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2021-01-18
Ibrahim, A. K., Hagras, E. A. A. A., Alfhar, A., El-Kamchochi, H. A..  2020.  Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) for Crypto Images. 2020 2nd International Conference on Computer Communication and the Internet (ICCCI). :119–125.

In this paper, a novel Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) has been introduced for Image Encryption. The biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the chaotic logistic map and hence, a new DCBDI-IEC has been suggested. DCBI-IEC is used to control the key schedule for all encryption and decryption processing. Statistical analysis, differential analysis and key sensitivity test are performed to estimate the security strengths of the proposed DCBI-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level for image encryption application.

2019-06-10
Kumar, A., Aggarwal, A., Yadav, D..  2018.  A Multi-layered Outlier Detection Model for Resource Constraint Hierarchical MANET. 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). :1–7.

For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.