A Survey of Network Requirements for Enabling Effective Cyber Deception
- URL: http://arxiv.org/abs/2309.00184v3
- Date: Mon, 8 Jan 2024 05:09:31 GMT
- Title: A Survey of Network Requirements for Enabling Effective Cyber Deception
- Authors: Md Abu Sayed, Moqsadur Rahman, Mohammad Ariful Islam Khan, Deepak Tosh,
- Abstract summary: This paper investigates the crucial network requirements essential for the successful implementation of effective cyber deception techniques.
With a focus on diverse network architectures and topologies, we delve into the intricate relationship between network characteristics and the deployment of deception mechanisms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the evolving landscape of cybersecurity, the utilization of cyber deception has gained prominence as a proactive defense strategy against sophisticated attacks. This paper presents a comprehensive survey that investigates the crucial network requirements essential for the successful implementation of effective cyber deception techniques. With a focus on diverse network architectures and topologies, we delve into the intricate relationship between network characteristics and the deployment of deception mechanisms. This survey provides an in-depth analysis of prevailing cyber deception frameworks, highlighting their strengths and limitations in meeting the requirements for optimal efficacy. By synthesizing insights from both theoretical and practical perspectives, we contribute to a comprehensive understanding of the network prerequisites crucial for enabling robust and adaptable cyber deception strategies.
Related papers
- Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories [15.764094200832071]
Cyber resilience focuses on preparation, response, and recovery from cyber threats that are challenging to prevent.
Game theory, control theory, and learning theories are three major pillars for the design of cyber resilience mechanisms.
This chapter presents various theoretical paradigms, including dynamic asymmetric games, moving horizon control, conjectural learning, and meta-learning.
arXiv Detail & Related papers (2024-04-01T16:02:21Z) - Attention-Based Real-Time Defenses for Physical Adversarial Attacks in
Vision Applications [58.06882713631082]
Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks raises serious security concerns.
This paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers.
It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.
arXiv Detail & Related papers (2023-11-19T00:47:17Z) - Stepping out of Flatland: Discovering Behavior Patterns as Topological Structures in Cyber Hypergraphs [0.7835894511242797]
We present a novel framework based in the theory of hypergraphs and topology to understand data from cyber networks.
We will demonstrate concrete examples in a large-scale cyber network dataset.
arXiv Detail & Related papers (2023-11-08T00:00:33Z) - Enhancing Network Resilience through Machine Learning-powered Graph
Combinatorial Optimization: Applications in Cyber Defense and Information
Diffusion [0.0]
This thesis focuses on developing effective approaches for enhancing network resilience.
Existing approaches for enhancing network resilience emphasize on determining bottleneck nodes and edges in the network.
This thesis aims to design effective, efficient and scalable techniques for discovering bottleneck nodes and edges in the network.
arXiv Detail & Related papers (2023-09-22T01:48:28Z) - A Theoretical Perspective on Subnetwork Contributions to Adversarial
Robustness [2.064612766965483]
This paper investigates how the adversarial robustness of a subnetwork contributes to the robustness of the entire network.
Experiments show the ability of a robust subnetwork to promote full-network robustness, and investigate the layer-wise dependencies required for this full-network robustness to be achieved.
arXiv Detail & Related papers (2023-07-07T19:16:59Z) - Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey [15.633226785669203]
This survey aims to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid.
An integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis.
A CRE framework is subsequently proposed to incorporate the five key resiliency enablers.
arXiv Detail & Related papers (2023-05-09T10:59:56Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A
Contemporary Survey [114.17568992164303]
Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention.
This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques.
New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
arXiv Detail & Related papers (2023-03-11T04:19:31Z) - Optimism in the Face of Adversity: Understanding and Improving Deep
Learning through Adversarial Robustness [63.627760598441796]
We provide an in-depth review of the field of adversarial robustness in deep learning.
We highlight the intuitive connection between adversarial examples and the geometry of deep neural networks.
We provide an overview of the main emerging applications of adversarial robustness beyond security.
arXiv Detail & Related papers (2020-10-19T16:03:46Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z) - A general framework for defining and optimizing robustness [74.67016173858497]
We propose a rigorous and flexible framework for defining different types of robustness properties for classifiers.
Our concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy.
We develop a very general robustness framework that is applicable to any type of classification model.
arXiv Detail & Related papers (2020-06-19T13:24:20Z)
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.