Due to the increasing integration of distributed energy generation in the electric grid, transactive energy markets (TEMs) have recently emerged to balance the demand and supply dynamically across the grid. TEM enables peer to peer (P2P) energy trading and brings flexibility by reducing users’ demand in the grid. It also enhances the system’s efficiency and reduces the pressure on electricity networks. However, it is vulnerable to major cyber attacks as users equipped with smart devices are participating autonomously in the energy market, and an extensive amount of information is exchanged through the communication channel. The potential attacks and impacts of those attacks need to be investigated to develop an attack resilient TEM-based power system. Hence, in this paper, our goal is to systematically identify possible cyber attacks associated with a TEM-based power system. In order to achieve this goal, we classify the attacks during the P2P and flexibility schemes of TEM into three main categories. Then, we explore the attacks under each category in detail. We further distinguish the adversary roles of each particular attack and see what benefits will be received by an adversary through each specific attack. Finally, we present the impact of the attacks on the market operation, consumers, and prosumers of the TEM in this paper.

Transactive Energy Markets (TEMs) have emerged as a promising approach to balance the demand and supply dynamically across the grid using the increasing integration of distributed energy generation in the electric grid. TEM enables peer to peer (P2P) energy trading and brings flexibility by reducing users' demand in the grid. TEM has various components. Adversaries can target any market component and perform false data injection attacks (FDIAs) either by maliciously acting as a market participant or by exploiting their fragile security mechanism during energy trading. Hence, in this study, we present and evaluate two FDIAs where attackers (1) manipulate the requested flexibility through an attack into flexibility requesters (2) modify forecasting data by executing an FDIA over TEM users' smart devices during the flexibility and P2P market of TEM-based micro-grid systems. We analyze the impact of these attacks on the market operation, prosumers, and consumers by conducting computer simulations. The result shows that adversaries gain significant financial benefits while victims face economic losses. The result also indicates that attackers could severely impact market operations just by injecting a small false demand.

Exploring human trust in artificial intelligence (AI) in Law Enforcement domain is paramount for its ethical and effective deployment. As AI systems become increasingly integrated into society, trust ensures transparency, accountability, and fairness in their deployment. Despite the rapid increase in discussion about AI usage in law enforcement in various sectors globally, no secondary research explores that for OCEANIA, specifically in Australia. Moreover, how humans are considered in AI law enforcement is an uninvestigated area in this region. This paper aims to investigate this gap by systematically exploring various applications used in this region and analyzing how human perspectives are used, and to what extent. We conducted a systematic literature review (SLR) based on 56 qualitatively selected studies to (i) classify the types, frequency, and demography of published research; (ii) explore how AI is leveraged; (iii) identify how and to what extent humans are considered in development and applications of AI in law enforcement; (iv) understand the challenges and solutions reported in this domain and worldwide. In summary, our findings point to the need for solutions to address operational and ethical issues in integrating AI into law enforcement and for unified legislation or regulations to effectively design and utilize AI in this domain. Based on the findings that highlight the important concerns in this domain, we recommend a list of future research directions.

Transactive Energy Market (TEM) operates a day-ahead market for efficient energy management and determines the energy prices in advance for the future period. This predetermined rate is used during real-time energy trading. Hence, it is crucial to provide the correct energy forecast to estimate fair energy prices for all users. However, this market poses a potential opportunity for attackers to manipulate energy prices through False Data Injection Attacks (FDIAs). Moreover, sharing energy forecasts with market operators (MO) or participants violates users’ privacy. To address these issues, this paper first presents two FDIAs to a day-ahead TEM and then numerically evaluates their effects. Then, we design a novel framework to address the trade-off between sharing forecasts and user privacy. We show how users can share the energy forecast with MO and other participants securely while preserving privacy using the developed framework. Finally, we show how our proposed framework detects malicious activities of users (e.g., deviation of actual energy supply/demand from forecast beyond a threshold) and prevent FDIAs effectively.