The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request.

Cyberbullying on social media is a significant public health concern. This paper systematically reviews the existing literature on cyberbullying to provide a clearer understanding of how it is defined and reported in terms of prevalence and impact. Utilizing the PRISMA search strategy, we examined 71 papers published from 2007 to 2022, offering a comprehensive synthesis of the field’s current understanding. Our findings highlight notable inconsistencies in the definition of cyberbullying across studies, underlining a critical need for a standardized conceptual framework. Additionally, while cyberbullying is shown to be highly prevalent among personalities exhibiting traits of Machiavellianism, psychopathy, and narcissism, our review identifies a crucial research gap: the underexploration of cyberbullying among adult populations. This review synthesizes the breadth of research on cyberbullying and highlights gaps in the existing literature. We have included our proposed standardized definition of cyberbullying.

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite the obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks to pose significant threats to the confidentiality, integrity and availability of data in WSNs. In this paper, we investigate two computational intelligence techniques for WSN intrusion detection. A back propagation neural network is compared with a support vector machine classifier. Using the NSL-KDD dataset, detection rates achieved by the two techniques for six cyber attacks are recorded. The results show that both techniques offer a high true positive rate and a low false positive rate, making both good options for intrusion detection. In addition, we further show the support vector machine classifiers suitability for anomaly detection, by demonstrating its ability to handle low sample sizes, whilst maintaining an acceptable FPR rate under the required threshold

The growth of the Internet of Things (IoT), and demand for low-cost, easy-to-deploy devices, has led to the production of swathes of insecure Internet-connected devices. Many can be exploited and leveraged to perform large-scale attacks on the Internet, such as those seen by the Mirai botnet. This paper presents a cross-sectional study of how users value and perceive security and privacy in smart devices found within the IoT. It analyzes user requirements from IoT devices, and the importance placed upon security and privacy. An experimental setup was used to assess user ability to detect threats, in the context of technical knowledge and experience. It clearly demonstrated that without any clear signs when an IoT device was infected, it was very difficult for consumers to detect and be situationally aware of threats exploiting home networks. It also demonstrated that without adequate presentation of data to users, there is no clear correlation between level of technical knowledge and ability to detect infected devices.

The following topics are dealt with: security of data; risk management; decision making; computer crime; invasive software; critical infrastructures; data privacy; insurance; Internet of Things; learning (artificial intelligence).