Crypto-Semantic Method of Text Data Protection

A method of text data protection called crypto-semantic is proposed. In order to implement the method within the formally defined restrictions of the selected sphere of applied uses, it is necessary to develop a corresponding lexicographical system in the form of an applied linguistic corpus and semantically structure the information using the constructed linguistic corpus so that the encrypted text message samples present semantically plausible text fragments. Under certain conditions the method provides absolute guarantee of text data protection from confidentiality compromise.

Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment

Internet of Things (IoT) is an emerging technology and its applications are flattering amidst many users, as it makes everything easier. As a consequence of its massive growth, security and privacy are becoming crucial issues where the IoT devices are perpetually vulnerable to cyber-attacks. To overcome this issue, intrusion detection and mitigation is accomplished which enhances the security in IoT networks. In this paper, we proposed Blockchain entrenched Bi-level intrusion detection and graph based mitigation framework named as HybridChain-IDS. The proposed work embrace four sequential processes includes time-based authentication, user scheduling and access control, bi-level intrusion detection and attack graph generation. Initially, we perform time-based authentication to authenticate the legitimate users using NIK-512 hashing algorithm, password and registered time are stored in Hybridchain which is an assimilation of blockchain and Trusted Execution Environment (TEE) which enhances data privacy and security. After that, we perform user scheduling using Cheetah Optimization Algorithm (COA) which reduces the complexity and then the access control is provided to authorized users by smart contract by considering their trust and permission level. Then, we accomplish bi-level intrusion detection using ResCapsNet which extracts sufficient features and classified effectively. Finally, risk of the attack is evaluated, and then the attacks graphs are generated by employing Enhanced k-nearest neighbor (KNN) algorithm to identify the attack path. Furthermore, the countermeasures are taken based on the attack risk level and the attack graph is stored in Hybridchain for eventual attack prediction. The implementation of this proposed work is directed by network simulator of NS-3.26 and the performance of the proposed HybridChain-IDS is enumerated based on various performance metrics