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Rengarajan, A.
- Optimization of Recent Attacks Using Internet Protocol
Abstract Views :339 |
PDF Views:2
Authors
Source
National Journal of System and Information Technology, Vol 5, No 1 (2012), Pagination: 8-24Abstract
The Internet threat monitoring (ITM) systems have been deployed to detect widespread attacks on the Internet in recent years. However, the effectiveness of ITM systems critically depends on the confidentiality of the location of their monitors. If adversaries learn the monitor locations of an ITM system, they can bypass the monitors and focus on the uncovered IP address space without being detected. In this paper, we study a new class of attacks, the invisible LOCalization (iLOC) attack. The iLOC attack can accurately and invisibly localize monitors of ITM systems. In the iLOC attack, the attacker launches low-rate port-scan traffic, encoded with a selected pseudo noise code (PN-code), to targeted networks. While the secret PN-code is invisible to others, the attacker can accurately determine the existence of monitors in the targeted networks based on whether the PN-code is embedded in the report data queried from the data center of the ITM system. We formally analyze the impact of various parameters on attack effectiveness. We implement the iLOC attack and conduct the performance evaluation on a real-world ITM system to demonstrate the possibility of such attacks. We also conduct extensive simulations on the iLOC attack using real-world traces. Our data show that the iLOC attack can accurately identify monitors while being invisible to ITM systems. Finally, we present a set of guidelines to counteract the iLOC attack.Keywords
Internet Threat Monitoring, Invisible Localization Attack, PN-code, Security, Attack Traffic, Traffic Rate
References
- Fariba Haddadi, Sara khanchi, Mehran Shetabi, and Vali Derhami (2010), ‘Intrusion Detection and Attack Classification Using Feed-Forward Neural Network’, Proceedings of 2nd International Conference on Computer and Network Technology, pp. 262-266.
- Ferrante A, Piuri V, and Castanier F (2005), ‘A QoS enabled packet scheduling algorithm for IPSec multi accelerator based systems’, Proceedings of the 2nd International Conference on Computing frontiers, pp. 221-229.
- Fineberg V (2002), ‘A practical architecture for implementing end-to-end QoS in an IP network’, IEEE Journal on Communication Magazine, Vol. 40, No. 1, pp. 122-130.
- Francis P, Handley M, Karp R, and Shenker S (2002), ‘A scalable contentaddressable network’, IEEE Journal on Information Computing, pp. 1190- 1199.
- Gao L (2001), ‘On inferring autonomous system relationships in the Internet’, IEEE Journal on Transactions of Networking, Vol. 9, No. 6, pp. 733-745.
- Ghandeharizadeh S, Song S, and Krishnamachari B (2004), ‘Placement of continuous media in wireless peer-to-peer networks’, Proceedings of International Conference on Transactions Multimedia, Vol. 6, Issue 2, pp. 335-342.
- Giaffreda (2001), ‘Name resolving and routing in mobile networks’, Proceedings of 2nd International Conference on 3G Mobile Communication Technologies, pp.191-195.
- Govindan R, Estrin D, and Silva F (2003), ‘Directed Diffusion for Wireless Sensor Networking’, IEEE Journal on Transactions of Networking, Vol. 11, No. 1, pp. 2-16.
- Haas, and Pearlman (2001), ‘The performance of query control schemes for the zone routing protocol’, ACM Journal on Transactions of Networking, Vol. 9, No. 4, pp. 427-438.
- Haim Zlatokrilov, and Hanoch Levy (2008), ‘Area avoidance routing in Distance-Vector networks’, IEEE Journal on Communication Society 2008, pp. 1148-1156.
- Heinzelman W, Kulik J, and Balakrishnan H (1999), ‘Adaptive protocols for Information Dissemination in Wireless Sensor Networks’, Proceedings of International Conference on Mobile Communication, pp. 174-185.
- Huayang Cao, Miao Wang, Xiaoqiang Wang, and Peidong Zhu (2009), ‘A Packet-based Anomaly Detection Model for Inter-domain Routing’, Proceedings of International Conference on Networking, Architecture, and Storage, pp.192-195.
- HuBaux, Buttyan, and Capkun (2001), ‘The quest for security in mobile ad hoc networks’, Proceedings of International Conference on Mobile host Communication, pp. 146-155.
- Jaideep Chandrashekar, Zhenhai Duan, and Xin Yuan (2006), ‘Controlling Inter-Domain Packet Filters to Control IP Spoofing Based on BGP Updates’, IEEE Journal on Communications Society, pp.341-352.
- Jayanth, and Bharghavan (1998), ‘Performance of transport protocols over a multicasting based architecture for Internet host mobility’, IEEE Journal on Communication Society 1998, pp. 1817-1823.
- Jingyuan Li, Liusheng Huang, Weijia Jia, Mingjun Xiao, and Peng Du (2006), ‘Systems on the basis of WiMAX and Wi-Fi’, IEEE Journal on Communication Society, pp. 819-824.
- Jirapummin C, Wattanapongsakorn N, and Kanthamanon P (2002), ‘Hybrid neural networks for intrusion detection system’, Proceedings of International Conference on Circuits, Computers and Communications, pp. 928-931.
- Johnson D, Maltz A, and Broch J (2001), ‘DSR: The Dynamic Source Routing Protocol for Multi-hop Wireless Ad hoc Networks’, IEEE Journal on Ad hoc Networking, pp. 139-152.
- Determining the Existence of Quantitative Association Rules in Data Mining
Abstract Views :240 |
PDF Views:3
Authors
Affiliations
1 Department of IT, RMDEC, Chennai, IN
2 Department of IT, Sree Sastha Institute of Technology, IN
3 Department of CSE, RMKEC, Chennai, IN
1 Department of IT, RMDEC, Chennai, IN
2 Department of IT, Sree Sastha Institute of Technology, IN
3 Department of CSE, RMKEC, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 6 (2010), Pagination:Abstract
Determining the association rules is a core topic of data mining. This survey paper aims at giving an overview to some of the previous researches done in this topic, evaluating the current status of the field, and envisioning possible future trends in this area. The theories behind association rules are presented at the beginning. Comparison of different algorithms is provided as part of the evaluation.
- Data Hiding in Encrypted Images Using Arnold Transform
Abstract Views :253 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Bharath University, IN
2 Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
1 Department of Computer Science and Engineering, Bharath University, IN
2 Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 1 (2016), Pagination: 1339-1344Abstract
Digital image steganography has several applications in information security and communication. Data hiding in encrypted images ensure that both the cover image and the secret message can be recovered at the receiver end. This work presents a novel data hiding and image encryption scheme using random diffusion andTwo dimensional Arnold cat mapping transform. The secret message bits are placed in the least significant bit positions of the cover image. Then a shared key is used to generate random 8 bit random integer stream and is added to the stego image in the random diffusion step. Arnold cat mapping transformation is done to scramble the pixels. The two steps of random diffusion and Arnold transform mapping are done alternatively several times to completely encrypt the image contents. The process is reversed at the receiver end to get both the secret messageand the cover image with little loss. The random diffusion step overcomes the limited period of the Arnold transform.Theembedding capacity of one bit per pixel is achieved. Security analysis is carried out which shows that the encryption is highly secure. The number of collisions is low thus preventing brute force attacks. The original cover image is recoverable with minimal losses.Keywords
Digital Images, Steganography, Arnold Transform, Data Hiding, Image Encryption.- Mining Scalable Multidimensional Sequential User Access Logs Using Parallel Partitioning Transaction Reduction Algorithm
Abstract Views :238 |
PDF Views:3
Authors
Affiliations
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of CSE, Veltech Multitech Dr.RS Engineering College, Avadi, Chennai, Tamil Nadu, IN
1 Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of CSE, Veltech Multitech Dr.RS Engineering College, Avadi, Chennai, Tamil Nadu, IN
Source
Software Engineering, Vol 10, No 1 (2018), Pagination: 6-12Abstract
Web usage mining refers to the automatic discovery and analysis of patterns in click stream generated as a result of user interactions with Web resources on one or more Web sites. The primary data sources used in Web usage mining are the server log files, which include Web server access logs and application server logs. The web usage mining techniques are used to analyze the web usage patterns for a web site. The user access log is used to fetch the user access patterns. These patterns are preprocessed with many preprocessing methods like data fusion, data cleaning, session identification, exclusive user identification, page view identification, term view identification and path completion. To make the entire preprocessing faster, Hash map is used for its data organization. After preprocessing, it gives an isolated group of users with common interests. The complete preprocessing has done with the usage patterns stored in a web server access logs in order to provide clean, unique and reduced dataset for pattern mining. This automatically reduced the original size of dataset which makes it easier of pattern mining, analysis and increases the prediction accuracy. There are numerous pattern mining approaches which can be applied on purified data. The preprocessing practices will exploit the quality of pattern mining methodologies and the results can be used for recommended systems to find the behavior of a user. Key objective is to wide-ranging the above activities with high speed and achieve high prediction accuracy by concentrating on data preprocessing, discovery curious patterns and assessment.
Keywords
Common Interests, Server Log Files, Preprocessing, Hash Map, Exclusive User Identification, Page View Identification, Matrix Transaction Reduction.References
- Udayasri.B, Sushmitha.N, Padmavathi.S, “A LimeLight on the Emerging Trends of Web Mining” , Special Issue of InternationalJournal of Computer Science & Informatics (IJCSI), ISSN(PRINT):2231–5292,Vol.-II,Issue-1,2
- “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data” JaideepSrivastava * t, Robert Cooley:l: , MukundDeshpande, Pang-Ning Tan
- “An effective data pre-processing method for Web Usage Mining”,IEEE, ISBN978-1-4673-5786-9 ,Page(s):7-10.
- Tsuyoshi Murata, Kota Saito, “Extracting Users Interests from Web Log Data” Proceedings of 2006 IEEE/WIC/ACM International Conference of Web Intelligence (WI 2006 Main Conference Proceedings)(WI’06) 2006 IEEE.
- R.M.Suresh, R.Padmajavalli, “An Overview of Data Preprocessing in Data and Web Usage Mining”, RMK Engineering College, Kavaraipettai, IEEE 2006.
- Mirghani. A. Eltahir, Anour F.A. Dafa-Alla, Extracting Knowledge from Web Server Logs Using Web Usage Mining, International Conference on Computing, Electrical and Electronics Engineering (ICCEEE), page-no:413- 417, IEEE, 2013
- Suneetha.K.R, Dr.R.Krishnamoorthi, “Data Preprocessing and Easy Access Retrieval of Data through Data Warehouse”, WCECS 2009, October 2009, 20-22, San Francisco, USA.
- B.Mobasher, R.Cooley, J.Srivastava, “Automatic personalization based on Web usage mining”, Communications of the ACM, Vol-43 (8), 2000, 142-151.
- R.Cooley, “Web Usage Mining: Discovery and Application if Interesting Patterns from Web data”, PhD thesis, Dept.of Computer Science, University of Minnesota, USA, 2000.
- “Review and Analysis of Hashing Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol-4, Issue-5, May 2014, 296-297.
- P.Nithya, Dr.P.Sumathi, “An Enhanced Preprocessing Technique for Web Log Mining by Removing Web Robots”, Tamilnadu, IEEE 2012.
- G.R.C. et al.,"An Efficient Preprocessing Methodology for Discovering Patterns and Clustering of Web Users using a Dynamic ART1 Neural Network," Fifth International Conference on Information Processing, 2011; SpringerVerlag.
- ”Research of an Algorithm Based on Web Usage Mining”,ShuyanBai; Vocational Coll. of Yantai, Qingtian Han; Qiming Liu; XiaoyanGaoIEEE, ISBN: 978-1-4244-3893-8, Page(s): 1-4.
- Maheswara Rao et. al,"An Enhanced Pre-Processing Research Framework for web Log Data Using a Learning Algorithm," Computer Science and Information Technology, DOI, pp. 1-15, 2011.
- Arvind K Sharma and P.C. Gupta, "Predicting the Behavior and Interest of the Website Users through Web Log Analysis”, International Journal of Computer Applications, Vol. 64, No. 7, February 2013.
- Arjun Ram Meghwal, Dr.Arvind K Sharma, “Identifying System Errors through Web Server Log Files in Web Log Mining”, International Journal of Computer Science and Technology”, Vol-7, Issue-1, Jan-March 2016.