B. Janet

Abstract: A Cube Index Model on multidimensional text database and effective study of Online Analytical Processing (OLAP) over such data had been experimented and found to provide good results. We had proposed a cube index model for unstructured text database derived from the text index structures. There are three kinds of hierarchies on it. They are term hierarchy and dimensional hierarchy. This paper proposes the document hierarchy. Two new operations scroll up and scroll down are discussed exclusively for the cube index. The implementation, OLAP execution and query processing on the index are studied. The performance study gives a good guarantee of the model to be used on unstructured text database.

Abstract: We have been writing about the significant contributions of several proven and promising technologies in ensuring the desired success of smart cities. However, the selection of technologies for establishing intelligent cites has to be made after a careful consideration of multiple factors. There are several technologies coming and going without contributing anything substantial for the originally visualized and articulated needs, and hence, the choice plays a vital role in shaping up and strengthening our cities for future challenges and changes. Another noteworthy point is that instead of going for a single technology, it is prudent and pertinent to embrace a cluster of technologies to reach the desired state comfortably. Technology clusters are becoming prominent these days. Especially considering the growing complexity of smart cities (being touted as the system of systems), the need for a collection of competent technologies is being felt across not only the technology-cluster choice but also the appropriate usage of it also is pivotal in achieving the target in a risk-free and relaxed manner. Thus, any smart city strategy has to clearly illuminate resilient technologies and methodologies together towards accelerating and attaining the varied goals of smart cities in this vast and vivacious planet. In this chapter, the authors discuss the immense potential and promise of the newly coined paradigm of the internet of things (IoT) in making next-generation cities that sharply elevate the features, facilities, and functionalities of our crumbling and clogging cities.

Abstract: The growing rate of malware and its complexity demands a new approach to detecting evolving malware instead of relying only on high-level features such as opcodes, API calls, control flow graphs, etc. Moreover, extracting these features is an expensive and time-consuming because it requires disassembling or code execution. This paper presents a new malware detection architecture using image analysis & machine learning, requiring significantly fewer resources and does not depend upon code disassembling or execution. The collected binary samples are systematically labeled using the AVClass tool and clustering method based on similar characteristics of them. The labeled malicious program is visualized into grayscale images to extract the local and global textural features. The local textural features are extracted using SIFT, KAZE, and ORB descriptors, and global features are extracted using GIST, Hu Moments, and HOG. A bag of visual words (BoVW) algorithm is designed to select low-dimensional features and construct a local feature map of malware grayscale images. The feature maps from different image descriptors are stacked and used to train five machine learning algorithms, namely, k-Nearest Neighbor (k-NN), Support Vector Machine(SVM), Random Forest (RF), Naive Bayes(NB), and ExtraTree classifier. Two datasets are used for evaluations — the public MalImg dataset of 9339 samples of 25 different families and 690 real-world malware of 22 families collected on honeypots in the wild. Intensive experiments are performed for image descriptors, image ratios, vocabulary size, and computational time as a mean time to detection(MTTD) to devise the best detector. The proposed method obtained test accuracy of 98.34% with stacked global features and 98.23% with stacked local features. Test accuracy of 92.75% with low false-positive rates is obtained for real-world recent malware datasets. Experiment results reveal the efficacy of the proposed method in detecting polymorphic obfuscated malware. Finally, a comparison with other similar malware detection systems is presented. • A new architecture of malware detection and classification using machine learning. • Novel use of hybrid textural features and bag of visual words. • A single image descriptor has difficulty capturing complex patterns of malware.

Abstract: Phishing is an online security attack in which the hacker aims in harvesting sensitive information like passwords, credit card information etc. from the users by making them to believe what they see is what it is. This threat has been into existence for a decade and there has been continuous developments in counter attacking this threat. However, statistical study reveals how phishing is still a big threat to today's world as the online era booms. In this paper, we look into the art of phishing and have made a practical analysis on how the state of the art anti-phishing systems fail to prevent Phishing. With the loop-holes identified in the state-of-the-art systems, we move ahead paving the roadmap for the kind of system that will counter attack this online security threat more effectively.

Abstract: The technological advancement of computer network and popularity of Internet introduces many on-line services. As the services like e-governance, banking, e-commerce and trading etc. are becoming Internet based, the timely availability of these services to the users have gained paramount importance. Denial of Service (DoS) attack is one of the common attack used by the attacker to interrupt or block availability of the services to the genuine users. Recently, slow DoS attacks are getting popular among the attackers. Generally, the analysis of slow DoS attack is mainly based on the parameters derived from the host machine. In this paper, we analyze the network traffic pattern generated by four different types of slow DoS attack which is targeted on HTTP application. Our analysis focuses on the network based parameters such as window size and delta time of the packet which can be collected from the network gateway. By monitoring and analyzing these parameters, a host machine independent early detection of slow DoS attack is derived and preventive action can be initiated from the network gateway itself.

Abstract: Clustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model[1, 2, 3J. This method is quite simple and valid and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.

Abstract: This study investigated the microwave absorption properties of core–shell composites containing; iron oxide decorated carbon nanotubes (CNTs) and silica (SiO2@Fe3O4–MWCNTs) with various thicknesses of silica shells (7, 20 and 50 nm). Transmission electron microscopy (TEM) and X-ray diffraction results confirmed the formation of these core–shell structures. Microwave absorption characterization of the samples at the ranging band under consideration (the X-band) showed increased absorption and shifting of the peaks to lower frequencies compared to the uncoated sample (Fe3O4–MWCNTs). The minimum reflection loss decreased with increasing SiO2 thickness. The minimum reflection loss of the composite with an optimized thickness of the silica shell (7 nm) exceeded −41 dB at 8.7–9 GHz.

Abstract: Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the Big Data epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ the NoSQL data storages to accommodate the high-dimensional data. Unfortunately, the existing data mining techniques which are designed for schema-oriented storages are non-applicable to the unstructured data style. Thus, the AaaS though still in its infancy, is gaining widespread attention for its ability to provide novel ways and opportunities to mine the heterogeneous data. In this paper, we discuss our AaaS tool that performs terms and topics extraction and organization from unstructured data sources such as NoSQL databases, textual contents (e.g., websites), and structured sources (e.g. SQL). The tool is built on methodologies such as tagging, filtering, association maps, and adaptable dictionary. The evaluation of the tool shows high accuracy in the mining process.

Abstract: Distributed Denial of Service attack (DDoS) is recognized to be one of the most catastrophic attacks against various digital communication entities. Software-defined networking (SDN) is an emerging technology for computer networks that uses open protocols for controlling switches and routers placed at the network edges by using specialized open programmable interfaces. In this article, a detailed study on DDoS threats prevalent in SDN is presented. First, SDN features are examined from the perspective of security, and then a discussion on SDN security features is done. Further, two viewpoints on protecting networks against DDoS attacks are presented. In the first view, SDN utilizes its abilities to secure conventional networks. In the second view, SDN may become a victim of the threat itself because of the centralized control mechanism. The main focus of this research work is on discovering critical security implications in SDN while reviewing the current ongoing research studies. By emphasizing the available state-of-the-art techniques, an extensive review of the advancement of SDN security is provided to the research and IT communities.