Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.

The IEEE 802.11i standard has been designed to enhance security in wireless networks. In the 4-way handshake the supplicant and the authenticator use the pairwise master key (PMK) to derive a fresh pairwise transient key (PTK). The PMK is not used directly for security while assuming the supplicant and authenticator have the same PMK before running 4-way handshake. In this paper, the 4-way handshake phase has been analysed using Isabelle tool to identify a new Denial-of-Service (DoS) attack. The attack prevents the authenticator from receiving message 4 after the supplicant sends it out. This attack forces the authenticator to re-send the message 3 until time out and subsequently to de-authenticate supplicant. This paper has proposed improvements to the 4-way handshake to avoid the Denial-of-Service attack.

In cloud computing environment, security becomes a more and more critical issue as users are sharing computing resources remotely. It is important to analyse the potential security challenges and risks clouds may face. In this paper, a number of security challenges, concerns and issues are discussed, including three major aspects of security, namely confidentiality, integrity and availability.

The rapid expansion of Internet of Things (IoT) devices in modern environments introduces significant security vulnerabilities, increasing the risk of cyberattacks and potential physical threats to users. While Network Intrusion Detection Systems based on Machine Learning (ML-based NIDS) hold promise for mitigating these risks, the effectiveness of such systems is heavily influenced by the quality and representativeness of the datasets used for their development and evaluation. This study provides a critical review of publicly available benchmarking datasets commonly used to train ML-based NIDS for IoT security, with a particular focus on two pivotal but often overlooked factors: testbed configurations and feature extraction methods. The study's findings reveal critical inconsistencies in dataset design and feature sets, posing challenges to model generalizability and its real-world application. To address these issues, the study proposes clear criteria for dataset assessment and practical recommendations for researchers and dataset developers. The findings demonstrate the urgent need for standardisation to enhance reproducibility, enable fair comparison of intrusion detection models, and bridge the gap between academic research and practical IoT security solutions.

Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tend to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. In addition, this paper demonstrates the feasibility of using Finite-state machines as a formal method to analyse the proposed protocols.