Guardians of Anonymity: Exploring Tactics to Combat Cyber Threats in Onion Routing Environments

• URL: http://arxiv.org/abs/2406.07563v1
• Date: Sat, 11 May 2024 23:18:00 GMT
• Title: Guardians of Anonymity: Exploring Tactics to Combat Cyber Threats in Onion Routing Environments
• Authors: Karwan Mustafa Kareem,
• Abstract summary: Onion routing networks, also known as darknets, are private networks that enable anonymous communication over the Internet. This paper comprehensively analyzes cybercrime threats and countermeasures in onion routing networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Onion routing networks, also known as darknets, are private networks that enable anonymous communication over the Internet. They are used by individuals and organizations to protect their privacy, but they also attract cybercriminals who exploit the anonymity provided by these networks for illegal activities. This paper comprehensively analyzes cybercrime threats and countermeasures in onion routing networks. We review the various types of cybercrime that occur in these networks, including drug trafficking, fraud, hacking, and other illicit activities. We then discuss the challenges associated with detecting and mitigating cybercrime in onion routing networks, such as the difficulty of tracing illegal activities back to their source due to the strong anonymity guarantees provided by these networks. We also explore the countermeasures that have been proposed and implemented to combat cybercrime in onion routing networks, including law enforcement efforts, technological solutions, and policy interventions. Finally, we highlight the limitations of existing countermeasures and identify potential directions for future research in this area, including the need for interdisciplinary approaches that combine technical, legal, and social perspectives to effectively combat cybercrime in onion routing networks.

Related papers

• Countering Autonomous Cyber Threats [40.00865970939829]
  Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations. This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
  arXiv  Detail & Related papers  (2024-10-23T22:46:44Z)
• Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks [82.3753728955968]
  We introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system.
  arXiv  Detail & Related papers  (2024-09-24T03:17:51Z)
• Accelerating Tactile Internet with QUIC: A Security and Privacy Perspective [3.2738809491666077]
  We envision a futuristic scenario where a QUIC-enabled network uses the underlying 6G communication infrastructure to achieve the requirements for Tactile Internet. This article reviews the existing security and privacy attacks in QUIC and their implication on users.
  arXiv  Detail & Related papers  (2024-01-12T16:05:13Z)
• Use of Graph Neural Networks in Aiding Defensive Cyber Operations [2.1874189959020427]
  Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures. We look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain.
  arXiv  Detail & Related papers  (2024-01-11T05:56:29Z)
• A Comprehensive Analytical Review on Cybercrime in West Africa [0.0]
  West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts. This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework. Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies.
  arXiv  Detail & Related papers  (2024-01-07T23:36:43Z)
• Darknet Traffic Analysis A Systematic Literature Review [0.0]
  The objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. The strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. This paper presents a comprehensive analysis of methods of darknet traffic using machine learning techniques to monitor and identify the traffic attacks inside the darknet.
  arXiv  Detail & Related papers  (2023-11-27T19:27:50Z)
• 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)
• Towards Effective Cybercrime Intervention [1.179179628317559]
  We propose to build a systematic framework through the lens of a cyber threat actor. We explore the motivation factors behind the crimes and the crime stages of the threat actors. We then formulate intervention plans so as to discourage the act of committing malicious cyber activities and also aim to integrate ex-cyber offenders back into society.
  arXiv  Detail & Related papers  (2022-11-17T13:40:53Z)
• TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack [46.79557381882643]
  We present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. TANTRA achieves an average success rate of 99.99% in network intrusion detection system evasion.
  arXiv  Detail & Related papers  (2021-03-10T19:03:38Z)
• 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)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

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.