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Shunmuganathan, K. L.
- A Multilayered Architecture for Hiding Executable Files in 3D Images
Abstract Views :458 |
PDF Views:108
Authors
Affiliations
1 CSE Dept., Jeppiaar Engg. College, Chennai-600119, IN
2 R.M.K. Engineering College, Chennai- 601206, IN
1 CSE Dept., Jeppiaar Engg. College, Chennai-600119, IN
2 R.M.K. Engineering College, Chennai- 601206, IN
Source
Indian Journal of Science and Technology, Vol 3, No 4 (2010), Pagination: 402-407Abstract
Steganography is a technique to hide secret messages in a host media called cover media. The advantage of steganography over cryptography is that messages do not attract attention to attackers and even receivers. Steganography and cryptography are often used together to ensure security of the secret messages. This paper introduces steganography work on 3D models. In this paper, we will exploit the geometric characteristics of 3D models to provide high-capacity data hiding. Capacity and invisibility are more important than robustness in the steganography system. Therefore, we aim at maximizing data hiding capacity while limiting distortion of cover models in a lower bounded value. A novel multilayered embedding scheme is proposed for enlarging the hiding capacity. In the extraction procedure, the embedding order can also be obtained using the secret key in the spatial analysis step. The payload can then be correctly extracted in this embedding order.Keywords
Steganography, Spatial Analysis, Polygon Models, Stego FileFull Text
References
- Benedens O (1999) Geometry-based watermarking of 3D polygonal models. Proc. IEEE Computer Graphics & Applications. 19 (1), 45-46.
- Cayre F, Devillers O, Schmitt F and Maıtre H (2004) Watermarking 3D triangle meshes for authentication and integrity. INRIA Res.Report RR-5223.
- Chao MW, Lin CH, Yu CW and Lee TY (2009) A high capacity 3D steganography algorithm. IEEE Trans. on visualization and computer graphics. 15 (2), 274-284.
- Cox IJ, Miller ML and Bloom JA (2000) Digital watermarking.
- Fridrich J (1999) Applications of data hiding in digital images.Tutorial for the ISSPA, 22-25.
- Garcia F and Dugelay J (2003) Texture-based watermarking of 3D video objects. IEEE Trans. Circuits & Systems for Video Technol. 13 (8), 853-866.
- Yin K, Pan Z, Jiaoying S and Zhang D (2001) Robust mesh watermarking based on multiresolution processing. Computers & Graphics. 25, 409-420.
- Katzenbeisser S and Petitcolas FAP (2000) eds., Information hiding techniques for steganography and digital watermarking. Artech House. pp:156-172.
- Lin HY, Liao HYM, Lu CS and Lin JC (2005) Fragile watermarking for authenticating 3D polygonal meshes. IEEE Trans. Multimedia. 7 (6), 997-1006.
- Ohbuchi R, Masuda H and Aono M (1998) Watermarking three-dimensional polygonal models through geometric and topological modifications. IEEE J. Selected Areas in Comm. 1, 551-560.
- Praun E (1999) Robust mesh watermarking. Proc. ACM SIGGRAPH ’99. IJTIC. pp:49-56,
- Venkat Narayana Rao T and Govardhan A (2009) Reversible watermarking mechanisms - a new paradigm in mage security. Indian J.Sci.Technol. 2 (5), 23-28. Domain site: http://www.indjst.org.
- Wu HT and Cheung YM (2005) A reversible data hiding approach to mesh authentication. Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI ’05), 1, 774-777.
- Distributed and Cooperative Multi-agent Based Intrusion Detection System
Abstract Views :374 |
PDF Views:121
Authors
Affiliations
1 Department of CSE, Jeppiaar Engineering College, Tamil Nadu–600119, IN
2 Department of CSE, RMK Engineering College, Tamil Nadu–601 206
1 Department of CSE, Jeppiaar Engineering College, Tamil Nadu–600119, IN
2 Department of CSE, RMK Engineering College, Tamil Nadu–601 206
Source
Indian Journal of Science and Technology, Vol 3, No 10 (2010), Pagination: 1070-1074Abstract
One of the primary challenges in intrusion detection is modeling typical application behavior, so that we can recognize attacks by their atypical effects without raising too many false alarms. IDS implemented using mobile agents is one of the new paradigms for intrusion detection. In this paper, we have proposed an effective intrusion detection system in which local agent collects data from its own system and it classifies anomaly behaviors using SVM classifier. Each local agent is capable of removing the host system from the network on successful detection of attacks. The mobile agent gathers information from the local agent before it allows the system to send data. Our system identifies successful attacks from the anomaly behaviors. Experimental results show that the proposed system has high detection rate and low false alarm rate which encourages the proposed system.Keywords
Mobile Agents, Classification, Intrusion Detection System, Packet Loss, Network SecurityFull Text
References
- Bhuse V and Gupta A (2006) Anomaly intrusion detection in wireless sensor networks.J. High Speed Networks. 15(1), 33–51.
- Bo Sun, Wu K, Xiao Y and Wang R (2006) Integration of mobility and intrusion detection for wireless ad hoc networks DOI: 10.1002/dac.853.
- Bradley KA, Cheung S, Puketza N, Mukherjee B and Olsson RA (1998) Detecting disruptive routers: A distributed network monitoring approach. Proc. IEEE Symp. Security Privacy. pp:115-124.
- Cabrera D and Gutiérrez C and Raman K. Mehra (2008) Ensemble methods for anomaly detection and distributed intrusion detection in mobile Ad-Hoc networks.Elsevier Sci. Publishers. 9(1), 96-119.
- Chen H, Han P, Zhou X and Gao C (2007) Lightweight anomaly intrusion detection in wireless sensor networks. Intelligence Security Informatics. Springerlink.
- Deng H, Xu, R, Li, J, Zhang, F, Levy, R and Lee W (2008) Agent-based cooperative anomaly detection for wireless ad hoc networks. Parallel Distributed Sys. 1, 8.
- Liu Y, Comaniciu C and Man H (2006) A Bayesian game approach personal wireless communications. ACM 159593507X.
- Liu Y, Li Y and Man H (2005) MAC layer anomaly detection in Ad Hoc networks. Proc. of the 6th IEEE Information Assurance Workshop. pp: 402-409.
- Mishra A and Nadkarni K (2003) Security in wireless Ad Hoc networks. CRC press LLC.
- Mishra A, Nadkarni K and Animesh Patcha (2004) Intrusion detection in wireless Ad Hoc networks. IEEE Wireless Commun. pp: 48-60.
- Mizrak AT, Cheng YC, Marzullo K and Savage S (2006) Detecting and isolating malicious routers. IEEE Trans. Dependable Secure Computing. 3(3), 230-244.
- Perlman R (1988) Network layer protocols with byzantine robustness, MIT LCS TR-429.
- Puttini R, Hanashiro M, García-Villalba J and Barenco CJ (2006) On the anomaly intrusion-detection in mobile Ad Hoc network environments. Personal Wireless Commun. Vol. 4217/2006, Springerlink.
- Subhadrabandhu FAD and Sarkar S (2008) Signature based intrusion detection for wireless Ad-Hoc networks: A comparative study of various routing protocols. Seas.
- Y.G. Liu, K.F. Chen, X.F. Liao, and W.Zhang (2004) A genetic clustering method for intrusion detection. Pattern Recognition, 37(5), 927-942.
- S.T. Sarasamma, Q.A. Zhu and J. Huff (2005) Hierarchial kohonenen net for anomaly detection in network security. IEEE Transactions on Systems, Man & Cybernetics. 32(2), 302-312.
- A Computational Intelligence for Evaluation of Intrusion Detection System
Abstract Views :742 |
PDF Views:146
Authors
Affiliations
1 Dept. of CSE, Jeppiaar Engineering College, Chennai, IN
2 Dept. of CSE, RMK Engineering College, Chennai, IN
1 Dept. of CSE, Jeppiaar Engineering College, Chennai, IN
2 Dept. of CSE, RMK Engineering College, Chennai, IN
Source
Indian Journal of Science and Technology, Vol 4, No 1 (2011), Pagination: 40-45Abstract
Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. Intrusion detection system is one of the widely used tools for defense in computer networks. In literature, plenty of research is published on Intrusion detection systems. In this paper we present a survey of intrusion detection systems. We survey the existing types, techniques and approaches of intrusion detection systems in the literature. We propose a new architecture for intrusion detection system and outline the present research challenges and issues in intrusion detection system using SVM classifiers. Finally we carry out our experiments based on our proposed methodology using DARPA (Defense advanced research projects agency) intrusion detection data set which is used for IDS evaluation.Keywords
IDS, Data Mining, Network, DARPA Data Set, SVMFull Text
References
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- Amin Hassanzadeh and Babak Sadeghian (2008) Intrusion detection with data correlation relation graph. IEEE, The Third Intl. Conf. on Availability, Reliability and Security. pp.982-989.
- Bane Raman Raghunath and Shivsharan Nitin Mahadeo (2008) Network intrusion detection system. IEEE, First Intl Conf. on Emerging Trends in Engg. & Technol. pp:1272-1277.
- Creation and Deployment of Data Mining-Based Intrusion Detection Systemsin Oracle Database 10g. http://www.oracle.com/technology/products/bi/odm/pdf/odm_based_intrusion_detection_paper_1205. pdf
- Divyata Dal, Siby Abraham, Ajith Abraham, Sugata Sanyal and Mukund Sanglikar (2008) Evolution induced secondary immunity: An artificial immune system based intrusion detection system. IEEE, 7th Computer Information Systems & Industrial Management Applications.pp:65-70.
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- Joong-Hee Leet, Jong-Hyouk Leet, Seon-Gyoung Sohn, Jong-Ho Ryu, and Tai-Myoung Chungt (2008) Effective value of decision tree with KDD 99 intrusion detection datasets for intrusion detection system. IEEE, ISBN: 978-89-5519-136-3
- Juan Wang, Qiren Yang and Dasen Ren (2009) An intrusion detection algorithm based on decision tree technology. IEEE Asia-Pacific Conf. on Information Processing. ISBN: 978-0-7695-3699-6. pp:333-335.
- Khosravifar B and Bentahar J 2008) An experience improving intrusion detection systems false alarm ratio by using honeypot. IEEE, 22nd Intl. Conf. on Advanced Information Networking and Applications.pp: 997-1004.
- Kola Sujatha P, Kannan A, Ragunath S, Sindhu Bargavi K and Githanjali S (2008) A behaviour based approach to host-level intrusion detection using selforganizing maps. IEEE, First Intl. Conf. on Emerging Trends in Engg. & Technol. pp:1267-1271.
- Lgor Vinicius Mussoi de Lima, Joelson Alencar Degaspari and Jo˜ao Bosco Mangueira Sobral (2008) Intrusion detection through artificial neural networks. IEEE, ISBN: 978-1-4244-2066-7.pp:867- 870.
- Lu Huijuan, Chen Jianguo and d Wei Wei (2008) Two stratum Bayesian network based anomaly detection model for intrusion detection system. IEEE, Intl. Symp. on Electronic Commerce & Security.pp:482-487.
- Marimuthu and A. Shanmugan (2008) Intelligent progression for anomoly intrusion detection. IEEE, ISBN: 978-1-4244-2106-0. pp:261-265.
- Mukkamala S, Sung AH and Abraham A (2004) Computational intelligent techniques for detecting denial of service attacks. Proc. of Innovations in Applied Artificial Intelligence, 17th Intl. Conf. on Industrial & Engg. Appl. of Artificial Intelligence & Expert Systems (IEA/AIE), Lecture Notes in Computer Science 3029 Springer, ISBN 3-540- 22007-0, pp: 633-642.
- Owais S, Snasel V, Kromer P and Abraham A (2008) Survey: Using genetic algorithm approach in intrusion detection systems techniques. CISIM 2008, IEEE. pp:300-307.
- Rakesh Agrawal, Arun Swami and Tomasz Imielinski (1993) Mining association rules between sets of items in large databases. Proc. of the 1993 ACM SIGMOD Conf. Washington DC, USA, May 1993. pp:1-10.
- Robert, Richardson (2007) Computer crime and security survey. http://i.cmpnet.com/v2.gocsi.com/ pdf/ CSISurvey2007.pdf.
- Sangeetha S, Vaidehi V, Srinivasan N, Rajkumar KV, Pradeep S, Ragavan N, Sri Sai Lokesh C, Subadeepak I and Prashanth V (2008) Implementation of application layer intrusion detection system using protocol analysis. IEEE-Intl Conf. on Signal processing, Commun. & Networking .pp:279-284
- Su MY, Chang KC, Wei HF and Lin CY (2008) A real-time network intrusion detection system based on incremental mining approach. IEEE.pp: 76- 81.
- Ya-Li Ding, Lei Li and Hong-Qi Luo (2009) A novel signature searching for intrusion detection system using data mining. IEEE 8th Intl. Conf. on Machine Learning & Cybernetics. ISBN: 978-1-4244-3703-0. pp:122-126.
- Youssif Al-Nashif, Aarthi Arun Kumar, Salim Hariri, Guangzhi Qu, Yi Luo and Ferenc Szidarovsky (2008) Multi-Level intrusion detection system. IEEE, Intl. Conf. on Automonic Computing. pp:131-140
- Zhan Jiuhua (2008) Intrusion detection system based on data mining. IEEE, Workshop on Knowledge Discovery and Data Mining, ISBN:978-0-7695-3090-1. pp:402-405.
- Zhengbing H, Zhitang Li and Junqi W (2008) A novel network intrusion detection system (NIDS) based on signatures search of data mining. IEEE, Workshop on knowledge discovery and data mining. pp:1-7.
- Network Based Anomaly Intrusion Detection System Using SVM
Abstract Views :439 |
PDF Views:105
Authors
Affiliations
1 Department of CSE, Jeppiaar Engineering College,TamilNadu–600 119
2 Department of CSE, RMK Engineering College,TamilNadu–601 206
1 Department of CSE, Jeppiaar Engineering College,TamilNadu–600 119
2 Department of CSE, RMK Engineering College,TamilNadu–601 206
Source
Indian Journal of Science and Technology, Vol 4, No 9 (2011), Pagination: 1105-1108Abstract
The security and integrity of a computer system is compromised when an intrusion occurs. It becomes impossible for legitimate users to access different network services when network-based attacks purposely occupy or sabotage network resources and services. Our proposed method is a scalable detection method for network based anomalies. We use Support Vector Machines (SVM) for classification. This paper presents a method for enhancing the training time of SVM, particularly when dealing with large data sets, using hierarchical clustering technique. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the problems of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps to find the boundary points, which are the most qualified data points to train SVM, between any two classes. We present a new approach of combination of SVM and DGSOT, which begins with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We show that our proposed variations contribute significantly in improving the training process of SVM with high percentage of detection accuracy.Keywords
SVM, Classification, Intrusion Detection, Intrusion Detection System, Network SecurityFull Text
References
- Anderson D, Frivold T and Valdes A (1995) Nextgeneration intrusion detection expert system (NIDES) a summary. Technical Report SRI-CSL-95-07. Computer Sci.Laboratory, SRI Intl. Menlo Park.
- Bivens A, Palagiri C, Smith R, Szymanski B and EmbrechtsM (2002) Intelligent engineering systems through artificial neural networks. Proc. ANNIE-2002, vol. 12, pp. 579–584.
- Balcazar JL, Dai Y and Watanabe O (2001) A random sampling technique for training support vector machines for primal-form maximal-margin classifiers, algorithmic learning theory. Proc. 12th Intl. Conf., ALT, pp: 119.
- Girardin L and Brodbeck D (1998) A visual approach or monitoring logs. Proc. 12th System Administration Conf. (LISA 98). pp: 299–308.
- Ilgun K, Kemmerer RA and Porras PA (1995) State transition analysis: A rule-based intrusion detection approach. IEEE Trans. Software Eng. 21(3), 181–199.
- Lee W and Stolfo SJ (2000) A framework for constructing features and models for intrusion detection systems. ACM Trans. Inform. Syst. Security. 3(4), 227– 261.
- Marchette D (1999) A statistical method for profiling network traffic. In: Proc. of the First USENIX Workshop on Intrusion Detection and Network Monitoring. pp:119– 128.
- McCanne S, Leres C and Jacobson V (1989) Libpcap. available via anonymous ftp at ftp://ftp.ee.lbl.gov/
- Lippmann R, Graf I, Wyschogrod D, Webster SE, Weber DJ and Gorton S (1998) The 1998 DARPA/AFRL off-line intrusion detection evaluation. In: Proc. of the First Intl. Workshop on Recent Advances in Intrusion Detection (RAID).
- Stolfo SJ, Lee W, Chan PK, Fan W and Eskin E (2001) Data miningbased intrusion detectors: an overview of the Columbia IDS project. ACM SIGMOD Record.30(4),5–14.
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- Upadhyaya S, Chinchani R and Kwiat K (2001) An analytical framework for reasoning about intrusions. In: Proc. IEEE Symposium on Reliable Distributed Systems. pp:99–108.
- Wang K and Stolfo SJ (2003) One class training for masquerade detection. In: Proc. 3rd IEEE Conf. Data Mining Workshop on Data Mining for Computer Security.
- Yu H, Yang J and Han J (2003) Classifying large data sets using SVM with hierarchical clusters. In: Proc. SIGKDD 2003. pp: 306–315.
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- Improved Detection of Dos Attacks Using Intelligent Computation Techniques
Abstract Views :244 |
PDF Views:4
Authors
Source
National Journal of System and Information Technology, Vol 3, No 2 (2010), Pagination: 127-138Abstract
IDSs play a principal role in pro-actively detecting intrusions into enterprise-level computer networks, therefore the accuracy with which it performs this vital function is of paramount importance. Many studies have previously been conducted to improve upon proper classification of detections using neural networks and machine learning algorithms. We try to compare the performance of various intelligent computation techniques like Bayesian networks, Naive Bayesian, Logistic regression, RBF networks, Multi-Layer perception, SVMs with the SMO model, Kth nearest neighbour and Random forest in detecting DoS attack patterns. The data that was used to train and validate these techniques was obtained from the MIT Lincoln lab study into IDSs. The results obtained provide a clear comparison of the individual intelligent computation techniques ability in identifying and classifying attack patterns.Keywords
Networks, Intrusion Detection, Denial of Service, Datasets, Data Mining, Bayesian Networks, Naive Bayesian, Logistic Regression, RBF Networks, Multi-layer Perception, Support Vector Machines, Sequential Minimal Optimization, Kth Nearest Neighbor, Random Forest
References
- Kruegal C., Valeur F., Vigna G., Kemmerer R., “Statefull intrusion detection for high speed networks”, In proceedings of IEEE Symposium on Security and Privacy, pp 285-294, May 2002
- Mukkamala S., and Sung. A. H. (2003) A Comparative Study of Techniques for Intrusion Detection. Proceedings of 15th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society Press, pp 570-579
- K. Park, and H. Lee, “On the Effectiveness of Router-Based Packet Filtering for Distributed DoS attack and Prevention in Power-Law Internets”, Proc. of the SGICOMM, pp. 15-26, 2001
- S. E. Webster, “The Development and Analysis of Intrusion Detection Algorithms”, S.M. Thesis, Massachusetts Institute of Technology, 1998
- K. Kendall, “A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems”, Master's Thesis, Massachusetts Institute of Technology, 1998.
- “Internet Protocol Specification”, IETF, RFC 791, September 1981
- CERT Advisory CA-1998-01 Smurf IP Denial-of-Service Attacks http://www.cert.org/advisories/CA-1998-01.html, January 5, 1998
- Jason Anderson, “An Analysis of Fragmentation Attacks”, March 2001
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- Vladimir V. N. (1995) The Nature of Statistical Learning Theory. Springer
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- [John Platt, “Fast training of support vector machines using sequential minimal optimization,” Advances in kernel methods: support vector learning, Pages: 185 – 208, 1999
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- Versioning File System in Cloud Computing with Intrusion Detection using Mobile Agent
Abstract Views :168 |
PDF Views:3
Authors
Affiliations
1 Sathyabama University, Chennai, IN
2 R.M.K. Engineering College, Thiruvallur, IN
1 Sathyabama University, Chennai, IN
2 R.M.K. Engineering College, Thiruvallur, IN
Source
Wireless Communication, Vol 5, No 4 (2013), Pagination: 163-169Abstract
Versioning file system is a file system to store the computer file in various several versions in the timeline. Snapshots of the gradual changes of the files are recorded in this file system. A gradual backup of the file directory is virtually stored in cloud. Intrusion detection for user versioning file handles a new way to track changes provided with the versioning metadata is hidden from the cloud host Mobile agent which reincarnates for each user file is used to govern the communication and parallel execution in cloud host. Each version of the files is stored and can be retrieved on user demand to perform replacement, difference viewing with other versioned file.
Keywords
Versioning File System, Intrusion Detection, Cloud Computing, Mobile Agent, Storage Service.- Intrusion Detection System with Dynamic Training Model
Abstract Views :135 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science and Engineering, Jeppiaar Engineering College, Tamilnadu–600119, IN
2 Department of Computer Science and Engineering, RMK Engineering College, Tamilnadu-601206, IN
1 Department of Computer Science and Engineering, Jeppiaar Engineering College, Tamilnadu–600119, IN
2 Department of Computer Science and Engineering, RMK Engineering College, Tamilnadu-601206, IN
Source
Wireless Communication, Vol 3, No 11 (2011), Pagination: 772-777Abstract
Intrusion detection relies on the extensive knowledge of security experts, particularly, on their familiarity with the computer systems to be protected. To reduce this dependency, various machine learning techniques and data mining techniques have been deployed for intrusion detection. An IDS is usually deployed in a dynamically changing environment, which requires continuous training of the intrusion detection model, in order to sustain sufficient performance. The manual training process carried out in the current systems depends on the system administrators in working out the training solution and in integrating it into the intrusion detection model.
In this paper, an automatically training IDS is proposed which will automatically train the detection model on-the-fly according to the feedback provided by operators when false predictions are encountered. The proposed system is evaluated using the KDDCup’99 intrusion detection dataset. Experimental results show that the system achieves up to 31% improvement in terms of misclassification cost when compared with a system lacking the tuning feature. If only 12% false predictions are used to train the model, the system still achieves about 32% improvement. Administrators can focus on verification of predictions with low confidence level, as only those predictions determined to be false will be used to train the detection model.
Keywords
Intrusion Detection, Classification, Data Mining, Learning Algorithm.- A Novel Low Distortion Image Steganography for Executable Files
Abstract Views :169 |
PDF Views:5
Authors
Affiliations
1 Jeppiaar Engineering College, Chennai – 119, IN
2 CSE, R.M.K. Engineering College, Chennai, IN
1 Jeppiaar Engineering College, Chennai – 119, IN
2 CSE, R.M.K. Engineering College, Chennai, IN
Source
Digital Image Processing, Vol 3, No 13 (2011), Pagination: 806-810Abstract
Steganography is the process of embedding a ―secret‖ digital data in another cover data. Not like general digital watermarking applications, steganography tries to form secret communication between the sender and receiver and thus causes the requirement of hiding the presence of the embedded message. This paper proposes a new steganography method for hiding executable files where palette images are considered as cover images. In this paper, a new algorithm has been proposed for hiding executable files and the experiment results show that the proposed technique provides a high load steganography arrangement in the pixel domain. Also the proposed approach provides a new idea by which the secret bit assignment with the embedding regions of the cover image. Since the image is divided into several regions and the embedding region is identified as it will generate lower distortion rate, this approach could be considered very secure one against steganalytic attacks.Keywords
Steganography, Watermarking, Steganalysis, PE file.- Executable File Hiding:An LSBMR Perspective
Abstract Views :127 |
PDF Views:2
Authors
Affiliations
1 CSE Department, Jeppiaar Engineering College, Chennai, IN
2 CSE Department, R.M.K. Engineering College, Chennai, IN
1 CSE Department, Jeppiaar Engineering College, Chennai, IN
2 CSE Department, R.M.K. Engineering College, Chennai, IN
Source
Digital Image Processing, Vol 3, No 7 (2011), Pagination: 432-436Abstract
Steganography is the approach for hiding any secret message in a variety of multimedia carriers like images, audio or video files. Whenever we are hiding a data, it is very important to make it invisible, so that it could be protected. A number of steganographic algorithms have been proposed based on this property of a steganographic system. This paper concentrates on integrating Tri way pixel value differencing approach and LSB matching revisited. The secret data embedded in images were images, text and audio signals so far. The proposed scheme has also come with the executable file as secret data. Also, the experimentation results show that, the important properties of a steganographic system such as imperceptibility, capacity of the carrier image and also resistance against the various steganalytic tools have also been achieved with this stego-system.Keywords
Spatial Domain, Executable File, TPVD, LSBMR, Steganalysis.- A Computational Intelligence for Performance Evaluation of Honeypots
Abstract Views :390 |
PDF Views:3
In our experiments for system level detection we use magic number techniques, virtual register sets technique and interrupt description table technique. In magic number technique our program takes the magic number, port number and command to execute as inputs and output whether it is VM ware or VPC or is it a host machine. In IDT technique our program uses SIDT we trace the finger prints of virtual machine and determine its VMware or VPC. In detection of sebek we look for the finger prints present in the memory and hijack the system call that is used by sebek. This paper also describes the results concerning the robustness and generalization capabilities of kernel methods in detecting honeypots using system and network finger printing data. We use traditional support vector machines. We also evaluate the impact of kernel type and parameter values on the accuracy of a support vector machine performing honeypot classification. In our experiments it is found that SVM performs the best for data sent on the same network.
Authors
Affiliations
1 Sathyabama University, Chennai, IN
2 R.M.K Engineering College, Chennai, IN
1 Sathyabama University, Chennai, IN
2 R.M.K Engineering College, Chennai, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 4 (2011), Pagination: 227-233Abstract
Internet security deals with the methods and tools used for protecting the information transactions in various business, government and academic organizations. Honeypot is an information gathering and learning tools. It is used to collect the information about the intruders, their attack patterns, reason for attack and tools used by thing. This information, which is collected about the intruders help a lot to learn about their motives, proceedings and the technical abilities of the intruders. This paper focuses on the detection of virtual environments and low interaction honeypots by using a feature set that is built using traditional system and network level finger printing mechanisms. Earlier work in the area has been mostly based on the system level detection. The results aim at bringing out the limitations in the current honeypot technology.In our experiments for system level detection we use magic number techniques, virtual register sets technique and interrupt description table technique. In magic number technique our program takes the magic number, port number and command to execute as inputs and output whether it is VM ware or VPC or is it a host machine. In IDT technique our program uses SIDT we trace the finger prints of virtual machine and determine its VMware or VPC. In detection of sebek we look for the finger prints present in the memory and hijack the system call that is used by sebek. This paper also describes the results concerning the robustness and generalization capabilities of kernel methods in detecting honeypots using system and network finger printing data. We use traditional support vector machines. We also evaluate the impact of kernel type and parameter values on the accuracy of a support vector machine performing honeypot classification. In our experiments it is found that SVM performs the best for data sent on the same network.
Keywords
Honeypot, Network, Operating System, Sebek, SVM.- Decentralized Multi Platform Data Fusion Using Agent Technology
Abstract Views :191 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Applications, RMK Engineering College, Tamilnadu, IN
2 Department of Computer Science and Engineering, RMK Engineering College, Tamilnadu, IN
1 Department of Computer Applications, RMK Engineering College, Tamilnadu, IN
2 Department of Computer Science and Engineering, RMK Engineering College, Tamilnadu, IN