A Review of Machine Learning-based Security in Cloud Computing
- URL: http://arxiv.org/abs/2309.04911v1
- Date: Sun, 10 Sep 2023 01:52:23 GMT
- Title: A Review of Machine Learning-based Security in Cloud Computing
- Authors: Aptin Babaei, Parham M. Kebria, Mohsen Moradi Dalvand, and Saeid
Nahavandi
- Abstract summary: Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure.
With the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality.
Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues.
- Score: 5.384804060261833
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cloud Computing (CC) is revolutionizing the way IT resources are delivered to
users, allowing them to access and manage their systems with increased
cost-effectiveness and simplified infrastructure. However, with the growth of
CC comes a host of security risks, including threats to availability,
integrity, and confidentiality. To address these challenges, Machine Learning
(ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the
need for human intervention in identifying and resolving security issues. With
the ability to analyze vast amounts of data, and make high-accuracy
predictions, ML can transform the way CSPs approach security. In this paper, we
will explore some of the most recent research in the field of ML-based security
in Cloud Computing. We will examine the features and effectiveness of a range
of ML algorithms, highlighting their unique strengths and potential
limitations. Our goal is to provide a comprehensive overview of the current
state of ML in cloud security and to shed light on the exciting possibilities
that this emerging field has to offer.
Related papers
- Generative AI and Large Language Models for Cyber Security: All Insights You Need [0.06597195879147556]
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs)
We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection.
We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA.
arXiv Detail & Related papers (2024-05-21T13:02:27Z) - Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security)
We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection.
Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
arXiv Detail & Related papers (2024-05-08T02:09:17Z) - Highlighting the Safety Concerns of Deploying LLMs/VLMs in Robotics [54.57914943017522]
We highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications.
arXiv Detail & Related papers (2024-02-15T22:01:45Z) - 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) - Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review [51.31851488650698]
Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid.
adversarial distortion injected into the power signal will greatly affect the system's normal control and operation.
It is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems.
arXiv Detail & Related papers (2023-08-30T03:29:26Z) - Machine Learning with Confidential Computing: A Systematization of Knowledge [9.632031075287047]
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces.
As a mature system-oriented approach, Confidential Computing has been utilized in both academia and industry to mitigate privacy and security issues in various ML scenarios.
We systematize the prior work on Confidential Computing-assisted ML techniques that provide i) confidentiality guarantees and ii) integrity assurances, and discuss their advanced features and drawbacks.
arXiv Detail & Related papers (2022-08-22T08:23:53Z) - Practical Machine Learning Safety: A Survey and Primer [81.73857913779534]
Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities.
New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks.
Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects.
arXiv Detail & Related papers (2021-06-09T05:56:42Z) - Robust Machine Learning Systems: Challenges, Current Trends,
Perspectives, and the Road Ahead [24.60052335548398]
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT)
They are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy.
This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities.
arXiv Detail & Related papers (2021-01-04T20:06:56Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - A Privacy-Preserving Distributed Architecture for
Deep-Learning-as-a-Service [68.84245063902908]
This paper introduces a novel distributed architecture for deep-learning-as-a-service.
It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
arXiv Detail & Related papers (2020-03-30T15:12:03Z)
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.