Cooperative Trust Framework for Cloud Computing Based on Mobile Agents

Cloud computing opens doors to the multiple, unlimited venues from elastic computing to on demand provisioning to dynamic storage, reduce the potential costs through optimized and efficient computing. To provide secure and reliable services in cloud computing environment is an important issue. One of the security issues is how to reduce the impact of for any type of intrusion in this environment. To counter these kinds of attacks, a framework of cooperative Hybrid intrusion detection system (Hy-IDS) and Mobile Agents is proposed. This framework allows protection against the intrusion attacks. Our Hybrid IDS is based on two types of IDS, the first for the detection of attacks at the level of virtual machines (VMs), the second for the network attack detection and Mobile Agents. Then, this framework unfolds in three phases: the first, detection intrusion in a virtual environment using mobile agents for collected malicious data. The second, generating new signatures from malicious data, which were collected in the first phase. The third, dynamic deployment of updates between clusters in a cloud computing, using the newest signatures previously created. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively. In this paper, we develop a collaborative approach based on Hy-IDS and Mobile Agents in Cloud Environment, to define a dynamic context which enables the detection of new attacks, with much detail as possible

Security and Privacy Issues in Cloud Computing

Cloud computing transforming the way of information technology (IT) for consuming and managing, promising improving cost efficiencies, accelerate innovations, faster time-to-market and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew ex-ponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment

State of the Art Intrusion Detection System for Cloud Computing

The term Cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today Cloud computing not only provides innovative improvements in resource utilisation but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies  are blooming rapidly. From the perspective of security, Cloud computing also introduces concerns about data protection and intrusion detection mechanism. This paper surveys, explores and informs researchers about the latest developed Cloud Intrusion Detection Systems by providing a comprehensive taxonomy and investigating possible solutions to detect intrusions in cloud computing systems. As a result, we provide a comprehensive review of Cloud Intrusion Detection System research, while highlighting the specific properties of Cloud Intrusion Detection System. We also present taxonomy on the key issues in Cloud Intrusion Detection System area and discuss the different approaches taken to solve the issues. We conclude the paper with a critical analysis of challenges that have not fully solved

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection