Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. Further to this, they are still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art understanding addressing the open problems to be tackled and available solutions to be adopted. Furthermore, it investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. Ultimately, it concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level, thus operating in a transparent, continuous and user-friendly manner.

In wealthy developing countries like the Kingdom of Saudi Arabia (KSA), the adoption of cloud computing is progressing slowly compared to the developed countries. To accelerate the cloud computing adoption, this study takes place exploring and investigating the associated security factors with cloud computing that influence organisations' desire for adopting the services of cloud computing. This exploratory study has been conducted through two steps: (1) develop a framework based on security factors discussed in related studies and (2) validate the security factors' relationships within the developed security framework. In the validation study, an instrument was distributed to 217 IT experts in different Saudi government organisations. Correlation analysis is used to explore the relationship(s) among items and factors. The results suggested that the security factors were significantly correlated with each other. The internal consistency reliability analysis results were great. Worthy Cronbach's alpha results also indicated that the items used to measure each factor were independent measures, which are positivity correlated with one another. The key findings of the exploratory factor analysis revealed three main components named as security benefits, security risks, and security awareness. Overall, the implications of this exploratory study provide a significant contribution towards cloud adoption in Saudi organisations. The outcomes of this study will serve as valuable information for policymakers, practitioners, and researchers.

Ethnic conflicts frequently lead to violations of human rights, such as genocide and crimes against humanity, as well as economic collapse, governmental failure, environmental problems, and massive influxes of refugees. Many innocent people suffer as a result of violent ethnic conflict. People’s ethnicity can pose a threat to their safety. There have been many studies on the topic of how to categorize people by race. Until recently, the majority of the work on face biometrics had been conducted on the problem of person recognition from a photograph. However, other softer biometrics such as a person’s age, gender, race, or emotional state are also crucial. The subject of ethnic classification has many potential uses and is developing rapidly. This study summarizes recent advances in ethnicity categorization by utilizing efficient models of convolutional neural networks (CNNs) and focusing on the central portion of the face alone. This article contrasts the results of two distinct CNN models. To put the suggested models through their paces, the study employed holdout testing on the MORPH and FERET datasets. It is essential to remember that this study’s results were generated by focusing on the face’s central region alone, which saved both time and effort. Classification into four classes was achieved with an accuracy of 85% using Model A and 86% using Model B. Consequently, classifying people according to their ethnicity as a fundamental part of the video surveillance systems used at checkpoints is an excellent concept. This categorization statement may also be helpful for picture-search queries.