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Kamalanaban, E.
- Energy Efficiency for Localization Accuracy by Beacon Movement Detection in Wireless Sensor Networks
Abstract Views :149 |
PDF Views:1
Authors
Affiliations
1 Department of CSE, Vel Tech. Dr. RR & Dr. SR Technical University, Chennai, IN
2 Vel Tech. Dr. RR & Dr. SR Technical University, Chennai, IN
3 Department of CSE, SV University,Tirupati, IN
1 Department of CSE, Vel Tech. Dr. RR & Dr. SR Technical University, Chennai, IN
2 Vel Tech. Dr. RR & Dr. SR Technical University, Chennai, IN
3 Department of CSE, SV University,Tirupati, IN
Source
Wireless Communication, Vol 3, No 6 (2011), Pagination: 420-426Abstract
Wireless sensor networks (WSNs) greatly extend our ability to monitor and control the physical world, as they have become the mark of pervasive technology. One of the main design issues for a sensor network is conservation of the energy available at each sensor node. We split the lifetime of the sensor network into equal periods of time known as rounds. Base stations are relocated at the start of a round. Energy efficient organization is forecasting the position of Mean Square Error for every node in the location. Localization is an important issue in wireless sensor networks as maintaining the location of information is indispensable in applications such as routing, target tracking. Beacons (known-location nodes) are one key approach to achieving localization in wireless networks. In this paper we propose a Beacon Movement Detection Schemes such as Location based, Neighbor based, Energy Saving Scheme, Modeling of Broadcasting Scheme, RSS-based localization and DV-hop using the distance vector exchange so that all nodes in the network get distances, in hops, to the landmarks and reliability factor information for wireless sensor networks during simulations, we evaluate the proposed schemes and examine their capability to improve the localization accuracy in events of beacon movement which improves localization.Keywords
Energy Efficient Organization, Localization, RSS-Based Localization Scheme, Energy Saving Scheme, DV-Hop, Signal Strength.
- Intrusion Detection System To Avoid Malicious Intruders In Higher Layer Network Security
Abstract Views :77 |
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 :143 |
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.