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Madhusudhanan, S.
- Prevention of Insider Attack against Database Access Control Mechanism
Abstract Views :173 |
PDF Views:1
Authors
Affiliations
1 Dept. of CSE, SNGCE, Kadayiruppu, Kerala, IN
1 Dept. of CSE, SNGCE, Kadayiruppu, Kerala, IN
Source
Journal of Network and Information Security, Vol 5, No 1 (2017), Pagination: 20-23Abstract
Existing SQL Access Control Mechanisms are extremely limited. Attackers can access the sensitive information through escalating their privileges. Practical attacks against existing database are increasing nowadays by using database features such as triggers and integrity constraints. Moreover the theories behind access control mechanism lack adequate security policies and attackers capabilities. This paper addresses the three main privilege escalation attacks by using database features and its prevention methods.Keywords
DBMS, DB Security, Access Control Mechanism, Insider Attacks, Privilege Escalation, SQL.
References
- Z. Nick, “Database security and cryptography,” National Technical University of Athens, 2000.
- Q. He, and A. I. Anton, “Requirements-based Access Control Analysis and Policy Specification (ReCAPS),” Information and Software Technology, June 2011.
- T. F. Lunt, and E. B. Fernandez, “Database security,” ACM SIGMOD Record 19, no. 4, pp. 90-97, 1990.
- Kriti, and I. Kashyap, “Database security and access control models: A brief overview,” International Journal of Engineering Research & Technology, vol. 2, no. 5, pp. 743-751, May 2013.
- M. Guarnieri, S. Marinovic, and D. Basin, “Strong and provably secure database access control,” IEEE European Symposium on Security and Privacy, January 2016.
- Fatigue Analysis of Steering Knuckle Using Finite Element Simulation:Technical Note
Abstract Views :237 |
PDF Views:140
Authors
Affiliations
1 Dept. of Mech. Engg., Dr. Mahalingam College of Engg. and Tech., Pollachi, Coimbatore, IN
1 Dept. of Mech. Engg., Dr. Mahalingam College of Engg. and Tech., Pollachi, Coimbatore, IN
Source
International Journal of Vehicle Structures and Systems, Vol 9, No 3 (2017), Pagination: 142-144Abstract
Steering knuckle is a prominent component of an automobile steering system. It is subjected cyclic loads from the suspension system during its service life. The Spheroid Graphite (SG) iron is the most commonly used material for manufacturing the steering knuckle. Fatigue behaviour is therefore a key consideration in its design and performance evaluation. Geometric modelling and stress analysis of such steering knuckle using finite element analysis software is carried out. Then the fatigue life of steering knuckle made from SG iron has been evaluated under the different load conditions and the results have been compared.Keywords
Steering Knuckle, Fatigue Life, Spheroid Graphite Iron, Finite Element Simulation.Full Text
- Intrusion Detection System To Avoid Malicious Intruders In Higher Layer Network Security
Abstract Views :84 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech Hightech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Prathyusha Engineering College, India., IN
3 Department of Computer Science and Engineering, Panimalar Engineering College, India., IN
4 Department of Information Technology, RMK Engineering College, India., IN
1 Department of Computer Science and Engineering, Veltech Hightech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Prathyusha Engineering College, India., IN
3 Department of Computer Science and Engineering, Panimalar Engineering College, India., IN
4 Department of Information Technology, RMK Engineering College, India., IN
Source
ICTACT Journal on Communication Technology, Vol 9, No 2 (2018), Pagination: 2868-2875Abstract
Online criminals are focusing their attention more and more on ordinary computer users, seeking to take advantage of them through a variety of social and technological exploitation techniques. Some hackers are getting more skilled and determined. The ability to conceal their identities, keep their communications secret, keep their finances separate from their activities, and make use of private infrastructure are all areas in which cybercriminals have shown a high degree of proficiency. It is of the utmost importance to safeguard computers with surveillance systems that are able to identify complex varieties of malware. In this paper, we utilized machine learning algorithm to validate the samples from different datasets. The machine learning classifier is utilized to find the efficacy of the entire model in validating the class samples. The simulation is conducted in python to test the efficacy of the model against various class of datasets. The results show that the proposed method achieves higher degree of accuracy than the other models.Keywords
IDS, Security, Attack, Network Security.References
- Jiankun Hu, Xinghuo Yu, D. Qiu and Hsiao-Hwa Chen, “A Simple and Efficient Hidden Markov Model Scheme for Host-Based Anomaly Intrusion Detection”, IEEE Network, Vol. 23, No. 1, pp. 42-47, 2009.
- K.K. Gupta, and R. Kotagiri, “Layered Approach Using Conditional Random Fields for Intrusion Detection”, IEEE Transactions on Dependable and Secure Computing, Vol. 7, No. 1, pp. 35-49, 2010.
- S. Devaraju and S. Ramakrishnan, “Performance Analysis of Intrusion Detection System using Various Neural Network Classifiers”, Proceedings of International Conference on International Conference on Recent Trends in Information Technology, pp. 1033-1038, 2011.
- Mendonça, R. V., Teodoro, A. A., Rosa, R. L., Saadi, M., Melgarejo, D. C., Nardelli, P. H., & Rodríguez, D. Z. (2021). IDS based on fast hierarchical deep convolutional neural network. IEEE Access, 9, 61024-61034.
- Neveen I. Ghali, “Feature Selection for Effective AnomalyBased Intrusion Detection”, International Journal of Computer Science and Network Security, Vol. 9, No. 3, pp. 285-289, 2009.
- R. Plutchik, “Emotion: Theory, Research, and Experience”, Academic Press, 1980.
- P.R. Kanna and P. Santhi, “Unified Deep Learning Approach for Efficient IDS using Integrated SpatialTemporal Features”, Knowledge-Based Systems, Vol. 226, pp. 107132-107143, 2021.
- H. Hindy, E. Bayne and M. Bures, “Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study”, Proceedings of International Conference on Network, pp.1-14, 2020.
- M. Zhou, L. Han, H. Lu and C. Fu, “Intrusion Detection System for IoT Heterogeneous Perceptual Network”, Mobile Networks and Applications, Vol. 33, No. 1, pp. 1-14, 2020.
- L. Xiao, X. Wan, X. Lu and Y. Zhang, “IoT Security Techniques based on Machine Learning: How do IoT Devices use AI to Enhance Security?”, IEEE Signal Processing Magazine, Vol. 35, No. 5, pp. 41-49, 2018.
- B. Gobinathan and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-9, 2021.
- Z.K. Maseer, “Benchmarking of Machine Learning for Anomaly Based IDSs in the CICIDS2017 Dataset”, IEEE Access, Vol. 9, pp. 22351-22370, 2021.
- X. Li and L. Wu, “Building Auto-Encoder IDS based on Random Forest Feature Selection”, Computers and Security, Vol. 95, pp. 101851-101865, 2020.
- T. Saba and S.A. Bahaj, “Anomaly-based IDS for IoT Networks through Deep Learning Model”, Computers and Electrical Engineering, Vol. 99, pp. 107810-107818, 2022.
- R. Ferdiana, “A Systematic Literature Review of IDS for Network Security: Research Trends, Datasets and Methods”, Proceedings of International Conference on Informatics and Computational Sciences, pp. 1-6, 2020.
- Intrusion Detection System To Avoid Malicious Intruders In Higher Layer Network Security
Abstract Views :148 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Veltech Hightech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Prathyusha Engineering College, India., IN
3 Department of Computer Science and Engineering, Panimalar Engineering College, India., IN
4 Department of Information Technology, RMK Engineering College, India., IN
1 Department of Computer Science and Engineering, Veltech Hightech Dr.Rangarajan Dr.Sakunthala Engineering College, India., IN
2 Department of Computer Science and Engineering, Prathyusha Engineering College, India., IN
3 Department of Computer Science and Engineering, Panimalar Engineering College, India., IN
4 Department of Information Technology, RMK Engineering College, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2868-2875Abstract
Online criminals are focusing their attention more and more on ordinary computer users, seeking to take advantage of them through a variety of social and technological exploitation techniques. Some hackers are getting more skilled and determined. The ability to conceal their identities, keep their communications secret, keep their finances separate from their activities, and make use of private infrastructure are all areas in which cybercriminals have shown a high degree of proficiency. It is of the utmost importance to safeguard computers with surveillance systems that are able to identify complex varieties of malware. In this paper, we utilized machine learning algorithm to validate the samples from different datasets. The machine learning classifier is utilized to find the efficacy of the entire model in validating the class samples. The simulation is conducted in python to test the efficacy of the model against various class of datasets. The results show that the proposed method achieves higher degree of accuracy than the other models.Keywords
IDS, Security, Attack, Network Security.References
- Jiankun Hu, Xinghuo Yu, D. Qiu and Hsiao-Hwa Chen, “A Simple and Efficient Hidden Markov Model Scheme for Host-Based Anomaly Intrusion Detection”, IEEE Network, Vol. 23, No. 1, pp. 42-47, 2009.
- K.K. Gupta, and R. Kotagiri, “Layered Approach Using Conditional Random Fields for Intrusion Detection”, IEEE Transactions on Dependable and Secure Computing, Vol. 7, No. 1, pp. 35-49, 2010.
- S. Devaraju and S. Ramakrishnan, “Performance Analysis of Intrusion Detection System using Various Neural Network Classifiers”, Proceedings of International Conference on International Conference on Recent Trends in Information Technology, pp. 1033-1038, 2011.
- Mendonça, R. V., Teodoro, A. A., Rosa, R. L., Saadi, M., Melgarejo, D. C., Nardelli, P. H., & Rodríguez, D. Z. (2021). IDS based on fast hierarchical deep convolutional neural network. IEEE Access, 9, 61024-61034.
- Neveen I. Ghali, “Feature Selection for Effective AnomalyBased Intrusion Detection”, International Journal of Computer Science and Network Security, Vol. 9, No. 3, pp. 285-289, 2009.
- R. Plutchik, “Emotion: Theory, Research, and Experience”, Academic Press, 1980.
- P.R. Kanna and P. Santhi, “Unified Deep Learning Approach for Efficient IDS using Integrated SpatialTemporal Features”, Knowledge-Based Systems, Vol. 226, pp. 107132-107143, 2021.
- H. Hindy, E. Bayne and M. Bures, “Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study”, Proceedings of International Conference on Network, pp.1-14, 2020.
- M. Zhou, L. Han, H. Lu and C. Fu, “Intrusion Detection System for IoT Heterogeneous Perceptual Network”, Mobile Networks and Applications, Vol. 33, No. 1, pp. 1-14, 2020.
- L. Xiao, X. Wan, X. Lu and Y. Zhang, “IoT Security Techniques based on Machine Learning: How do IoT Devices use AI to Enhance Security?”, IEEE Signal Processing Magazine, Vol. 35, No. 5, pp. 41-49, 2018.
- B. Gobinathan and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-9, 2021.
- Z.K. Maseer, “Benchmarking of Machine Learning for Anomaly Based IDSs in the CICIDS2017 Dataset”, IEEE Access, Vol. 9, pp. 22351-22370, 2021.
- X. Li and L. Wu, “Building Auto-Encoder IDS based on Random Forest Feature Selection”, Computers and Security, Vol. 95, pp. 101851-101865, 2020.
- T. Saba and S.A. Bahaj, “Anomaly-based IDS for IoT Networks through Deep Learning Model”, Computers and Electrical Engineering, Vol. 99, pp. 107810-107818, 2022.
- R. Ferdiana, “A Systematic Literature Review of IDS for Network Security: Research Trends, Datasets and Methods”, Proceedings of International Conference on Informatics and Computational Sciences, pp. 1-6, 2020.