Journal of Cybersecurity and Information Management

This paper proposes a privacy-preserving federated learning approach to enhance cyber threat intelligence sharing. Cyber threats are becoming more sophisticated and are posing serious security risks to organizations. Sharing threat intelligence information can help to detect and mitigate these threats quickly. However, privacy concerns and data protection regulations hinder the sharing of sensitive information. Federated learning is a promising approach that allows multiple parties to collaborate in building a global model while preserving data privacy. We propose a framework that utilizes federated learning to train a global threat intelligence model without compromising the privacy of individual organizations' data. Our approach also includes a differential privacy mechanism to ensure the anonymity of the participating organizations. We demonstrate the effectiveness of our approach through experiments conducted on real-world datasets, showing that it achieves high accuracy while maintaining data privacy. The proposed approach has the potential to facilitate more effective and secure cyber threat intelligence sharing among organizations.

Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our experimental results show that the combination of facial landmarks detection and frequency analysis provides the best performance in detecting deepfake videos, with an accuracy of over 95%. Our findings suggest that machine learning algorithms can be a powerful tool in detecting deepfake videos, and feature extraction techniques play a crucial role in achieving high accuracy.

One of the most significant uses of the Internet of Things is military infiltration detection (IoT). Autonomous drones play a major role in IoT-based vulnerability scanning (UVs). By relocating UVs remotely, this work introduces a new algorithm called the Moth-Flame Optimization Algorithm (MFO). In particular, MFO is used to proactively manage UVs under various scenarios and under different intrusion-covering situations. According to actual studies, the suggested algorithm is both profitable and efficient.

Phishing is a familiar kind of cyberattack in the present digital world. Phishing detection with maximum performance accuracy and minimum computational complexity is continuously a topic of much interest. A novel technology was established for improving the phishing detection rate and decreasing computational constraints recently. But, one solution has inadequate for addressing every problem due to attackers from cyberspace. Thus, the initial objective of this work is for analysing the performance of different deep learning (DL) techniques from detection phishing activity. This study introduces a novel Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification (BSOLSTM-PWC) for Cybersecurity. The proposed BSOLSTM-PWC technique enables to accomplish cybersecurity by the identification and classification of phishing webpages. To accomplish this, the BSOLSTM-PWC technique initially employs data pre-processing technique to transform the data into actual format. Besides, the BSOLSTM-PWC technique employs LSTM classifier for the identification and categorization of phishing webpages. Moreover, the BSO algorithm is utilized to appropriately adjust the hyperparameters involved in the LSTM model. For reporting the improved outcomes of the BSOLSTM-PWC method, a wide-ranging simulation analysis is made using benchmark dataset. The experimental outcomes reported the enhanced outcomes of the BSOLSTM-PWC method on existing methods.

Safety and security risks to critical infrastructure organizations are well known, and incidents in both fields have taken place. To help critical infrastructure organizations manage these areas, safety and security standards have been created.  The main aim of this paper is to present a framework that has been created to manage both safety and security by providing guidance on how to create a Safety and Security Management System (SSMS).   The framework identifies and remediates conflicts and issues between IT, OT, safety, and security. While also creating processes that can combine safety and security compliance to standards to reduce duplication of work and allow one process to manage both areas. A survey was carried out to understand if the framework would be of use to organizations and to better understand the issues users have with managing safety and security and how they manage conflicts that can occur.  The survey showed key areas of concern for organizations and how the framework can be of use to them.  It identified six themes from the research and identified improvements opportunities for the framework that can be implemented.