Titlebook: Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities; Sanjay Misra,Amit Kumar Tyagi Book 202

Inexorable

Biometric E-Voting System for Cybersecurity,res of the system and the inherent cost of implementation were estimated adequately. This will guide Independent National Electoral Commission (INEC) in appropriating the allocation of resources. There is also provision for capital and content insurance cover against the fear of manipulation by elec

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Wrapper Based Approach for Network Intrusion Detection Model with Combination of Dual Filtering Tection accuracy of the minority class under the assumption that the overall distribution is unchanged and the information loss of majority samples. Three different decision tree classifiers were used to compute the performance of the selected subset features. Remarkable and outstanding fair comparison

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CAPE

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An Obfuscation Technique for Malware Detection and Protection in Sandboxing,s achieved through heatmaps, scroll maps, attention maps, and keyloggers. Then the data has been converted into text and it is stored in the database. The obtained data has been obfuscated and given for futher process. This is achieved through an AI tool called Delphix. Also this chapter gives an ov

AMOR

Secure Data Sharing with Interplanetary File System for Pharmaceutical Data,raphic Smart Contracts for Device Authentication and User Access control scheme are written in Solidity Language and executed on Remix IDE. Transaction and Execution cost are evaluated for Advanced Encryption Standard (AES) algorithm and Rivest-Shamir-Adelman (RSA) algorithm in terms of ether as gas

Meditative

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Description of a Network Attack Ontology Presented Formally,tégé, an ontology editor. The Attack Scenario class, a core class of the ontology, represents types of network attacks, for example, a Denial of Service attack. The ontology is evaluated by showing three examples of real attacks that are correctly classified by the presented ontology.

Binge-Drinking

A Long Short Term Memory Model for Credit Card Fraud Detection,n programming language. The relevant features selected were then fed into the LSTM-RNN for classification. The results obtained were compared with past work and our fraud model recorded high classification accuracy as well as reduced false alarm rate. It has 99.58% Prediction Accuracy, 99.6% Precisi

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