AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments
- URL: http://arxiv.org/abs/2404.05602v3
- Date: Fri, 01 Nov 2024 20:51:17 GMT
- Title: AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments
- Authors: Mohammed Ashfaaq M. Farzaan, Mohamed Chahine Ghanem, Ayman El-Hajjar, Deepthi N. Ratnayake,
- Abstract summary: The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies.
This research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments.
The findings highlight the effectiveness of the Random Forest model, achieving an accuracy 90% for the Network Traffic and 96% for the Malware Analysis Dual Model application.
- Score: 0.0
- License:
- Abstract: The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments. This system, encompassing Network Traffic Classification, Web Intrusion Detection, and post-incident Malware Analysis (built as a Flask application), achieves seamless integration across platforms like Google Cloud and Microsoft Azure. The findings from this research highlight the effectiveness of the Random Forest model, achieving an accuracy of 90% for the Network Traffic Classifier and 96% for the Malware Analysis Dual Model application. Our research highlights the strengths of AI-powered cyber security. The Random Forest model excels at classifying cyber threats, offering an efficient and robust solution. Deep learning models significantly improve accuracy, and their resource demands can be managed using cloud-based TPUs and GPUs. Cloud environments themselves provide a perfect platform for hosting these AI/ML systems, while container technology ensures both efficiency and scalability. These findings demonstrate the contribution of the AI-led system in guaranteeing a robust and scalable cyber incident response solution in the cloud.
Related papers
- Transforming the Hybrid Cloud for Emerging AI Workloads [81.15269563290326]
This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.
The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.
This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
arXiv Detail & Related papers (2024-11-20T11:57:43Z) - 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) - Feature Selection using the concept of Peafowl Mating in IDS [2.184775414778289]
Cloud computing provides services that are Infrastructure based, Platform based and Software based.
The popularity of this technology is due to its superb performance, high level of computing ability, low cost of services, scalability, availability and flexibility.
The obtainability and openness of data in cloud environment make it vulnerable to the world of cyber-attacks.
To detect the attacks Intrusion Detection System is used, that can identify the attacks and ensure information security.
arXiv Detail & Related papers (2024-02-03T06:04:49Z) - Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection
in Power Distribution Grids [0.0]
This paper proposes a physics-informed convolutional autoencoder (PIConvAE) to detect stealthy cyber-attacks in power distribution grids.
The proposed model integrates the physical principles into the loss function of the neural network by applying Kirchhoff's law.
arXiv Detail & Related papers (2023-12-08T00:05:13Z) - When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures.
We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN.
Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
arXiv Detail & Related papers (2023-06-09T14:33:26Z) - Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for
Enhanced Deep Learning Performance and Efficiency [0.0]
In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications.
This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learning performance and efficiency.
arXiv Detail & Related papers (2023-04-26T15:38:00Z) - 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) - Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the
Age of AI-NIDS [70.60975663021952]
We study blackbox adversarial attacks on network classifiers.
We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions.
We show that a continual learning approach is required to study attacker-defender dynamics.
arXiv Detail & Related papers (2021-11-23T23:42:16Z) - Automating Privilege Escalation with Deep Reinforcement Learning [71.87228372303453]
In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents.
We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation.
Our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
arXiv Detail & Related papers (2021-10-04T12:20:46Z) - Artificial Intelligence and Machine Learning in 5G Network Security:
Opportunities, advantages, and future research trends [5.431496585727341]
5G networks' primary selling point has been higher data rates and speed.
As 5G networks' primary selling point has been higher data rates and speed, it will be difficult to tackle wide range of threats.
This article presents AI and ML driven applications for 5G network security.
arXiv Detail & Related papers (2020-07-09T01:02:13Z)
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.