Journal of Cybersecurity and Information Management

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2690-6775ISSN (Online) 2769-7851ISSN (Print)

An Energy Efficient Clustering Protocol using Enhanced Rain Optimization Algorithm in Mobile Adhoc Networks

Mohamed Elhoseny , X. Yuan

Energy efficiency is a significant challenge in mobile ad hoc networks (MANETs) design where the nodes move randomly with limited energy, leading to acceptable topology modifications. Clustering is a widely applied technique to accomplish energy efficiency in MANET. Therefore, this paper designs a new energy-efficient clustering protocol using an enhanced rain optimization algorithm (EECP-EROA) for MANET. The EROA technique is derived by integrating the Levy flight concept to the ROA to enhance global exploration abilities. In addition, the EECP-EROA technique intends to proficiently select CHs and the nearby nodes linked to the CH to generate clusters. Moreover, the EECP-EROA technique has derived an objective function with different input parameters. To showcase the superior performance of the EECP-EROA technique, a brief set of simulations takes place, and the results are inspected under varying aspects. The experimental values pointed out the betterment of the EECP-EROA technique over the other methods.

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Vol. 7 Issue. 2 PP. 85-94, (2021)

BioPay: A Secure Payment Gateway through Biometrics

Gurpreet Singh.*, Divyanshi Kaushik, Hritik Handa, Gagandeep Kaur, Sunil Kumar Chawla , Ahmed A. Elngar

Due to emerging technological developments, major enhancements are taking place in the area of a secure and quick transaction. BioPay being a secure payment method is a one-step ahead. In the proposed methodology, there is no involvement of any credit or debit card or any other account information like OTP or CVV; it solely depends upon some unique identifying characteristic of a human known as biometrics. This work proposes a novel method that allows users to complete transactions quickly and securely using face and finger recognition. The transaction initiates with scanning face features and matching it with the database which in turn retrieves all the information associated with that customer account. After that, the system will scan the fingerprints of the subject and verify the transaction. This methodology can be implemented in ATMs and smartphones resulting in enhanced security and flexibility for payment purposes. 

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Vol. 7 Issue. 2 PP. 65-76, (2021)

An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security

Mustafa s Khalifa , Ahmed N. Al-Masri

Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.

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Vol. 7 Issue. 2 PP. 77-84, (2021)

An Artificial Intelligence-based Intrusion Detection System

Thani Almuhairi , Ahmad Almarri , Khalid Hokal

Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.

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Vol. 7 Issue. 2 PP. 95-111, (2021)