The dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit smart application vulnerabilities significantly increases. Therefore, improving smart application security during the software development process is mandatory to ensure sustainable smart cities. But the challenge is how to adopt security practices in the software development process. There are Several established and mature security testing frameworks exist that consider security requirements and testing during Several already established and mature security testing frameworks exist that consider security requirements and testing during Software Development Life Cycle (SDLC), but there is a unique challenges posed by smart city applications and the need for a comprehensive approach to address the evolving threat landscape in this context. This paper proposed a framework that adopts security testing practices in all phases of the software development process. The proposed framework identifies several security activities and steps that can be applied in each phase of the software development process.

Despite the significant benefits that the Internet of Healthcare Things (IoHT) offered to the medical sector, there are concerns and risks regarding these systems which can delay their wide deployment as they handle sensitive and often life-critical medical information. In addition to the IoHT concerns and risks, there are security constraints which include hardware, software, and network constraints that pose a security challenge to these systems. Therefore, security measures need to be deployed that can overcome the concerns, mitigate the risks, and meet the constraints of the IoHT. For these reasons, a subclasses intrusion detection system for the IoHT is proposed in this research work based on a novel variation of the standard one-class support vector machine (OSVM), namely, deep subclass dispersion OSVM (Deep SDOSVM), which considers subclasses in the target class, i.e., normal class, in order to minimize the data dispersion within and between subclasses, thereby improving the discriminative power and classification performance of the intrusion detection system. A deep clustering model is used for subclasses generation in the proposed Deep SDOSVM approach, namely, the dynamic autoencoder model (DynAE), to overcome the drawbacks of the classical clustering algorithms and further enhance the classification performance of the intrusion detection system. The proposed deep clustering subclasses intrusion detection system was evaluated on the real-world TON_IoT data set and compared to other state-of-the-art one-class classifiers. Experimentation results have shown that the proposed approach outperformed the other relevant one-class classifiers for network intrusion detection.

Over the years, there have been many factors that have had an influence on the landscape of higher education in the UK. These factors include the rise of tuition fees, the introduction of the teaching excellence framework and the formation of office for students. A key performance indicator that has an impact on these factors is student experience, which is influenced by positive or negative feedback and engagement. Although this forms a key part of the learning environment, it is still perceived as one of the weakest aspects when it comes to enhancing the student experience especially for the students with learning difficulties. During the recent pandemic, significant levels of changes have been introduced to the teaching and learning approaches. Machine learning approaches are proven useful for providing flexible solutions for various problems in different fields. With the focus on the students with learning difficulties; this paper proposed a machine learning based approach to support such students and analyse the complexities of their learning difficulties to make sure they benefit from the new approaches in the post-pandemic era. The proposed approach has been tested initially for dyslexia, where the main complexities such as recognition of words, fluency in reading and writing are analysed. This research will lead to the novel introduction of intelligent approach to revolutionize the learning ability and overcome any learning difficulties at different learning and teaching levels.

Abstract In the last few decades, Intrusion Detection System (IDS), in particular, machine learning‐based anomaly detection, has gained importance over Signature Detection Systems (SDSs) in the novel attacks detection. Herein, a novel approach called T‐Distributed Stochastic Neighbour Embedding and Random Forest Algorithm (T‐SNERF) is presented for the classification of cyber‐attacks. The approach consists of three different steps. First, the examination of feature correlations is provided. Second, the T‐Distributed Stochastic Neighbour Embedding (T‐SNE) data dimensional reduction technique is used. Third, Random Forest (RF) technique is utilised to evaluate the complications in the accuracy and False‐Positive Rate (FPR). The proposed approach has been tested on various well‐known datasets, namely, UNSW‐NB 15, CICIDS‐2017, and phishing datasets. The proposed novel approach achieved significant results compared with existing approaches, achieving 100% accuracy, and 0% FPR for the UNSW‐NB15 dataset, and achieving high accuracy rates, up to 99.7878%, and 99.7044%, for CICIDS‐2017 and Phishing datasets respectively.

Code smells are indicators of inappropriate and possibly harmful design decisions that could lead to issues in the comprehensibility and maintainability of software systems. To avoid such quality complications, understanding the presence and prioritising the removal of code smells are required. This study presents a visualisation approach to help better understanding the evolutional characteristics of code smells presented in the different versions of the software system. The core of the visualisation approach is the metaphor of buildings and building blocks. An overall framework for detecting, categorising and visualising code smells is proposed. Three types of code smells were considered in this study. The considered code smells are God Class, Long Method and Type Checking. The applicability of the proposed approach is demonstrated by evaluating several versions of an open‐source java software and visualising the detected code smells. Additionally, a pilot experimental study is conducted to empirically assure the usefulness of the proposed visualisations.