Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain

A. Oommen Philip 1  and RA K Saravanaguru 2
  • 1 School of Computing Science and Engineering, , Vellore, India
  • 2 School of Computing Science and Engineering, , Vellore, India

Abstract

Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [2] Kaiwartya O., et al., "Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects," in IEEE Access, 2016, vol. 4, pp. 5356-5373

  • [3] Philip A O., Saravanaguru RA.K., “A Vision of Connected and Intelligent Transportation Systems”, International Journal of Civil Engineering and Technology, Volume 9, Issue 2, February 2018, pp. 873–882

  • [4] Wu Y., Abdel-Aty M., Lee J., “Crash risk analysis during fog conditions using real-time traflc data, Accident Analysis & Prevention”, May 30 2017, Volume 114, pp. 4-11

  • [5] Wang Y., Zhang W., “Analysis of Roadway and Environmental Factors Affecting Traflc Crash Severities”, Transportation Research Procedia, 2017, Vol 25, pp. 2124-2130

  • [6] Delen D., Tomak L., Topuz K., Eryarsoy E., “Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods”, Journal of Transport & Health, March 2017, vol 4, pp. 118-131

  • [7] Li L., Shrestha S., Hu G., “Analysis of road traflc fatal accidents using data mining techniques”, IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), London, 2017, pp. 363-370

  • [8] Chen C., Zhang G., Tarefder R., Ma J., Wei, H., Guan H., “A multi-nomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes”, Accident Analysis and Prevention, 2015, vol 80, pp. 76–88

  • [9] Sun P., Guo G., Yu R., Traflc crash prediction based on incremental learning algorithm, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, 2017, pp. 182-185

  • [10] Park S-h., Kim S-m., Ha Y-g., “Highway traflc accident prediction using VDS big data analysis”, The Journal of Supercomputing, July 2016, Volume 72, Issue 7, pp. 2815–2831

  • [11] Ghimire B., Bhattacharjee S., Ghosh SK., "Analysis of Spatial Autocorrelation for Traflc Accident Data Based on Spatial Decision Tree”, Fourth International Conference on Computing for Geospatial Research and Application, 2013, San Jose, CA, pp. 111-115

  • [12] Dong C., Shao C., Li J., Xiong Z., “An Improved Deep Learning Model for Traflc Crash Prediction”, Journal of Advanced Transportation. 2018, pp. 1-13

  • [13] Yu R., Abdel-Aty M., “Utilizing support vector machine in real-time crash risk evaluation”, Accident Analysis & Prevention, 2013 vol. 51, pp. 252–259

  • [14] Tang et al., "ChainFS: Blockchain-Secured Cloud Storage", 2018, IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, 2018, pp. 987-990

  • [15] Wang S., Zhang Y., Zhang Y., "A Blockchain-Based Framework for Data Sharing with Fine-Grained Access Control in Decentralized Storage Systems," in IEEE Access, 2018, vol. 6, pp. 38437-38450

  • [16] Es-Samaali H., Outchakoucht A., Leroy J P., “A blockchain-based access control for big data,” Int. J. Computer Networks Communication and Security, July 2017 vol. 5, no. 7, pp. 137–147

  • [17] Zhou L., Wang L., Sun Y., Lv P., "BeeKeeper: A Blockchain-Based IoT System with Secure Storage and Homomorphic Computation," in IEEE Access, 2018, vol. 6, pp. 43472-43488

  • [18] Dorri A., Kanhere S. S., Jurdak R., “Towards an Optimized BlockChain for IoT,” IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), 2017, Pittsburgh, PA, pp. 173-178

  • [19] Ouaddah A., Elkalam A. A., Ouahman A. A., “Towards a novel privacy-preserving access control model based on blockchain technology in IoT”, In book: Europe and MENA Cooperation Advances in Information and Communication Technologies Springer, Sept 2017, pp. 523–533

  • [20] Liu B., Liu B., Yu X. L., Chen S., Xu X., Zhu L., "Blockchain Based Data Integrity Service Framework for IoT Data", IEEE International Conference on Web Services (ICWS), 2017, Honolulu, HI, pp. 468-475

  • [21] Do H. G., Ng W. K., "Blockchain-Based System for Secure Data Storage with Private Keyword Search", IEEE World Congress on Services (SERVICES), 2017, Honolulu, HI, pp. 90-93

  • [22] Zyskind G., Nathan O., Pentland A S., "Decentralizing Privacy: Using Blockchain to Protect Personal Data", IEEE Security and Privacy Workshops, 2015, San Jose, CA, pp. 180-184

  • [23] Cebe M., Erdin E., Akkaya K., Aksu H., Uluagac S., "Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles", in IEEE Communications Magazine, October 2018, vol. 56, no. 10, pp. 50-57

  • [24] Nilsson D. K., Larson U. E., “Conducting Forensic Investigations of Cyber Attacks on Automobile In-Vehicle Networks”, Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop, Jan 2008, article 8, pp. 1-6

  • [25] Bethencourt J., Sahai A., Waters B., “Ciphertext-Policy Attribute-Based Encryption”, IEEE Symposium on Security and Privacy (SP ’07), 2007, Berkeley, pp. 321-334

OPEN ACCESS

Journal + Issues

Search