As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade’s Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.

As a result of the globalisation of the semiconductor design and fabrication processes, integrated circuits are becoming increasingly vulnerable to malicious attacks. The most concerning threats are hardware trojans. A hardware trojan is a malicious inclusion or alteration to the existing design of an integrated circuit, with the possible effects ranging from leakage of sensitive information to the complete destruction of the integrated circuit itself. While the majority of existing detection schemes focus on test-time, they all require expensive methodologies to detect hardware trojans. Off-the-shelf approaches have often been overlooked due to limited hardware resources and detection accuracy. With the advances in technologies and the democratisation of open-source hardware, however, these tools enable the detection of hardware trojans at reduced costs during or after production. In this manuscript, a hardware trojan is created and emulated on a consumer FPGA board. The experiments to detect the trojan in a dormant and active state are made using off-the-shelf technologies taking advantage of different techniques such as Power Analysis Reports, Side Channel Analysis and Thermal Measurements. Furthermore, multiple attempts to detect the trojan are demonstrated and benchmarked. Our simulations result in a state-of-the-art methodology to accurately detect the trojan in both dormant and active states using off-the-shelf hardware.

This paper reviews the development and application architecture of an expert system to assist the Mauritian population with queries they may have about labor or employment law. The expert system makes use of Machine Learning, Speech Recognition/Synthesis and Natural Language Processing techniques to converse with users through a web interface. The expert system also takes advantage of a large knowledge base, that allows the system to teach itself employment law principles. The knowledge base is created from "Understanding Employment Law and Remuneration Orders in Mauritius", written by Ved Prakash Torul [1], which is a simplified version of the Employment Relations Act and the Employment Rights Act. The book explains employment law in common language, to help the public understand their constitutional rights. The expert system allows users to communicate and express their employment issues, so that they are aware of their next course of action, either they are an employer, employee, or a union. The paper also reviews the evaluation period, which consisted of a preliminary testing period. Through the evaluation, it was concluded that the expert system was able to respond to individual responses with a Precision of 66% and Recall of 85%. While the Expert System is able to converse with users on certain topics on Employment Law, further evaluation would need to be conducted. Additionally, the knowledge base will need to be updated over time.

Positive public administration is the call to focus research on positive examples from which other public organizations can learn. One aspect of achieving positive public value is sensegiving; the influencing of people’s sensemaking in uncertain situations toward a preferred outcome. Such sensegiving has been proven essential in public contexts, especially when dealing with many stakeholders. But how does sensegiving succeed in multicultural contexts with a high risk of misunderstanding, mistrust, and conflict? This chapter addresses this question with a single case study about the implementation of the new Light Rail Transit System in Mauritius; a country known for its cultural diversity and multi-ethnic society. Through the analysis of various collected qualitative data, it identifies the role of cultural chameleonizing as a moderator that can foster successful sensegiving in multicultural contexts. The findings contribute to PPA and sensegiving literature by highlighting mechanisms for better management in an increasingly diverse world.