments.
  arXiv  Detail & Related papers  (2022-09-13T05:59:02Z)
• A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design [1.9100854225243937]
  Well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. Given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. We propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models.
  arXiv  Detail & Related papers  (2022-08-15T03:03:05Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)
• Identification of Attack-Specific Signatures in Adversarial Examples [62.17639067715379]
  We show that different attack algorithms produce adversarial examples which are distinct not only in their effectiveness but also in how they qualitatively affect their victims. Our findings suggest that prospective adversarial attacks should be compared not only via their success rates at fooling models but also via deeper downstream effects they have on victims.
  arXiv  Detail & Related papers  (2021-10-13T15:40:48Z)
• Unknown Presentation Attack Detection against Rational Attackers [6.351869353952288]
  Presentation attack detection and multimedia forensics are still vulnerable to attacks in real-life settings. Some of the challenges for existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few-shot learning, and explainability. New optimization criterion is proposed and a set of requirements are defined for improving the performance of these systems in real-life settings.
  arXiv  Detail & Related papers  (2020-10-04T14:37:10Z)
• A black-box adversarial attack for poisoning clustering [78.19784577498031]
  We propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
  arXiv  Detail & Related papers  (2020-09-09T18:19:31Z)
• Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey [1.8782750537161614]
  This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting, and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses.
  arXiv  Detail & Related papers  (2020-07-01T21:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.