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Magesh, S.
- Reverse Engineering of Bitlocker External Key Files and Meta Data-A Forensic Need
Abstract Views :244 |
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Authors
Affiliations
1 Department of Information Security and Computer Forensics, SRM University, Chennai, IN
2 Resource Center for Cyber Forensics, CDAC, Trivandrum, IN
3 Department of Information Security and Computer Forensics, SRM University, Chennai, IN
1 Department of Information Security and Computer Forensics, SRM University, Chennai, IN
2 Resource Center for Cyber Forensics, CDAC, Trivandrum, IN
3 Department of Information Security and Computer Forensics, SRM University, Chennai, IN
Source
Software Engineering, Vol 3, No 7 (2011), Pagination: 317-322Abstract
Microsoft’s Bit locker tool has made the job of forensic analysts tougher. It’s full disk encryption feature enables users to encrypt their data. When operated in USB key mode, bit locker generates an external key file called .bek file[1]. This file must be needed for an investigator to unlock and decrypt any encrypted drive. If the investigator fails to obtain this .bek file, he cannot unlock the encrypted media and cannot proceed with the further analysis. In this paper we propose a solution to this problem which aims at reconstruction of a .bek file. We observe the metadata sector of the encrypted drive. The metadata sector gives information about the .bek file name. This can be used to reconstruct a file. This reconstructed .bek file can be used to unlock an encrypted media and proceed with further forensic analysis.Keywords
Bitlocker[4], .bek File[4], Metadata[1], USB Key Mode[4].
- Authentication Framework for Military Applications Employing Wireless Sensor Networks and Private Cloud
Abstract Views :117 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, SRM University, Chennai - 603203, Tamil Nadu, IN
1 Department of Information Technology, SRM University, Chennai - 603203, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 21 (2016), Pagination:Abstract
Objectives: To provide an authentication framework between military data centres pertaining to different levels of operations within the private cloud and a simple authentication schema for authenticating users at the wing-commander level in the special sinks deployed in our territory closer to line of control. Methods: In order to achieve the above mentioned objectives, we designed a conceptual defense structure that will highlight the various hierarchical levels of military operations. Military WSNs and data centres will utilize the designed simple authentication schema to improve the lifetime of the WSNs. The methodology adopted primarily consists of modifications to the existing Kerberos setup, so that it could fit the conceptual defense structure by utilizing Heimdal Kerberos distribution. Heimdal's modified Kerberos distribution is utilized in the cloud gateway system to create Kerberos Distribution Center. The modified Kerberos equations are provided in this paper. Findings: Based on the simulations carried out, it is identified that number of messages required for various dialogs for modified Kerberos is relatively less compared to the original version of Kerberos. The response time for modified Kerberos in single realm and cross realm based on different number of requests showed that modified Kerberos is performing better and efficient with respect to the response time metric. Minimum number of messages required for Kerberos Authentication using v4 (Simple dialog), v4 (Secure dialog), v4 (Authentication dialog), v5 (Request for service in another realm using Inter realm authentication) are 3, 5, 6 and 7 respectively. Response times range for single realm lie in the range of 3ms to 20ms approximately for 10 to 100 requests per minute. Response times range for cross realm lie in the range of 7 ms to 47 ms approximately for 10 to 100 requests per minute. The authentication time to authenticate instructions received at special sinks from level 1 resource via cloud gateway ranges from 4.5 ms to 6 ms for message sizes ranging from 100 bytes to 1000 bytes. The response times obtained from single realm authentication indicates lesser values as compared against cross realm authentication which is in consensus with the theory of Kerberos. Applications: The proposed scheme finds its application in all mission critical tasks where the time taken for successful authentication of users should be drastically reduced to improve the system performance.Keywords
Authentication, Cloud, Defense, Kerberos, Wireless Sensor Networks.Full Text
- Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol
Abstract Views :249 |
PDF Views:4
Authors
Affiliations
1 Department of Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, ID
2 Department of Computer Science, Sri Aravindar Engineering College, Villupuram, Tamil Nadu, IN
3 Department of Computer Science, Sri Malolan College of Arts and Science, Kanchipuram, Tamil Nadu, IN
4 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
5 School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, IN
1 Department of Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, ID
2 Department of Computer Science, Sri Aravindar Engineering College, Villupuram, Tamil Nadu, IN
3 Department of Computer Science, Sri Malolan College of Arts and Science, Kanchipuram, Tamil Nadu, IN
4 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
5 School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 4 (2020), Pagination: 93-102Abstract
Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energy-aware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability.Keywords
Taylor Series, Grey Wolf Optimization, Multi-hop Routing, Energy Efficiency, SecurityFull Text
References
- Deepti Gupta, “Wireless Sensor Networks ‘Future trends and Latest Research Challenges’”, IOSR Journal of Electronics and Communication Engineering, vol. 10, no. 2,pp.41-46, 2015.
- Khalaf, Osamah Ibrahim, and Bayan Mahdi Sabbar. "An overview on wireless sensor networks and finding optimal location of nodes", Periodicals of Engineering and Natural Sciences, vol.7, no. 3, pp: 1096-1101, 2019.
- Amruta Lipare, Damodar Reddy Edla, VenkatanareshbabuKuppili, “Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function”, Elsevier, Applied Soft Computing Journal,vol. 84, no. 105706, 2019.
- Gupta V., Pandey R.,“An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks”, International journal of Engineering Science and Technology, vol. 19, pp:1050–1058, 2016.
- Osamah Ibrahim Khalaf,GhaidaMuttasharAbdulsahib And Bayan Mahdi Sabbar, “Optimization of Wireless Sensor Network Coverage using the Bee Algorithm”, Journal of Information Science And Engineering, vol. 36, pp.377-386, 2020.
- M. Lehsaini, H. Guyennet, and M. Feham, “An efficient cluster-based self-organisation algorithm for wireless sensor networks,” International Journal of Sensor Networks, vol. 7, no. 1-2, pp. 85–94, 2010.
- Qingjian Ni, Qianqian Pan, Huimin Du, Cen Cao and YuqingZhai, “A Novel Cluster Head Selection Algorithm Based On Fuzzy Clustering and Particle Swarm Optimization”, IEEE/ACM Transactions on Computational Biology and Bioinformatics,vol. 10, pp:76-84, 2017.
- S.Murugan, S.Jeyalaksshmi, B.Mahalakshmi, G.Suseendran, T.NusratJabeen,R.Manikandan, “Comparison of ACO and PSO algorithm using energy consumption and load balancing in emerging MANET and VANET infrastructure”, JCR, vol. 7, no. 9, pp: 1197-1204, 2020.
- R. Elkamel, A. Cherif, R. Elkamel, A. Cherif, R. Elkamel, A. Cherif, “Energy-efficient routing protocol to improve energy consumption in wireless sensors networks: energy efficient protocol in WSN”, International Journal of Communication System, Vol. 30, no. 6, 2017.
- Sabor N., Abo-Zahhad M., Sasaki S., Ahmed S.M, “An unequal multi-hop balanced immune clustering protocol for wireless sensor networks”, Journal of Applied Soft Computing, vol. 43, pp:372–389, 2016.
- Wang Ke, OuYangrui, Ji Hong, Zhang Heli, Li Xi, “Energy aware hierarchical cluster-based routing protocol for WSNs”, The Journal of China Universities of Posts and Telecommunications,vol. 23, no. 4, pp: 46-52, 2016.
- Mohan, R., Ananthula, V.R., “Reputation-based secure routing protocol in mobile ad-hoc network using Jaya Cuckoo optimization”, International Journal of Modeling, Simulation, Science Computing, vol. 10, no. 3, 2019.
- Cengiz, K., Dag, T.,“Energy aware multi-hop routing protocol for WSNs”,IEEE Access,vol. 6, pp. 2622–2633, 2018.
- Shende, D. K., &Sonavane, S. S., “CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications”, Springer Wireless Networks, pp. 1-9,2020.
- Sampathkumar, A,Mulerikkal, J., &Sivaram, M., “Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks”, Springer Wireless Networks, vol. 21, pp. 1-12,2020.
- Mohan, R., Reddy, A.V., “T-Whale: trust and whale optimization model for secure routing in mobile Ad-Hoc network”, International Journal of Artificial Life Research (IJALR), vol. 8, no. 2, pp: 67–79, 2018.
- Ch, Ram & A, Venugopal, “M-LionWhale: Multi-objective optimization model for secure routing in mobile Ad-hoc network”, IET Communications, vol. 12, pp. 1-7, 2018.
- Kumar, R., Kumar, D., & Kumar, D., “EACO and FABC to multi-path data transmission in wireless sensor networks”, IET Communications, vol. 11, no. 4, pp. 522–530, 2017.
- Rajeev Kumar and Dilip Kumar, “Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks”,Hindawi journal of sensors, Article ID 5836913, pp. 1-19, 2016.
- P. Kuila, P.K. Jana, “Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach”, Engineering Applications Artificial Intelligence, Vol. 33, pp. 127–140, 2014.
- R. Pachlor, D. Shrimankar, “VCH-ECCR: a centralized routing protocol for wireless sensor networks”,Journal of Sensor, vol.1, pp. 1–10, 2017
- Srbinovska, M., Cundeva-Blajer, M., Optimization Methods for Energy Consumption Estimation in Wireless Sensor Networks, Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 7, no. 2, pp 261-274, 2019
- Carolina Del-Valle-Soto , Carlos Mex-Perera , Juan Arturo Nolazco-Flores, Ramiro Velázquez and Alberto Rossa-Sierra, “Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols”, Journal of Energies, vol. 13, no. 728, 2020.
- Trupti Mayee Behera, Sushanta Kumar Mohapatra, Umesh Chandra Samal, Mohammad. S. Khan, Mahmoud Daneshmand, and Amir H. Gandomi, “Residual Energy Based Cluster-head Selection in WSNs for IoT Application”, IEEE Internet of Things Journal,vol.6, no.3, pp. 5132-5139, 2019.
- Zhao, L., Qu, S. & Yi, Y. “A modified cluster-head selection algorithm in wireless sensor networks based on LEACH”, Journal of Wireless Communication Network, vol. 1, no. 287, 2018.
- Concepts and Contributions of Edge Computing in Internet of Things (IoT): A Survey
Abstract Views :295 |
PDF Views:0
Authors
Affiliations
1 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
2 Department of Information Science and Technology, Anna University, CEG, Chennai, Tamil Nadu, IN
3 Department of Computer Applications, Dr. M.G.R Educational and Research Institute, Chennai, Tamil Nadu, IN
1 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
2 Department of Information Science and Technology, Anna University, CEG, Chennai, Tamil Nadu, IN
3 Department of Computer Applications, Dr. M.G.R Educational and Research Institute, Chennai, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 5 (2020), Pagination: 146-156Abstract
Edge has become a growing trend in recent years. Bringing computing and analytics remarkably close to the data where it originated is the leading cause of edge computing. As the data is growing day by day, there arises the bottleneck in computation and network layers. Due to the enormous growth of Internet of Things (IoT) devices with its recent applications, the need for real-time computation has readily driven edge computing. Today data processing is an excellent paradigm for real-time data. In the integration of various IoT devices to solve the computing perplexities, created the emergence of the Edge computing. This paper clarifies concepts and contributions of edge computing associated with IoT devices. The proposed work produces a thumbnail survey on edge computing and its performance management towards IoT devices. The characteristics and architecture of Edge computing over IoT devices are furnished. The state-of-the-art on edge computing applications in the real-time scenario is discussed in this article. The proposed work explores the key benefits of Edge computing towards IoT devices, along with the comparative principles of edge computing over the Cloud, are represented. The existing challenges of edge computing are also discussed in this work.Keywords
Edge Computing, IoT Devices, Data Processing, Performance Computing.Full Text
References
- Wang, R., Yan, J., Wu, D., Wang, H. and Yang, Q., 2018. Knowledge-centric edge computing based on virtualized D2D communication systems. IEEE Communications Magazine, 56(5), pp.32-38.
- Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F. and Liu, L., 2014. Service level agreement based energy-efficient resource management in cloud data centers. Computers & Electrical Engineering, 40(5), pp.1621-1633.
- Coady, Y., Hohlfeld, O., Kempf, J., McGeer, R. and Schmid, S., 2015. Distributed cloud computing: Applications, status quo, and challenges. ACM SIGCOMM Computer Communication Review, 45(2), pp.38-43.
- Satyanarayanan, M., 2017. The emergence of edge computing. Computer, 50(1), pp.30-39.
- Satyanarayanan, M., Bahl, P., Caceres, R. and Davies, N., 2009. The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4), pp.14-23.
- Elkhatib, Y., Porter, B., Ribeiro, H.B., Zhani, M.F., Qadir, J. and Rivière, E., 2017. On using micro-clouds to deliver the fog. arXiv preprint arXiv:1703.00375.
- Peng, M. and Zhang, K., 2016. Recent advances in fog radio access networks: Performance analysis and radio resource allocation. IEEE Access, 4, pp.5003-5009.
- Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M. and Ayyash, M., 2015. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4), pp.2347-2376.
- Evans, D., 2011. The internet of things: How the next evolution of the internet is changing everything. CISCO white paper, 1(2011), pp.1-11.
- Chiang, M. and Zhang, T., 2016. Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), pp.854-864.
- Ganz, F., Puschmann, D., Barnaghi, P. and Carrez, F., 2015. A practical evaluation of information processing and abstraction techniques for the internet of things. IEEE Internet of Things journal, 2(4), pp.340-354.
- Razzaque, M.A., Milojevic-Jevric, M., Palade, A. and Clarke, S., 2015. Middleware for internet of things: a survey. IEEE Internet Things Journal. 3(1), 70–95 (2016).
- Brogi, A. and Forti, S., 2017. QoS-aware deployment of IoT applications through the fog. IEEE Internet of Things Journal, 4(5), pp.1185-1192.
- Chen, M., Li, W., Hao, Y., Qian, Y. and Humar, I., 2018. Edge cognitive computing based smart healthcare system. Future Generation Computer Systems, 86, pp.403-411.
- Zhang, Q., Yu, Z., Shi, W. and Zhong, H., 2016, October. Demo abstract: Evaps: Edge video analysis for public safety. In 2016 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 121-122). IEEE.
- 16. Khan, L.U., Yaqoob, I., Tran, N.H., Kazmi, S.A., Dang, T.N. and Hong, C.S., 2020. Edge computing enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal.DOI: 10.1109/JIOT.2020.2987070
- Stojkoska, B.R. and Trivodaliev, K., 2017, November. Enabling internet of things for smart homes through fog computing. In 2017 25th Telecommunication Forum (TELFOR) (pp. 1-4). IEEE.
- Olaniyan, R., Fadahunsi, O., Maheswaran, M. and Zhani, M.F., 2018. Opportunistic Edge Computing: Concepts, opportunities, and research challenges. Future Generation Computer Systems, 89, pp.633-645.
- Kim, Y. and Huh, E.N., 2019. EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes. Applied Sciences, 9(12), p.2560.
- Liu, Y., Xu, C., Zhan, Y., Liu, Z., Guan, J. and Zhang, H., 2017. Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach. Computer Networks, 129(2), pp.399-409.
- Yang, J., Lu, Z. and Wu, J., 2018. Smart-toy-edge-computing-oriented data exchange based on blockchain. Journal of Systems Architecture, 87, pp.36-48.
- Zhao, Z., Min, G., Gao, W., Wu, Y., Duan, H. and Ni, Q., 2018. Deploying edge computing nodes for large-scale IoT: A diversity aware approach. IEEE Internet of Things Journal, 5(5), pp.3606-3614.
- Weisong, S., Xingzhou, Z., Yifan, W. and Qingyang, Z., 2019. Edge computing: state-of-the-art and future directions. Journal of Computer Research and Development, 56(1), p.69.
- Weisong, S., Hui, S., Jie, C., Quan, Z. and Wei, L., 2017. Edge computing—An emerging computing model for the Internet of everything era. Journal of Computer Research and Development, 54(5), p.907-924
- Wang, Y., Liu, M., Zheng, P., Yang, H. and Zou, J., 2020. A smart surface inspection system using faster R-CNN in cloud-edge computing environment. Advanced Engineering Informatics, 43, p.101037.
- Shi, W., Cao, J., Zhang, Q., Li, Y. and Xu, L., 2016. Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), pp.637-646.
- Satyanarayanan, M., 2017. The emergence of edge computing. Computer, 50(1), pp.30-39. [28] Green, J., 2014. The internet of things reference model. In Internet of Things World Forum (pp. 1-12).
- Sun, X. and Ansari, N., 2016. EdgeIoT: Mobile edge computing for the Internet of Things. IEEE Communications Magazine, 54(12), pp.22-29.
- Alrawais, A., Alhothaily, A., Hu, C. and Cheng, X., 2017. Fog computing for the internet of things: Security and privacy issues. IEEE Internet Computing, 21(2), pp.34-42.
- Kang, J., Yu, R., Huang, X. and Zhang, Y., 2017. Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 19(8), pp.2627-2637.
- Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J. and Polakos, P.A., 2017. A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 20(1), pp.416-464.
- Jararweh, Y., Doulat, A., AlQudah, O., Ahmed, E., Al-Ayyoub, M. and Benkhelifa, E., 2016, May. The future of mobile cloud computing: integrating cloudlets and mobile edge computing. In 2016 23rd International conference on telecommunications (ICT) (pp. 1-5). IEEE.
- Li, H., Ota, K. and Dong, M., 2018. Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE network, 32(1), pp.96-101.
- Zissis, D. and Lekkas, D., 2012. Addressing cloud computing security issues. Future Generation computer systems, 28(3), pp.583-592.
- Liang, H., Huang, D., Cai, L.X., Shen, X. and Peng, D., 2011, April. Resource allocation for security services in mobile cloud computing. In 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 191-195). IEEE.
- Wu, Y., Liu, Y., Ahmed, S.H., Peng, J., and Abd El-Latif, A.A., 2019. Dominant Data Set Selection Algorithms for Electricity Consumption Time-Series Data Analysis Based on Affine Transformation. IEEE Internet of Things Journal, 7(5), pp.4347-4360.
- Taleb, T., Dutta, S., Ksentini, A., Iqbal, M. and Flinck, H., 2017. Mobile edge computing potential in making cities smarter. IEEE Communications Magazine, 55(3), pp.38-43.
- Cao, J., Ren, L., Shi, W. and Yu, Z., 2014, October. A framework for component selection in collaborative sensing application development. In 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing (pp. 104-113). IEEE.
- Debauche, O., Mahmoudi, S., Mahmoudi, S.A., Manneback, P. and Lebeau, F., 2020. A new edge architecture for ai-iot services deployment. Procedia Computer Science, 175, pp.10-19.