Online conversations are often disrupted by trolling, causing distress and increased conflict. Previous research has primarily focused on addressing harmful content after it has appeared. However, real-time strategies for managing emotions have not been explored much. This study introduces a comment queuing system that delays emotionally charged comments, encourages self-reflection, and lessens impulsive reactions. A mixed-methods approach was employed to demonstrate that the developed queuing approach can help reduce the spread of negative emotional content while maintaining conversational flow. User feedback indicates that participants believe the queuing mechanism could calm discussions, reduce impulsive comments, and create a more balanced emotional tone in conversations. They expressed interest in seeing it implemented on social media platforms. A strong link was found between users’ typical emotional states while using social media and their perceptions of the delay; calm users found the mechanism beneficial, while frustrated users expected it to cause more frustration.

The automotive industry increasingly relies on Over-the-Air (OTA) updates to deliver essential security patches to vehicles. However, this dependence may introduce significant cy-bersecurity vulnerabilities, particularly concerning the integrity and privacy of updates. This paper presents an integrated framework that combines threat modeling and formal verification by employing an identical system model across all stages. The process begins with applying the ThreatGet tool to identify potential threats in the OTA update process, which directly guide the formulation of formal security requirements expressed as Computation Tree Logic (CTL) properties. The same high-level model encompassing a Cloud Server (CS), Telematics Control Unit (TCU), Central Gateway Unit (CGU), Advanced Driver Assistance System (ADAS) module, and an attacker module is consistently used for both threat modeling and encoding in the NuSMV model checker. This unified approach ensures that identified threats translate seamlessly into verifiable properties. Experimental threat analysis and verification results demonstrate the effectiveness of our integrated approach in uncovering OTA update vulnerabilities properties.

Smart environments are increasingly equipped with interconnected digital systems to manage access and physical security. However, traditional authentication methods, typically restricted to static checkpoints, fail to provide persistent assurance once entry is granted, leaving facilities vulnerable to credential misuse, tailgating, and unauthorised movement. This paper presents the Continuous Authentication Platform (CAP), a modular, multi-modal framework developed within the RAAISE project to enable continuous and context-aware verification across dynamic facility zones. CAP integrates heterogeneous off-the-shelf sensors, including NFC, RFID, biometric, motion, and WiFi positioning units, which collectively support persistent user tracking and real-time access enforcement. The platform’s architecture couples distributed sensing and edge processing with a centralised intelligence layer for event correlation and policy-driven decision-making. A live testbed deployment at Deakin University was used to evaluate CAP’s performance under realistic operational conditions. Results from functional trials demonstrate CAP’s ability to detect credential misuse, prevent tailgating, and maintain authentication continuity with sub-second responsiveness. These findings underscore CAP’s potential as a scalable, privacy-aligned foundation for next-generation smart facility security systems.

Interpersonal communication is key in managing people's emotions on digital platforms. Studies have shown that people use social media to regulate their emotions and find support for rest and recovery. However, these platforms are not designed for emotion regulation (ER), which limits their effectiveness in this regard. To address this, we propose to enhance interpersonal emotion regulation (IER) on online platforms through content recommendation. The objective is to empower users to regulate their emotions while actively or passively engaging in online platforms by crafting media content that aligns with IER strategies, particularly empathic responding. The proposed system aims to facilitate both user-initiated and system-initiated emotion regulation for real-time IER practices. Our mixed-method research includes analyzing 37.5K Reddit posts and a user survey to develop a Contextual Multi-Armed Bandits (CMAB) recommendation system. The experimentation shows that the empathic recommendations generated by the proposed recommendation system are preferred by users over widely accepted ER strategies such as distraction and avoidance.

Technological evolution in the Industrial Internet of Things (IIoT) domain has fostered smart grid systems’ operation, performance, connectivity, and delivery with higher efficiency. However, it has also exposed the platform to a broader surface for attackers. Current information technology (IT)-centric solutions for detecting, preventing, and mitigating attacks have limitations, especially in comprehensively monitoring industrial control operational technology (OT) and communication systems. The rise of sophisticated cyberattacks, such as targeted ransomware, demand more robust security measures, leading to the emergence of zero trust (ZT) deployment as a response to these threats. This article proposes a new framework for implementing ZT comprising both IT and OT in smart grid infrastructures, with multiple security mechanisms and robust system coverage. We present an EigenGame algorithm for integrating diverse data sources into a rich-context format and an enhanced approach to quantum reinforcement learning for reliable malicious behavior detection in IIoT-enabled smart grids. The framework was evaluated using five sets of data from the X-IIoTID dataset, demonstrating its good performance in verifying any behavior inside the system and identifying any malicious behavior related ransomware attacks.

Space anomaly detection plays a critical role in safeguarding the integrity and reliability of space systems amid the rising tide of threats. This survey aims to deepen comprehension of space cyber threats through space threat modeling, and meticulously examine the unique challenges of space anomaly detection. The survey identifies scalability, real-time detection, limited labeled data availability, concept drift, and adversarial attacks as key challenges based on thorough literature analysis and synthesis. By extensively exploring state-of-the-art anomaly detection techniques, the study evaluates their applicability, strengths, and limitations within space networks. Going beyond analysis, a notable contribution of this work involves integrating stream-based and graph-based methods, tailored to capture the intricate temporal and structural relationships inherent in space networks. This innovative hybrid approach holds promise for heightened detection accuracy and sets the stage for future research endeavors. As space threats continue evolving in both number and sophistication, this survey timely provides insights, recommendations, and a clear roadmap for researchers, engineers, and practitioners to fortify space anomaly detection mechanisms.

Federated Learning (FL)-based Intrusion Detection Systems (IDSs) have recently surfaced as viable privacy-preserving solution to decentralized grid zones. However, lack of consideration of communication delays and straggler nodes in conventional synchronous FL hinders their applications within the real-world. To level the playing field, we propose a novel semi-asynchronous FL solution on basis of a preset-cut-off time and a buffer system to mitigate the adverse effects of communication latency and stragglers. Furthermore, we leverage the use of a Deep Auto-encoder model for effective cyberattack detection. Experimental evaluations of our proposed framework on industrial control datasets validate superior attack detection while decreasing the adverse effects of communication latency and straggler nodes. Lastly, we notice a 30% improvement in the computation time in the presence of communication latency/straggler nodes, thus validating the robustness of our proposed method.

Federated Learning (FL)-based Intrusion Detection Systems (IDSs) have recently surfaced as viable privacy-preserving solution to decentralized grid zones. However, conventional synchronous FL methods face technical challenges including the lack of consideration of communication delays and straggler nodes. To level the playing field, we propose a novel power system misbehaviour detection framework that leverages semi-asynchronous federated learning and dynamic aggregation. Specifically, our framework introduces an adaptive learning rate mechanism in the semi-asynchronous FL setting, allowing for efficient model updates and mitigating the impact of stragglers on the training process. Experiments conducted on publicly available Mississippi State University and Oak Ridge National Laboratory Power System Attack (MSU-ORNL PSA) Dataset demonstrate that our adaptive learning semi-asynchronous FL framework achieves superior attack detection rate while safeguarding data confidentiality and minimizing the negative effects of practical world communication latency and straggler nodes. Furthermore, our proposed method shows a significant 40% improvement in training time compared to conventional synchronous FL methods, showcasing the effectiveness and efficiency of our recommended approach.

Cyber-Physical Systems (CPSs) enable Information Technology to be integrated with Operation Technology to efficiently monitor and manage the physical processes of various critical infrastructures. Recent incidents in cyber ecosystems have shown that CPSs are becoming increasingly vulnerable to complex attacks. These incidents often lead to sensing and actuation misbehaviour by illegal manipulations of data, which can severely impact the underlying physical processes of critical infrastructures. Current research acknowledges that IT-based security measures cannot entirely protect CPSs from such threats. Moreover, they are not designed to monitor the measurement level activities of physical processes, and they fail to mitigate blended cyberattacks, especially multi-stage and zero-day ones. This article addresses these limitations by proposing a framework, named UnSupervised Misbehaviour Detection (USMD), comprising a deep neural network that learns about a system's expected behaviour from data-driven representations. USMD can identify in real-time the attacks on CPSs by using the long-short term memory and Attention method for multi-sensor data. The USMD's performance is evaluated on various known data sets (i.e., ToN_IoT, SWaT, WADI and Gas pipeline datasets). The experimental results indicate that the superior performance of USMD compared with six state-of-the-art methods, which we implemented and extensively tested. USMD achieves F-scores of 0.9699 and 0.9702 on SWaT and WADI datasets, respectively.

Security concerns for Internet of Things (IoT) applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and, therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning based ResNet model and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

Smart energy management based on the Internet of Things (IoT) aims to achieve optimal energy utilization through real-time energy monitoring and analyses of power consumption patterns in IoT networks (e.g., residential homes and offices) supported by wireless technologies - this is of great significance for the sustainable development of energy. Energy disaggregation is an important technology to realize smart energy management, as it can determine the power consumption of each appliance from the total load (e.g., aggregated data). Also, it gives us clear insights into users’ daily power-consumption-related behaviours, which can enhance their awareness of power-saving and lead them to a more sustainable lifestyle. This paper reviews the state-of-the-art algorithms for energy/power disaggregation and public datasets of power consumption. Also, potential use cases for smart energy management based on IoT networks are presented along with a discussion of open issues for future study.

The Industrial Internet of Things (IIoT) is creating a massive impact in a wide range of applications. In addition, with the forthcoming 5G and 6G technologies, vehicular ad-hoc networks will have pioneer advancements. However, security concerns are not well addressed, as vehicular networks should be deployed at a large scale. To address the security concerns, especially to ensure secure emergency message transmission, a blockchain-based protocol is proposed in this paper, where one of the blockchains is to store the authentication information of the vehicle, and another one to store and distribute blockchain services. Experimental analysis revealed that the proposed blockchain-based protocols are superior than the existing ones in terms of several metrics.

Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model.

Breast Cancer is one of the leading causes of death worldwide. Early detection is very important in increasing survival rates. Intensive research is therefore done to improve early detection of such cancers through the use of available technology. This includes various image processing techniques andgeneral machine learning. However, the reported accuracy for many of these studies was often not at the desirable level. Deep Learning based techniques are a promising approach for the early detection of Breast Cancer. We have therefore done a comparative analysis of seven Deep Learning techniques applied to the Wisconsin Breast Cancer (Diagnostic) Dataset. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were proven to be the most effective algorithms as these have demonstrated good results for the majority of performance indicators used in this study, including an accuracy of over 99 percent.

Carefully constructed cyber-attacks directly influence the data integrity and the operational functionality of the smart energy grid. In this paper, we have explored the data integrity attack behaviour in a wide-area sensor-enabled IIoT-SCADA system. We have demonstrated that an intelligent cyber-attacker can inject false information through the sensor devices that may remain stealthy in the traditional detection module and corrupt estimated system states at the utility control centres. Next, to protect the operation, we defined a set of critical measurements that need to be protected for the resilient operation of the grid. Finally, we placed the measurement units using an optimal allocation strategy by ensuring that a limited number of nodes are protected against the attack while the system observability is satisfied. Under such scenarios, a wide range of experiments has been conducted to evaluate the performance considering IEEE 14-bus, 24 bus-reliability test system, 85-bus, 141-bus and 145-bus test systems. Results show that by ensuring the protection of around 25% of the total nodes, the IIoT-SCADA enabled energy grid can be protected against injection attacks while observability of the network is well-maintained.

Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity attacks that exploit conventional static authentication mechanisms that authenticate users only at an ingress point. However, widely researched human user characteristics-based CA mechanisms cannot be extended to continuously authenticate Internet of Things (IoT) devices. The challenges are exacerbated with the increased adoption of device-to-device (d2d) communication in critical infrastructures. Existing d2d authentication protocols proposed in the literature are either prone to subversion or are computationally infeasible to be deployed on constrained IoT devices. In view of these challenges, we propose a novel, lightweight and secure CA protocol that leverages communication channel properties and a tunable mathematical function to generate dynamically changing session keys. Our preliminary informal protocol analysis suggests that the proposed protocol is resistant to known attack vectors and thus has strong potential for deployment in securing critical and resource-constrained d2d communication.

In a smart grid distribution management system, operation, planning, forecasting and decision making relies on demand-side management functions, which require real-time smart grid data. This data has significant dollar value because it is extremely useful for efficient control and intelligent prediction of the energy consumption, and expert management of residential and commercial load. However, the huge amount of (smart grid) data generated at a very high velocity poses a number of challenges. Utility companies have a huge demand for efficient summarization techniques to mine interesting patterns and extracting useful and actionable intelligence. Research from various domains has shown that data summarization can significantly improve the scalability and efficiency of various data analytic tasks (e.g., transactional database mining, data streams mining, network monitoring). This paper proposes a summarization approach (i.e., a set of algorithms, data structures, and query mechanisms) that enables the utility company to accurately infer various energy consumption patterns in real-time by automatic monitoring of smart grid data using significantly less computational resources. The proposed summarization approach is suitable for processing spatiotemporal streams, and it can also provide answers in real-time to various smart grid applications (e.g., demand-side management, direct load control, smart pricing and Volt-VAr control). Both theoretical bound and experimental evaluation are presented in this paper, which shows that the memory required for the proposed data structure grows linearly for the first 52 weeks; but interestingly, after the first year, the memory growth is negligible. The experimental results show that the proposed approach can process around 4 million smart meter readings every second or 120 million readings every minute. The proposed approach outperforms widely commercially used Database Management Systems (DBMSs) in terms of update and query costs: it is about 200 times faster than DBMSs in terms of update time, and about 340 times faster than DBMSs in terms of query time.

Recent studies show that smart grid is vulnerable to cyber anomalies. In this paper, an anomaly detection method is proposed to identify the abnormal patterns in the network power flows, which results from the accidental or deliberate changes of the database. The proposed method utilizes a multivariate time series statistical forecasting technique based on vector autoregressive model. To understand the power flow behavior of the system, a multiphase optimal power flow analysis is conducted. The proposed method is validated using IEEE Power Distribution System Analysis Subcommittee recommended 34-node and 123-node test systems. Three different experiments are performed to test the effectiveness of the proposed approach. Vulnerability and computational complexity issues of this paper are also addressed elaborately. Results obtained from this analysis show that the proposed method successfully captures the network anomalies at a high detection rate allowing only a few number of false alarms.

Recently, there has been significant increase in interest on Smart Grid security. Researchers have proposed various techniques to detect cyber-attacks using sensor data. However, there has been little work to distinguish a cyber-attack from a power system physical fault. A serious operational failure in physical power grid may occur from the mitigation strategies if fault is wrongly classified as a cyber-attack or vice-versa. In this paper, we utilize a data-driven approach to accurately differentiate the physical faults from cyber-attacks. First, we create a realistic dataset by generating different types of faults and cyber-attacks on the IEEE 30 bus benchmark test system. With extensive experiments, we observe that most of the established supervised methods perform poorly for the classification of faults and cyber-attacks specially for the practical datasets. Hence, we provide a data-driven approach where labelled data are projected in a new low-dimensional subspace using Principal Component Analysis (PCA). Next, Sequential Minimal Optimization (SMO) based Support Vectors are trained using the new projection of the original dataset. With both simulated and practical datasets, we have observed that the proposed classification method outperforms other existing popular supervised classification approaches considering the cyber-attack and fault datasets.