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- International Journal of Innovative Research in Information Security is an International peer-reviewed research publishing journal with a laser-beam focal aim of promoting and publishing original high quality research dealing with theoretical and scientific aspects in all disciplines of Information Technology. It is a monthly published online international scientific research journal focusing on issues in information technology research.
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- IRJCS:: International Research Journal of Computer Science, Volume IV (Issue XII): 01-06 (December 2017)1. S. Berchtold, C Bohm, and H. Kriegel. The Pyramid-Technique: Towards Breaking the Curse of Dimensionality. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pages 142–153, Seattle, Washington, 2010, 98. 185 2. Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. The SR-tree : An index structure for high-dimensional data. In Proceedings of 22th International Conference on Very Large Data Bases, VLDB’12, pages 28–39, Bombay, India, 2012. 3. N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger. The SR-tree: an Efficient and Robust Access Method for Points and Rectangles. In Proceedings of ACM-SIGMOD International Conference on Management of Data, pages 322–331, Atlantic City, NJ, May 2011. 4. K. Chakrabarti and S. Mehrotra. The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces. In Proceedings of the 16th International Conference on Data Engineering, pages 440–447, San Diego, CA, February 2012. 5. Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. Cure: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD conference on Management of Data, pages 73–84, Seattle, WA, 2011. 6. R. Kurniawati, J. S. Jin, and J. A. Shepherd. The SS+-tree: An improved index structure for similarity searches in a high-dimensional feature space. In Proceedings of SPIE Storage and Retrieval for Image and Video Databases, pages 13–24, February 2012. 7. N. Katayama and S. Satoh. The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pages 369–380, Tucson, Arizona, 2013. 8. J.T. Robinson. The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 10–18, Ann Arbor, MI, April 2013. 9. D.A. White and R. Jain. Similarity Indexing with the SS-tree. In Proceedings of the 12th Intl. Conf. on Data Engineering, pages 516–523, New Orleans, Louisiana, February 2014. 10. D. Yu, S. Chatterjee, G. Sheikholeslami, and A. Zhang. Efficiently detecting arbitrary shaped clusters in very large datasets with high dimensions. Technical Report 98-8, State University of New York at Buffalo, Department of Computer Science and Engineering, November 2013. 11. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pages 103–114, Montreal, Canada, 2012..
- IJIRIS:: International Journal of Innovative Research Journal in Information Security, Volume IV (Issue XII): 01-07 (December 2017)1 Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. “Bayesian CART Model Search.” Journal of the American Statistical Association, Vol. 93(443), pp 935–948, September 1998. 2 Sujata Garera, Niels Provos, Monica Chew, and Aviel D. Rubin. “A framework for detection and measurement of phishing attacks.” In Proceedings of the 2007 ACM workshop on Recurring malicious code - WORM ’07, page 1, 2007. 3 Abhishek Gattani, AnHai Doan, Digvijay S. Lamba, NikeshGarera, Mitul Tiwari, Xiaoyong Chai, Sanjib Das, Sri Subramaniam, AnandRajaraman, and VenkyHarinarayan. “Entity extraction, linking, classifica- tion, and tagging for social media.” Proceedings of the VLDB Endowment, Vol. 6(11), pp 1126–1137, August 2013. 4 David D. Lewis. Naive (Bayes) at forty: The independence assumption in information retrieval. pages 4–15. 1998. 5 Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. “Learning to detect malicious URLs.” ACM Transactions on Intelligent Systems and Technology, Vol. 2(3), pp 1–24, April 2011. 6 FadiThabtah Maher Aburrous, M.A.Hossain, KeshavDahal. “Intelligent phishing detection system for e-banking using fuzzy data mining.” Expert Systems with Applications, Vol. 37(12), pp 7913–7921, Dec 2010. 7 AnkushMeshram and Christian Haas. “Anomaly Detection in Industrial. Networks using Machine Learning: A Roadmap.” In Machine Learning for Cyber Physical Systems, pages 65–72. Springer Berlin Heidelberg, Berlin, Heidelberg, 2017. 8 Xuequn Wang Nik Thompson,Tanya Jane McGill. “Security begins at home: Determinants of home computer and mobile device security behavior.” Computers & Security, Vol. 70, pp 376–391, Sep 2017. 9 Dan Steinberg and Phillip Colla. “CART: Classification and Regression Trees.” The Top Ten Algorithms in Data Mining, pp 179–201, 2009. 10 D. Teal. “Information security techniques including detection, interdiction and/or mitigation of memory injection attacks,” Google patents. Oct 2013. 11 Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. “Design and Evaluation of a Real-Time URL Spam Filtering Service.” In 2011 IEEE Symposium on Security and Privacy, pp 447–462. May 2011. 12 Sean Whalen, Nathaniel Boggs, and Salvatore J. Stolfo. “Model Aggregation for Distributed Content Anomaly Detection.” In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec ’14, pp 61–71, New York, USA, 2014. ACM Press. 13 Ying Yang and Geoffrey I. Webb. “Discretization for Naive-Bayes learning: managing a discretization bias and variance.” Machine Learning, Vol. 74(1), pp 39–74, Jan 2009..
- IJIRIS:: International Journal of Innovative Research in Information Security, Volume VI (Issue VI): 117-128 (November 2019)1. Symantec, 2019 Internet Security Threat Report, Vol. 24, 2019. 2. Akamai, 2019 State of the Internet / Security: Media Under Assault, 2019. https://www.akamai.com 3. J. Xia, S. Vangala, J. Wu, L. Gao, and K. Kwiat, “Effective Worm Detection for Various Scan Technique,” Journal of Computer Security, vol.14, no.4, pp.359–387, 2006. 4. W. Yu, X. Wang, X. Fu, D. Xuan, and W. Zhao, “An Invisible Localization Attack to Internet Threat Monitors,” IEEE Trans. Parallel and Distributed Systems, vol.20, no.11, pp.1611–1625, 2009. 5. M. Narita, K. Ogura, B.B. Bista, and T. Takata, “Evaluating a Dynamic Internet Threat Monitoring Method for Preventing PN Code-Based Localization Attack,” Proc. 17th International Conference on Network-Based Information Systems (NBiS 2014), 2014. 6. M. Narita, B.B. Bista, and T. Takata, “A Practical Study on Noise-Tolerant PN Code-Based Localisation Attacks to Internet Threat Monitors,” Int. J. Space-Based and Situated Computing, vol.3, no.4, pp.215–226, December 2013. 7. W. Yu, S. Wei, G. Ma, X. Fu, and N. Zhang, “On Effective Localization Attacks Against Internet Threat Monitors,” Proc. 2013 IEEE International Conference on Communications (ICC), pp.2011–2015, 2013. 8. UCSD Network Telescope. https://www.caida.org/projects/network telescope/ 9. Guillot, R. Fontugne, P. Winter, P. Me´rindol, A. King, A. Dainotti, and C. Pelsser, “Chocolatine: Outage Detection for Internet Background Radiation,” 10. Proc. Network Traffic Measurement and Analysis Conference (TMA), June 2019. DShield. http://www.dshield.org/ 11. M. Eto, D. Inoue, J. Song, J. Nakazato, K. Ohtaka, and K. Nakao, “nicter: A Large-Scale Network Incident Analysis System: Case Studies for Understanding Threat Landscape,” Proc. 1st Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, pp.37–45, 2011. 12. D. Inoue, M. Eto, K. Suzuki, M. Suzuki, and K. Nakao, “DAEDALUS-VIZ: Novel Real-Time 3D Visualization for Darknet Monitoring-Based Alert System,” Proc. 9th International Symposium on Visualization for Cyber Security, pp.72–79, October 2012. 13. H. Kanehara, Y. Murakami, J. Shimamura, T. Takahashi, D. Inoue and N. Murata, “Real-time botnet detection using nonnegative tucker decomposition,” 14. Proc. the 34th ACM/SIGAPP Symposium on Applied Computing, pp.1337–1344, April 2019. 15. X. Fan, C. Li, and X. Dong, “ A Real-Time Network Security Visualization System Based on Incremental Learning (ChinaVis 2018),” J. Visualization, pp.1–15, October 2018. 16. Y. Shinoda, K. Ikai, and M. Itoh, “Vulnerabilities of Passive Internet Threat Monitors,” Proc. 14th USENIX Security Symposium, pp.209–224, 2005. 17. J. Bethencourt, J. Franklin, and M. Vernon, “Mapping Internet Sensors with Probe Response Attacks,” Proc. 14th USENIX Security Symposium, pp.193– 208, 2005. 18. S. Wei, D. Shen, L. Ge, W. Yu, E.P. Blasch, K.D. Pham, and G. Chen, “Secured Network Sensor-Based Defense System,” Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 2015. 19. W. Yu, N. Zhang, X. Fu, R. Bettati, and W. Zhao, “Localization Attacks to Internet Threat Monitors: Modeling and Countermeasures,” IEEE Trans. Computers, vol.59, no.12, pp.1655–1668, 2010. 20. ENISA, “Proactive Detection of Network Security Incidents, Report,” https://www.enisa.europa.eu/2011. 21. M. Kamizono et al., “anti Malware engineering WorkShop ~MWS Datasets 2015~,” MWS2015, 2015..
- IJIRIS:: International Journal of Innovative Research in Information Security, Volume V (Issue VIII): 471-474 (September 2018)1. K. Wu and C. Wang, “Steganography using reversible texture synthesis” IEEE Transactions on Image Processing Vol.24 pp 130-139,January 2015 2. Shreyank N Gowda, “An Advanced Diffie-Hellman Approach to Image Steganography ” IEEE Transactions on advance network and telecommunication system Vol.19 pp 1-4,june 2016 3. Sherin Sugathan, “An Improved LSB Embedding Technique for Image Steganography ” International conference on applied and theoretical computing and communication technology Vol.33 pp 609-612,2016 4. S. Singh and V. K. Attri Dual Layer Security of data using LSB Image Steganography Method and AES Encryption Algorithm International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 5 , pp. 259-266 ,2015 5. Utsav Sheth and Shiva Saxena, “Image Steganography Using AES Encryption and Least Significant Nibble ” International conference on communication and signal processing Vol.11 pp 0876-0879,2016 6. Radu Pietraru, “Secure communication method based on encryption and steganography” International conference on control system and computer science Vol.31 pp 453-458,2017 7. Y Manjula and K B Shivakumar ,”Enhanced Secured Image Steganography using Double Encryption Algorithm” International Conference on Computing for Sustainable Global Development (IndiaCom),2016 8. Tanushree Shelare and Varsha Powar,”A secure transmission approach using B-Trees in steganography”International Conference on Automatic Control and Dynamic Optimization Techniques,2016 9. https://www.sans.org/reading-room/whitepapers/vpns/review-chaffing-winnowing-876 10. http://www.asp.net/: This is the official Microsoft ASP.NET web site. It has a lot of: tutorials, training videos, and sample projects. 11. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6850714&queryText%3Dimage+steganography 12. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6658020&queryText%3Dimage+steganography.
- IJIRIS:: International Journal of Innovative Research in Information Security, Volume V (Issue IX): 465-470 (September 2018)1. Hussein T. Mouftah Khaled A. Ali, "Wireless personal area networks architecture and protocols for multimedia applications," Ad Hoc Networks, vol. 9, pp. 675–686, 2011. 2. Prashant Pillai, Vince W.C. Chook, Stefano Chessa, Alberto Gotta,Y. Fun Hu Paolo Baronti, "Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards," Computer Communications, vol. 30, pp. 1655–1695, 2007. 3. ZigBee Alliance, ZigBee Specification, January.2008, ZigBee Document 053474r17. 4. Kamaran Javed , “ZigBee suitability for Wirelee Sensor Networks in Logistic Telemetry Applications”, School of information Science,Computer and Electrical Engineering, Jan 2006 5. Ricardo Augusto Rodrigues da Silva Severino, Ön the use of IEEE 802.15.4/ZigBee for Time-Sensitive Wireless Sensor Network Applications," Polytechnic Institute of Porto, 2008. 6. F. Cuomo et al., Cross-layer network formation for energy-efficient IEEE 802.15.4/ZigBee Wireless Sensor Networks, Ad Hoc Netw. (2011). 7. Standard for part 15.4: Wireless MAC and PHY specifications for low rate WPAN, IEEE Std 802.15.4, IEEE, New York, NY (Oct 2003). 8. Vijay Anand Sai Ponduru Archana Bharathidasan, "Sensor Networks: An Overview," Department of Computer Science, University of California, Davis, CA 95616,. 9. Nidhi Patel, Hiren Kathiriya , Arjav Bavarva , “Wireless Sensor Network using ZigBee “,Intenational Journal of Research in Engineering and Technology, Vol 2, Issue 6, Jun 2013 10. A. Geetha , “Intelligent Helmet for Coal Minors with voice over ZigBee and Environmental Monitoring” , Middle East Journal of Scentific Research , ISSN 1990-9233,IDOSI Publications 2014. 11. Action Nechibvute, Courage Mudzingw, “Wireless Sensor Networks for SCADA and Industrial Control Systems” , International Journal of Engineering and Technology , Vol 3 No. 12, Dec2013. 12. Hariprabha .V, Vasantharathna .S, “Monitoring and control of food storage depots using Wireless Sensor Network” , International Journal of Industrial Electronics and Electrical Engineering, ISSN 2437-6982,Vol 2 Issue 6 June 2014. 13. Dipali K Shende , Arun K Mane , Rahul K More , Nikel M Nawale , “ARM 7 based Wireless Data Transmission using ZigBee” , International Journal of Innovative research in Electrical, Electronics , Instrumentation and Control Engineering, Vol3 Issue 5, may 2015..
