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In this paper, we propose a method to create the 60-dimensional feature vector for protein sequences via the general form of pseudo amino acid composition. The construction of the feature vector is based on the contents of amino acids, total distance of each amino acid from the first amino acid in the protein sequence and the distribution of 20 amino acids. The obtained cosine distance metric (also called the similarity matrix) is used to construct the phylogenetic tree by the neighbour joining method. In order to show the applicability of our approach, we tested it on three proteins: 1) ND5 protein sequences from nine species, 2) ND6 protein sequences from eight species, and 3) 50 coronavirus spike proteins. The results are in agreement with known history and the output from the multiple sequence alignment program ClustalW, which is widely used. We have also compared our phylogenetic results with six other recently proposed alignment-free methods. These comparisons show that our proposed method gives a more consistent biological relationship than the others. In addition, the time complexity is linear and space required is less as compared with other alignment-free methods that use graphical representation. It should be noted that the multiple sequence alignment method has exponential time complexity.

Web services are a type of application software, which can be remotely accessed through the Internet. Due to the proliferating growth of web services of the same functionality, the user goes into a dilemma to select suitable service for him. In this paper, we study the web service selection (WSS) problem in a sequential composition model. We formulated the WSS as a constrained optimization problem. To solve the problem, we suggest a modified artificial bee colony (mABC) algorithm, which uses a chaotic-based opposition learning method to generate a better initial population. To improve the exploration capability of the mABC, a new search equation for employed bee phase is suggested. On the other hand, to improve the exploitation capacity of the mABC, a new search strategy, inspired by differential evolution (DE), is adopted in the onlooker bee phase. We test the mABC on synthetic web service selection problem taken from QWS dataset. To assess the relative performance of the mABC, we compare it against five other state-of-the-art algorithms. The experimental results show that the mABC is better than other existing approaches in terms of response time, latency, availability, and reliability. INDEX TERMS Web service selection, artificial bee colony algorithm, chaotic map, QoS attributes.

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Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals' wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users' devices probe requests and locations without users' active cooperation. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3%.

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Internet is the most powerful medium as on date, facilitating varied services to numerous users. It has also become the environment for cyber warfare where attacks of many types (financial, ideological, revenge) are being launched. The ecommerce transactions being carried out online are of major interest to cybercriminals. The Internet needs to be protected from these attacks and an appropriate response has to be generated to handle them to reduce the impact. Network forensics is the science that deals with capture, recording, and analysis of network traffic for investigative purpose and incident response. There are many tools which assist in capturing data transferred over the networks so that an attack or the malicious intent of the intrusions may be investigated. This paper presents a generic framework for network forensic analysis by specifically identifying the steps connected only to network forensics from the already proposed models for digital investigation. Each of the phases in the framework is elucidated. A comparison of the proposed model is done with the existing models for digital investigation. Research challenges in various phases of the model are approached with specific reference to network forensics.

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