Journal of Cyber Security and Mobility – Best Paper Awards

We are very pleased to announce the Best Paper Awards from the Journal of Cyber Security and Mobility!

The Journal of Cyber Security and Mobility, published by River Publishers, is an international, open-access, peer-reviewed journal publishing original research, review, and tutorial papers across all cybersecurity fields—including information security, computer and network security, cryptography, and digital forensics—as well as interdisciplinary articles addressing privacy, ethical, legal, and economic aspects of cybersecurity, along with emerging solutions inspired by other scientific disciplines, such as nature-inspired approaches.

Author: Peng Xiao
Title: Malware Cyber Threat Intelligence System for Internet of Things (IoT) Using Machine Learning
Abstract: Cyber Intelligence (CI) is a sophisticated security solution that uses machine learning models to protect networks against cyber-attack. Security concerns to IoT devices are exacerbated because of their inherent weaknesses in memory systems, physical and online interfaces, and network services. IoT devices are vulnerable to attacks because of the communication channels. That raises the risk of spoofing and Denial-of-Service (DoS) attacks on the entire system, which is a severe problem. Since the IoT ecosystem does not have encryption and access restrictions, cloud-based communications and data storage have become increasingly popular. An IoT-based Cyber Threat Intelligence System (IoT-CTIS) is designed in this article to detect malware and security threads using a machine learning algorithm. Because hackers are continuously attempting to get their hands on sensitive information, it is important that IoT devices have strong authentication measures in place. Multifactor authentication, digital certificates, and biometrics are just some of the methods that may be used to verify the identity of an Internet of Things device. All devices use Machine Learning (ML) assisted Logistic Regression (LR) techniques to address memory and Internet interface vulnerabilities. System integrity concerns, such as spoofing and Denial of Service (DoS) attacks, must be minimized using the Random Forest (RF) Algorithm. Default passwords are often provided with IoT devices, and many users don’t bother to change them, making it simple for cybercriminals to get access. In other instances, people design insecure passwords that are easy to crack. The results of the experiments show that the method outperforms other similar strategies in terms of identification and wrong alarms. Checking your alarm system’s functionality both locally and in terms of its connection to the monitoring centre is why you do it. Make sure your alarm system is working properly by checking it on a regular basis. It is recommended that you do system tests at least once every three months. The experimental analysis of IoT-CTIS outperforms the method in terms of accuracy (90%), precision (90%), F-measure (88%), Re-call (90%), RMSE (15%), MSE (5%), TPR (89%), TNR (8%), FRP (89%), FNR (8%), Security (93%), MCC (92%).

Authors: Usman Rauf, Fadi Mohsen, Zhiyuan Wei
Title: A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions & Recommendations
Abstract: In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help understand the attributes that play crucial roles in insider threat detection. In addition, they can be beneficial for testing the newly designed security solutions in this domain. We also present recommendations for establishing a baseline standard for analyzing insider-threat data sets. This baseline standard could be used in the future to design resilient architectures and provide a road map for organizations to enhance their defense capabilities against insider threats.

Congratulations to the authors for their exceptional work advancing cybersecurity and mobility research!