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Mobile devices that run Android operating system are widely used. The applications running in Android mobiles can have malicious permissions due to malware. In other words, Android applications might spread malware which can sabotage valuable data. Therefore it is essential to have mechanism to classify malware and benign mobile applications running in Android phones. Since Android mobile applications run in the confines of mobile devices and associated servers, it is very challenging task to detect Android malware. Many solutions came into existence to detect malware applications. Of late Abawajy et al. proposed a technique known as Iterative Classifier Fusion System (ICFS) which employs classifiers iteratively with fusion to generate a final classifier for effective detection of malware. They combined NB tree classifier, Multilayer perception and Lib SVM with polynomial kernel to achieve this. However, the system does not focus on reduction or pruning of Android application permissions so as to build a classifier that reduces time and space complexity. In the proposed system, a methodology is proposed that focuses on reduction or pruning of android application permissions and ranking them in order to build a classifier that reduces time and space complexity. The classifier modelled with best ranked permissions can be representative of all permissions as least significant permissions are pruned to reduce search space. This paper built a prototype application to demonstrate proof of the concept. The experimental results revealed that the proposed system performs better in improving detection accuracy besides precision and recall measures.

Malware, malware detection technique, pruning, ranking

[1] P. Faruki, A. Bharmal, V. Laxmi, V. Ganmoor, M. S. Gaur, M. Conti, and M. Rajarajan, “Android security: A survey of issues, malware penetration, and defences,” IEEE Communications Surveys and Tutorials, vol. 17, pp. 998–1022, 2015.
[2] Y. Zhou and X. Jiang, “Dissecting Android malware: Characterization and evolution,” in Proceedings of the 33rd IEEE Symposium on Security and Privacy, San Francisco, CA, pp. 95–109, 2012.
[3] J. Walls and K.-K. R. Choo, “A review of free cloud-based antimalware apps for Android,” in Proceedings of 2015 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Trust Com 2015, vol. 1, pp. 1053–1058, 2015.
[4] S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, and M. Conti, “Employing program semantics for malware detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2591–2604, 2015.
[5] P. Faruki, S. Bhandari, V. Laxmi, M. Gaur, and M. Conti, “Droid analyst : Synergic app framework for static and dynamic app analysis,” Studies in Computational Intelligence, vol. 621, pp. 519–552, 2015.
[6] L. Sinha, S. Bhandari, P. Faruki, M. S. Gaur, V. Laxmi, and M. Conti, “Flow Mine : Android app analysis via data flow,” in Proceeding of the 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016, pp. 435–441, 2016.
[7] J. Abawajy, M. Chowdhury, and A. Kelarev, ”Hybrid Consensus Pruning of Ensemble Classifiers for Big Data Malware Detection,” IEEE Transaction on Cloud Com put 3(2):111, 2017.
[8] S. Sheen, R. Anitha, and V. Natarajan, “Android based malware detection using a multi feature collaborative decision fusion approach,” Neuro computing, vol. 151, pp. 905–912, 2015.
[9] S. Naval, V. Laxmi, M. S. Gaur, S. Raja, M. Rajarajan, and M. Conti, “Environment-reactive malware behaviour: Detection and categorization,” in Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance, ser. LNCS, vol. 8872, pp. 167–182, 2015.
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[11] K. Zhao, D. Zhang, X. Su, and W. Li, “Fest: A feature extraction and selection tool for Android malware detection,” in 20th IEEE Symposium on Computers and Communication, ISCC 2015, pp. 714–720, 2015
[12] J. Abawajy, A. Kelarev “Iterative Classifier Fusion System for the Detection of Android Malware”. IEEE Transactions on Big Data, Vol. 5, No. 4, p1-12, 2017.

Ramisetti Uma Maheswari, R Raja Sekhar, "Pruning and Ranking Based Classifier for Efficient Detection of Android Malware," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.201-205, 2018.

Mobile Ad hoc Network (MANET) is outstanding for its restricted transmission scope of remote system interface. Thus, multiple hops (multi-hops) might be required for trading the data starting with one node then onto the next over the system with no base stations or switches. In MANETs, as there is no chain of command among nodes, each node is in charge of sending packets to its neighboring nodes. Because of serious asset requirements like memory, registering power, vitality, data transfer capacity and time, a few nodes may not partake in sending the packets for sparing its assets. The nearness of selfish behaviour among nodes may prompt system apportioning and has a noteworthy negative effect in throughput and the system activity. To maintain a strategic distance from such circumstances selfish node deduction is imperative. The proposed framework introduced an Improved Exponential Reliability Coefficient based Reputation Mechanism (IERCRM) which disengages the selfish nodes from the steering way in view of Enhanced Exponential Reliability Coefficient (EExRC). This unwavering quality coefficient controlled through exponential disappointment rate in light of moving normal strategy features the latest past conduct of the versatile nodes for measuring its validity. From the reenactment comes about, it is apparent that, the proposed IERCRM approach outflanks the current Exponential Reliability Coefficient based Reputation Mechanism in terms of performance evaluation metrics such as end to end delay, throughput and detection rate.

Manet, Enhanced Exponential, Reliability, Coefficient, selfishnode and Priority Factor

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[11] Informant: Detecting Sybils Using Incen-tives N. Boris Margolin and Brian N. Levine Department of Computer Science, Univ. of Massachusetts, Amherst, MA, USA {margolin,brian}@cs.umass.edu.
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Daniel Nesa Kumar C, Saravanan V, "Improved Exponential Reliability Coefficient Based Reputation Mechanism for Isolating Selfish Nodes in Mobile Ad hoc Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.206-212, 2018.

Data leakage is big security threat to organization, when third party agent carried out data leakage. Perturbation method use for the detecting and preventing data leakage and also for assessing data third party agent. Firstly, watermarking techniques used for data leakage but in that techniques modification to original data take place. To overcome this disadvantage data allocation strategies is used. It improve feasibility of finding guilt agent. Data based on sample request and explicit request using allocation strategies allocate by the distributor to detect the agent. Fake object are given with original data. If one or more fake object is leaked, then distributor detect the leakage by the agent was guilty. So, perturbation is efficient technique handle data leakage and also make less sensitive to data before handle to agent.

Agent, Distributor, Perturbation, Detection and Fake Object

[1] XiaokuiShu, Danfeng Yao and Elisa Bertino, “Privacy-Preserving Detection of Sensitive Data Exposure”, IEEE Transactions on Information Forensics and Security, 1092-1103.
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Shubhangi G. Dhawase, Bhagyashri J. Chaudhari, Neha S. Kolambe, Poonam S. Masare, "Data Leakage Detection and Prevention of Confidential Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.213-218, 2018.

Document stream is the stream where documents are flows continuously. By monitoring these documents it is possible to have different applications of the real world like demand presentation, contextual advertisements, filtering of news updates, and general filtering of information to meet the needs of users. User preferences are used to process the top-k monitoring of documents streams continuously. However, it is a tedious and challenging task to fulfil the aspirations of various users and their preferences. In the literature many solutions are found. However an adaptive approach is essential to achieve better results. In this paper we proposed a framework and implemented to have continuous monitoring and approximation of document streams to Top-k queries of different users. Thus the proposed system yields more utility to end users than existing system. Top-k queries instead of preferences can provide the intent of users more clearly. Thus the filtered documents can reveal the user intention in making such queries. An algorithm named Adaptive Identifier Ordering (AIO) is implemented to achieve this. AIO adapts to the runtime dynamics of streaming besides using top-k queries to reports users with most appropriate documents. We build a prototype application to demonstrate proof of the concept.

Document streams, top-k queries, continuous monitoring, adaptive identifier ordering

B. Mounika, R. Raja Sekher, "Approximate Top-k Queries Monitoring on Document Streams," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.219-324, 2018.

Wireless sensor network consists of low powered battery operated tiny sensor nodes that are designed to work independently. Coverage is a prominent issue in WSNs that affects the quality of service. Coverage holes can appear anywhere in the monitored region because of several reasons like energy depletion, link failure etc. In this paper, a decentralized, node based, localized coverage hole detection algorithm is proposed. It works in two phases. In the first phase, each node identifies the critical boundary points with its neighbors. Thereafter in second phase, all critical points will be grouped together to form a hole. Simulation results show that our algorithm works better than existing detection algorithm.

Wireless Sensor Networks, Critical Boundary Points, Coverage Holes, Detection of Coverage Holes

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[3] K. Shi, H. Chen, and Y. Lin, "Probabilistic coverage based sensor scheduling for target tracking sensor networks", Information sciences, vol. 292, pp. 95-110, 2015.
[4] B. Wang, H. B. Lim, and D. Ma, "A coverage-aware clustering protocol for wireless sensor networks", Computer Networks, vol. 56, no. 5, pp. 1599-1611, 2012.
[5] X. Gu, J. Yu, D. Yu, G. Wang, and Y. Lv, "ECDC: An energy and coverage-aware distributed clustering protocol for wireless sensor networks", Computers & Electrical Engineering, vol. 40, no. 2, pp. 384-398, 2014.
[6] J. A. Torkestani, "An adaptive energy-efficient area coverage algorithm for wireless sensor networks", Ad hoc networks, vol. 11, no. 6, pp. 1655-1666, 2013.
[7] S. Misra, M. P. Kumar, and M. S. Obaidat, "Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks", Computer Communications, vol. 34, no. 12, pp. 1484-1496, 2011.
[8] X. Di, "A novel coverage-preserving clustering algorithm for wireless sensor networks", Physics Procedia, vol. 33, pp. 1054-1059, 2012.
[9] H.-C. Ma, P. K. Sahoo, and Y.-W. Chen, "Computational geometry based distributed coverage hole detection protocol for the wireless sensor networks", Journal of network and computer applications, vol. 34, no. 5, pp. 1743-1756, 2011.
[10] F. Yan, P. Martins, and L. Decreusefond, "Connectivity-based distributed coverage hole detection in wireless sensor networks," in Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, 2011, pp. 1-6: IEEE.
[11] S. Babaie and S. S. Pirahesh, "Hole detection for increasing coverage in wireless sensor network using triangular structure", arXiv preprint arXiv:1203.3772, 2012.
[12] W. Li and W. Zhang, "Coverage hole and boundary nodes detection in wireless sensor networks", Journal of network and computer applications, vol. 48, pp. 35-43, 2015.
[13] F. Yan, A. Vergne, P. Martins, and L. Decreusefond, "Homology-based distributed coverage hole detection in wireless sensor networks", IEEE/ACM Transactions on Networking, vol. 23, no. 6, pp. 1705-1718, 2015.
[14] P. Kumar Sahoo, M.-J. Chiang, and S.-L. Wu, "An efficient distributed coverage hole detection protocol for wireless sensor networks", Sensors, vol. 16, no. 3, p. 386, 2016.
[15] L. Aliouane and M. Benchaïba, "Efficient boundary detection of coverage hole in WSNs," in Networks, Computers and Communications (ISNCC), 2016 International Symposium on, 2016, pp. 1-6: IEEE.
[16] P. Antil, A. Malik, and S. Kumar, "Neighbor Adjacency based Hole Detection Protocol for Wireless Sensor Networks", Procedia Computer Science, vol. 79, pp. 866-874, 2016.
[17] Z. Kang, H. Yu, and Q. Xiong, "Detection and Recovery of Coverage Holes in Wireless Sensor Networks", JNW, vol. 8, no. 4, pp. 822-828, 2013.

Manoj Verma, Sanjay Sharma, "A novel approach for detection of coverage holes in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.225-232, 2018.

Clustering is the process for grouping of similar document into a single cluster and dissimilar documents in other clusters. Document clustering is the process of grouping similar text documents in a single cluster. K-means clustering algorithm is a center predictable approach which selects initial centers randomly. In this paper, improved k-means clustering algorithm is used for text documents which predicts centers manually. Standard k-means uses cosine similarity but improved k-means uses Euclidean similarity measures for grouping similar documents in a single cluster. According to experimental results, accuracy of improved k-means is high as compared to existing k-means algorithm. Performance of proposed algorithm is measured in terms of F-measure, Precision, time and recall.

Clustering, Document clustering, Tf-Idf, K-means, Euclidean similarity

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J. Verma, N. Verma, "Optimizing Document Clustering for Dimension Reduction using improved k-means," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.233-238, 2018.

Mobile Adhoc Networks are vulnerable to attacks due to its dynamic topology. All attacks, blackhole, dropping attack and flooding are analyzed and implemented against Optimized Link State Routing Protocol on Network Simulator-3. The results clearly show how attacks could rigorously affect communication and, the need for security solutions for such highly dynamic networks.

Mobile Adhoc Networks, Optimized Link State Routing Protocol, Attacks, Blackhole, Dropping , Flooding

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[10] R. Kumari, P. Nand , “ Performance analysis for MANETs using certain realistic mobility models:NS-2”,International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE),Vol.6, Issue.1 , pp.70-77, Feb-2018.
[11] Leena Pal, Pradeep Sharma, Netram Kaurav and Shivlal Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4, 2013.
Shivlal Mewada, Umesh Kumar Singh and Pradeep Sharma, "A Novel Security Based Model for Wireless Mesh Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.11-15, 2013.

Himani Bali, Naveen Hemrajani, "Analysis of Attacks against Optimized Link State Routing Protocol -based Mobile Ad Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.239-242, 2018.

Semantic Web is extending web2.0 to web3.0 with an idea of incorporating intelligence or meaning to the existing web. Relational databases have been playing a crucial role in software development since many years. Also rapidly developing semantic web is based on mapping and compatibility of the existing data on the web which may be either in relational or non-relational form. RDF & Web Ontology are two major representations in semantic web, and SPARQL is a query language, used to query data from different semantic web resources like RDF/OWL/LOD (Linked Open Data). SPARQL processing and execution is playing a crucial role in mapping data from different sources. In this paper, first, a brief literature survey is being presented and discussed, focusing on SPARQL usage in mapping. Second, it focuses on various concerns of SPARQL query processing and execution in different domains along with SQL conversions and illustrations. Third, it presents analysis of various approaches and tools for the migration of data into semantic web from other data sources supported by a proposed model for information processing.

SPARQL, RDF, OWL, LOD, Hadoop, Relational Databases (RDB), D2RQ, Twinkle, Jena fuseki, Jena ARQ, DBpedia

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R. Gupta, S.K. Malik, "A Model for Mapping Semantic Web Data with Heterogeneous Data Sources Using SPARQL," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.243-254, 2018.

Cloud computing has increased awesome consideration from industry yet there are numerous issues that are in their primitive stage which is hampering the development of Cloud. One of these issues is security of information put away in the servers of data centres of Cloud specialist organizations. Numerous plans have been produced till date for guaranteeing security of information in Distributed Systems. Many strategies have been contemplated; dissected and new technique has been proposed which infix the parameters of security like confidentiality of data, recovery of data and uprightness of information to such an extent that it guarantees security of information put away in the servers of Cloud frameworks. This paper examines the imperative research and troubles that presents data breach in Cloud computing and gives best practices to specialist organizations and moreover attempts intend to impact cloud servers to upgrade their fundamental problems in this genuine monetary situation.

Cloud Computing, Data Breach, Distributes System, Data Security

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Savrabh Kumar Sharma, Rajneesh Singh, "Issues and Challenges In Reducing Data Breaches in Cloud Architecture," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.255-258, 2018.

Web is a global space for information where the data is largely present in unstructured or semi-structured form. Semantic Web is an extension of the present web scenario, envisioned to achieve a better collaboration between humans and machines by allowing the computer systems to understand the meaning of the user’s data and structuring the information on the web. Achieving this idea of a smart web includes realizing its programming and mathematical concepts as a significant aspect. Semantic web programming entails the representation and development of knowledge concepts and integrating them with applications. Algebra, combinatorics (graph theory), logic and set theory are the pure mathematics’ concepts that are majorly used by semantic web that forms its foundation and aids it in improving the similarity search, inferencing capabilities etc. In this paper, significant mathematical and programming concepts of semantic web have been explored, revisited, discussed and presented which may be a useful resource for semantic web researchers.

Semantic Web, RDF, Ontology, Programming, Mathematical Concepts

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N. Malik, S.K. Malik, "Significant Programming and Mathematical Concepts of Semantic Web," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.259-265, 2018.