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Providing Response to Security Incidents in the Cloud Computing with Autonomic Systems and Big Data

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This article provides a real-time intrusion response system in order to reduce the consequences of the attacks in the Cloud Computing. Our work proposes an autonomic intrusion response technique that uses a utility function to determine the best response to the attack providing self-healing properties to the environment. To achieve this goal, we propose the Intrusion Response Autonomic System (IRAS), which is an autonomic intrusion response system, using Big Data techniques for data analysis. I. INTRODUCTION As a complement to the work presented in [1], the object of this article is to present the results and details of its implementation. Because of their distributed nature, cloud computing environments are a great target for intruders interested in exploring possible vulnerabilities in their services and consequently using the abundant resources maliciously. The growing number of attacks and vulnerability exploitation techniques requires preventative measures by system administrators. In this context, the need for a highly effective and rapid reactive security system gains importance. These measures are getting more complex with the growth of data heterogeneity and the increasing complexity of the attacks. In addition, slow reaction time from human agents and the huge amount of data and information generated, makes the decision making process an arduous task. In response to this, there is an increase in the usage of Intrusion Detection Systems (IDS) [2], as a way to identify attack patterns, malicious actions and unauthorized access to an environment [3]. The need for IDS is growing due to limitations in Intrusion Preventing Systems (IPS)-which focus on alerting administrators when a vulnerability is detected, connectivity and threat evolution, as well as the financial appeal of cybercrime [4]. Despite their growing importance, currently available IDS solutions have limited response mechanisms. While the research focus is on better intrusion detection techniques, response and effective threat reaction are still mostly manual and rely on human agents to take effect [5]. Recently, some intrusion detection tools have begun providing limited sets of automated responses, but with the growing complexity of intrusions, the need for more effective response system strategies has increased. Due to implementation limitations , research on intrusion detection techniques advance faster than intrusion response systems [3].
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... Recently, there have been some efforts to address intrusion response in cloud environments [9], [10], [11]. However, those studies do not investigate attack propagation patterns, and do not consider interactions among compromised VMs. ...
... There are some attack response methods that are presented for cloud infrastructure. Vieira et al. [10] proposed intrusion response autonomic system (IRAS) that uses a utility function to suggest the best response in order to reduce the consequences of the attacks in the cloud. Raju and Geethakumari [11] proposed a framework for detecting the attacker. ...
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... The solution employs a knowledge based approach to detect known attacks by comparing attack signatures to suspicious actions [27], [28]. Figure 2 depicts the operation. ...
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We introduce a method for Intrusion Detection based on the classification, understanding and prediction of behavioural deviance and potential threats, issuing recommendations, and acting to address eminent issues. Our work seeks a practical solutions to automate the process of identification and response to Cybersecurity threats in hybrid Distributed Computing environments through the analysis of large datasets generated during operations. We are motivated by the growth in utilisation of Cloud Computing and Edge Computing as the technology for business and social solutions. The technology mix and complex operation render these environments target to attacks like hijacking, man-in-the-middle, denial of service, phishing, and others. The Autonomous Intrusion Response System implements innovative models of data analysis and context-aware recommendation systems to respond to attacks and self-healing. We introduce a proof-of-concept implementation and evaluate against datasets from experimentation scenarios based on public and private clouds. The results present significant improvement in response effectiveness and potential to scale to large environments.
... The solution employs a knowledge based approach to detect known attacks by comparing attack signatures to suspicious actions [27], [28]. Figure 2 depicts the operation. ...
Preprint
Full-text available
We introduce a method for Intrusion Detection based on the classification, understanding and prediction of behavioural deviance and potential threats, issuing recommendations, and acting to address eminent issues. Our work seeks a practical solutions to automate the process of identification and response to Cybersecurity threats in hybrid Distributed Computing environments through the analysis of large datasets generated during operations. We are motivated by the growth in utilisation of Cloud Computing and Edge Computing as the technology for business and social solutions. The technology mix and complex operation render these environments target to attacks like hijacking, man-in-the-middle, denial of service, phishing, and others. The Autonomous Intrusion Response System implements innovative models of data analysis and context-aware recommendation systems to respond to attacks and self-healing. We introduce a proof-of-concept implementation and evaluate against datasets from experimentation scenarios based on public and private clouds. The results present significant improvement in response effectiveness and potential to scale to large environments.
... This approach can only guarantee local optimal response action selection because the look-ahead is limited to a single action and therefore it does not take full advantage of a stateful model. The work has been then extended in [25] with the concrete implementation of the approach, but the MDP model has been replaced with a stateless utility function. ...
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