Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021

• URL: http://arxiv.org/abs/2104.13254v1
• Date: Tue, 27 Apr 2021 17:35:31 GMT
• Title: Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
• Authors: John Emanuello, Kimberly Ferguson-Walter, Erik Hemberg, Una-May O Reilly, Ahmad Ridley, Dennis Ross, Diane Staheli, William Streilein
• Abstract summary: Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. The challenges in this space are quite different from those associated with applying AI in other domains such as computer vision. In spite of these challenges, machine learning has been applied to this domain and has achieved some success in the realm of detection. A growing part of the commercial sector is providing ML-enhanced capabilities as a service.
• Score: 6.647584840410018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever-evolving adversary, the massive amounts of data which could contain evidence of an attack, and the speed at which defensive actions must be taken, innovations which enable autonomy in cybersecurity must continue to expand, in order to move away from a reactive defense posture and towards a more proactive one. The challenges in this space are quite different from those associated with applying AI in other domains such as computer vision. The environment suffers from an incredibly high degree of uncertainty, stemming from the intractability of ingesting all the available data, as well as the possibility that malicious actors are manipulating the data. Another unique challenge in this space is the dynamism of the adversary causes the indicators of compromise to change frequently and without warning. In spite of these challenges, machine learning has been applied to this domain and has achieved some success in the realm of detection. While this aspect of the problem is far from solved, a growing part of the commercial sector is providing ML-enhanced capabilities as a service. Many of these entities also provide platforms which facilitate the deployment of these automated solutions. Academic research in this space is growing and continues to influence current solutions, as well as strengthen foundational knowledge which will make autonomous agents in this space a possibility.

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)
• Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions [3.99098935469955]
  Industry 5.0 focuses on human and Artificial Intelligence (AI) collaboration for performing different tasks in manufacturing. The huge involvement of these devices and interconnection in various critical areas, such as economy, health, education and defense systems, poses several types of potential security flaws. XAI has been proven a very effective and powerful tool in different areas of cybersecurity, such as intrusion detection, malware detection, and phishing detection.
  arXiv  Detail & Related papers  (2024-07-21T09:28:05Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• ExTRUST: Reducing Exploit Stockpiles with a Privacy-Preserving Depletion System for Inter-State Relationships [4.349142920611964]
  This paper proposes a privacy-preserving approach that allows multiple state parties to privately compare their stock of vulnerabilities and exploits. We call our system Extrust and show that it is scalable and can withstand several attack scenarios.
  arXiv  Detail & Related papers  (2023-06-01T12:02:17Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Challenges of engineering safe and secure highly automated vehicles [0.0]
  This paper sets out to summarize the major challenges that are still to overcome for achieving safe, secure, reliable and trustworthy highly automated vehicles (HAV) Four challenges have been identified as being the main obstacles to realizing HAV: Realization of continuous, post-deployment systems improvement, handling of uncertainties and incomplete information, verification of HAV with machine learning components, and prediction.
  arXiv  Detail & Related papers  (2021-03-05T08:52:31Z)
• Machine Learning (In) Security: A Stream of Problems [17.471312325933244]
  We identify, detail, and discuss the main challenges in the correct application of Machine Learning techniques to cybersecurity data. We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions. We present how existing solutions may fail under certain circumstances, and propose mitigations to them.
  arXiv  Detail & Related papers  (2020-10-30T03:40:10Z)
• 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)

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.