With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

Sensitive data processing occurs more and more on machines or devices out of users control. In the Internet of Things world, for example, the security of data could be posed at risk regardless the adopted deployment is oriented on Cloud or Edge Computing . In these systems different categories of attacks—such as physical bus sniffing, cold boot, cache side-channel, buffer overflow , code-reuse, or Iago—can be realized. Software-based countermeasures have been proposed. However, the severity and complexity of these attacks require a level of security that only the hardware support can ensure. In the last years, major companies released a number of architectural extensions aiming at provide hardware-assisted security to software. In this paper, we realize a comprehensive survey of HW-assisted technological solutions produced by vendors like Intel, AMD, and ARM for both embedded edge-devices and hosting machines such as cloud servers. The different approaches are classified based on the type of attacks prevented and the enforced techniques. An analysis on their mechanisms, issues, and market adoption is provided to support investigations of researchers approaching to this field of systems security.

A comprehensive survey of hardware-assisted security: From the edge to the cloud

These days, cloud computing is one of the most promising technologies to store information and provide services online efficiently. Using this rapidly developing technology to protect computer-based systems from cyber-related attacks can bring many advantages over traditional protection schemes. The protected assets can be any computer-based systems such as cyber-physical systems (CPS), critical systems, desktop and laptop computers, mobile devices, and Internet of Things (IoT). Malicious software (malware) is any software which targets the computer-based system to launch cyber-attacks to threaten the integrity, confidentiality and availability of the data. To detect the massively growing malware attacks surface, we propose an intelligent behavior-based detection system in the cloud environment. The proposed system first creates a malware dataset on different virtual machines which identify distinctive features efficiently. Then, selected features are given to the learning-based and rule-based detection agents to separate malware from benign samples. Totally, 10,000 program samples have been analyzed to evaluate the performance of the proposed system. The proposed system can detect both known and unknown malware efficiently with high detection and accuracy rate. Besides, the proposed method results have outperformed the leading methods’ results in the literature. Our evaluation results show that the proposed algorithms along with machine learning (ML) classifiers achieve 99.8% detection rate, 0.4% false positive rate, and 99.7% accuracy. Our proposed system and algorithms may assist those who would like to develop a novel malware detection system in the cloud environment.

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Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

The Internet of Things (IoT) is rapidly changing our society to a world where every thing is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity an ...

/pdf/cyber-storms-come-from-clouds-security-of-cloud-computing-in-1jqwfocc0m.pdf

Cyber-Storms Come from Clouds : Security of Cloud Computing in the IoT Era

The European Proceedings of Social & Behavioural Sciences

In the last few years, research has been motivated to provide a categorization and classification of security concerns accompanying the growing adaptation of Infrastructure as a Service (IaaS) clouds. Studies have been motivated by the risks, threats and vulnerabilities imposed by the components within the environment and have provided general classifications of related attacks, as well as the respective detection and mitigation mechanisms. Virtual Machine Introspection (VMI) has been proven to be an effective tool for malware detection and analysis in virtualized environments. In this paper, we classify attacks in IaaS cloud that can be investigated using VMI-based mechanisms. This infers a special focus on attacks that directly involve Virtual Machines (VMs) deployed in an IaaS cloud. Our classification methodology takes into consideration the source, target, and direction of the attacks. As each actor in a cloud environment can be both source and target of attacks, the classification provides any cloud actor the necessary knowledge of the different attacks by which it can threaten or be threatened, and consequently deploy adapted VMI-based monitoring architectures. To highlight the relevance of attacks, we provide a statistical analysis of the reported vulnerabilities exploited by the classified attacks and their financial impact on actual business processes.

/pdf/classifying-malware-attacks-in-iaas-cloud-environments-4qn5h8907h.pdf

Mykolai Protsenko

Papers

Classifying malware attacks in IaaS cloud environments