Zero Botnets: An Observe-Pursue-Counter Approach

• URL: http://arxiv.org/abs/2201.06068v1
• Date: Sun, 16 Jan 2022 15:17:20 GMT
• Title: Zero Botnets: An Observe-Pursue-Counter Approach
• Authors: Jeremy Kepner, Jonathan Bernays, Stephen Buckley, Kenjiro Cho, Cary Conrad, Leslie Daigle, Keeley Erhardt, Vijay Gadepally, Barry Greene, Michael Jones, Robert Knake, Bruce Maggs, Peter Michaleas, Chad Meiners, Andrew Morris, Alex Pentland, Sandeep Pisharody, Sarah Powazek, Andrew Prout, Philip Reiner, Koichi Suzuki, Kenji Takahashi, Tony Tauber, Leah Walker, Douglas Stetson
• Abstract summary: Adversarial Internet robots (botnets) represent a growing threat to the safe use and stability of the Internet. Reducing the presence of botnets on the Internet, with the aspirational target of zero, is a powerful vision for galvanizing policy action.
• Score: 8.30754292538163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial Internet robots (botnets) represent a growing threat to the safe use and stability of the Internet. Botnets can play a role in launching adversary reconnaissance (scanning and phishing), influence operations (upvoting), and financing operations (ransomware, market manipulation, denial of service, spamming, and ad click fraud) while obfuscating tailored tactical operations. Reducing the presence of botnets on the Internet, with the aspirational target of zero, is a powerful vision for galvanizing policy action. Setting a global goal, encouraging international cooperation, creating incentives for improving networks, and supporting entities for botnet takedowns are among several policies that could advance this goal. These policies raise significant questions regarding proper authorities/access that cannot be answered in the abstract. Systems analysis has been widely used in other domains to achieve sufficient detail to enable these questions to be dealt with in concrete terms. Defeating botnets using an observe-pursue-counter architecture is analyzed, the technical feasibility is affirmed, and the authorities/access questions are significantly narrowed. Recommended next steps include: supporting the international botnet takedown community, expanding network observatories, enhancing the underlying network science at scale, conducting detailed systems analysis, and developing appropriate policy frameworks.

Related papers

• Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
  Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure. This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
  arXiv  Detail & Related papers  (2025-02-27T11:22:25Z)
• Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack [0.0]
  Malicious actors, including hackers and hacktivist groups, often disseminate ideological content and coordinate activities through the "Dark Web" This paper proposes a novel predictive threat intelligence framework designed to systematically collect, analyze, and visualize Dark Web data.
  arXiv  Detail & Related papers  (2024-11-26T23:00:51Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [70.93622520400385]
  This paper systematically quantifies the robustness of VLA-based robotic systems. We introduce an untargeted position-aware attack objective that leverages spatial foundations to destabilize robotic actions. We also design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• 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)
• Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
  Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
  arXiv  Detail & Related papers  (2024-09-29T23:57:25Z)
• AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent [1.3643061988716354]
  We propose an architecture that enhances security within edge networks specifically for intrusion detection. By securing the network environment using an edge network managed by a Raspberry Pi module, we aim to safeguard sensitive data and maintain a secure workplace.
  arXiv  Detail & Related papers  (2024-08-08T07:39:23Z)
• A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes [1.3631461603291568]
  Generative Adversarial Networks (GANs) have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains.
  arXiv  Detail & Related papers  (2024-07-11T19:51:48Z)
• Guardians of Anonymity: Exploring Tactics to Combat Cyber Threats in Onion Routing Environments [0.0]
  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.
  arXiv  Detail & Related papers  (2024-05-11T23:18:00Z)
• 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)
• 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)
• Darknet Traffic Big-Data Analysis and Network Management to Real-Time Automating the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework [0.0]
  We propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process. It is an effective and accurate computational intelligent tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time.
  arXiv  Detail & Related papers  (2021-02-16T19:03:25Z)
• 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)
• Automating Botnet Detection with Graph Neural Networks [106.24877728212546]
  Botnets are now a major source for many network attacks, such as DDoS attacks and spam. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically.
  arXiv  Detail & Related papers  (2020-03-13T15:34:33Z)

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.