Volume 17 (2017): Issue 4 (November 2017)

Cloud computing is emerging as one of the powerful computing technologies in the field of Information Technology due to its flexibility and cost reduction. This paper provides a detailed survey on security issues of the services provided by cloud computing and solutions to mitigate them. The main objective of this paper is to empower a new researcher to figure out the concepts of cloud computing, the services provided by them, and the security issues in the services. It also provides solutions to avoid or mitigate the different security issues which occur in the services provided by cloud computing. Additionally, it provides insight into the cloud computing model proposed by the National Institute of Standards and Technology (NIST), data stages and data security basics in a multi-tenant environment. This paper explores the different security methods proposed by different researchers and analyzes them.

Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.

Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

The bridge protocol (Address Resolution Protocol) ARP, integrating Ethernet (Layer 2) and IP protocol (Layer 3) plays a vital role in TCP/IP communication since ARP packet is the first packet generated during any TCP/IP communications and they are the first traffic from the host. In the large data center, as the size of the broadcast domain (i.e., number of hosts on the network) increases consequently the broadcast traffic from the communication protocols like ARP also increases. This paper addresses the problem faced by Layer 2 protocols like insecured communication, scalability issues and VM migration issues. The proposed system addresses these issues by introducing two new types of messaging with traditional ARP and also combat the ARP Cache poisoning attacks like host impersonation, MITM, Distributed DoS by making ARP stateful. The components of the proposed methodology first start the process by decoding the packets, updates the invalid entry made by the user with Timestamp feature and messages being introduced. The system has been implemented and compared with various existing solutions.

The general contribution of this research is the implementation of new formal type of relative view, which has been added to the Black-Litterman Model (BLM) for asset management. It is well known that the BLM integrates both historical data about the assets’ returns and subjective views given by experts and investors. Such complicated model is expected to give more realistic assessment about the dynamical behavior of the stock exchanges. The BLM applies both absolute and relative views about the asset returns. The paper proves that the currently applied relative views with equal weights are equivalent to assess the risk of a virtual portfolio with these assets of the view which participate with equal weights. The paper extends this form of views, applying non-equal weights of the assets. This new formal description has been tested on a market, containing ten world known indices for a 10 years period. The calculations which have been provided give benefits to the suggested non-equal weighted form of subjective views. It gives more conservative results and decreases the portfolio risk supporting the same level of returns, provided by the average market behavior.

In present times a great number of clustering algorithms are available which group objects having similar features. But most of the datasets have data values that are categorical, which makes it difficult to implement these algorithms. The concept of genetic algorithm on intuitionistic fuzzy k-Mode method is proposed in the paper to cluster categorical data. This model is an extension of intuitionistic fuzzy k-Mode in which the notion of fitness related objective functions, crossovers, mutations and probability has been added to provide better clusters for the data objects. Also the intuitionistic parameter has been retained for the calculation of membership values of element x in a given cluster. UCI repository datasets were used for assessing efficacy of algorithms. The qualified analysis and results depict much consistent performance, where a significant improvement is achieved as compared to intuitionistic fuzzy k-Mode and simulated annealing based intuitionistic fuzzy k-mode. Genetic Algorithm based intuitionistic fuzzy k-Mode is very efficient when clustering is applied on large datasets that are categorical in nature, which proves to be very critical for data mining processes.

This paper proposes a new contour-based speech endpoint detector which combines the log-Group Delay Mean-Delta (log-GDMD) feature, an adaptive twothreshold scheme and an eight-state automaton. The adaptive thresholds scheme uses two pairs of thresholds - for the starting and for the ending points, respectively. Each pair of thresholds is calculated by using the contour characteristics in the corresponded region of the utterance. The experimental results have shown that the proposed detector demonstrates better performance compared to the Long-Term Spectral Divergence (LTSD) one in terms of endpoint accuracy. Additional fixed-text speaker verification tests with short phrases of telephone speech based on the Dynamic Time Warping (DTW) and left-to-right Hidden Markov Model (HMM) frameworks confirm the improvements of the verification rate due to the better endpoint accuracy.

As in recent years digital data transmission and image application have been increasing, maintaining secure transmission of image is of high importance. Image Encryption is implemented to achieve security on image applications. This paper exhibits a survey on various existing image encryption techniques. The paper mainly focuses on two types: Image encryption with Key(s) and Image Encryption without Key(s). In addition it also describes several properties of a good image encryption technique. The paper presents a survey of most popular algorithms and research papers that are related with different image encryption techniques.

Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

The image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.