The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people\'s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices\' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

The constantly changing nature of cyber threats presents unprecedented difficulties for people, institutions, and governments across the globe. Cyber threats are a major concern in today\'s digital world like hacking, phishing, malware, and data breaches. These can compromise anyone\'s personal information and harm the organizations. An intrusion detection system plays a vital responsibility to identifying abnormal network traffic and alerts the system in real time if any malicious activity is detected. In our present research work Artificial Neural Networks (ANN) layers are optimized with the execution of Spider Monkey Optimization (SMO) to detect attacks or intrusions in the system. The developed model SMO-ANN is examined using publicly available dataset Luflow, CIC-IDS 2017, UNR-IDD and NSL -KDD to classify the network traffic as benign or attack type. In the binary Luflow dataset and the multiclass NSL-KDD dataset, the proposed model SMO-ANN has the maximum accuracy, at 100% and 99%, respectively.

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA\'s potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

Increasing numbers of healthcare data breaches highlight the need for structured organisational responses to protect patients, trainees and psychiatrists against identity theft and blackmail. Evidence-based guidance that is informed by the COVID-19 pandemic response includes: timely and reliable information tailored to users\' safety, encouragement to take protective action, and access to practical and psychological support. For healthcare organisations which have suffered a data breach, insurance essentially improves access to funded cyber security responses, risk communication and public relations. Patients, trainees and psychiatrists need specific advice on protective measures. Healthcare data security legislative reform is urgently needed.

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency-Inverse Document Frequency (TF-IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features\' relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance.

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

Increased levels of digitalisation present major opportunities for efficiency in the oil and gas industry but can also contribute to new risks and vulnerabilities. Based on developments in the industry, the Norwegian Ocean Industry Authority (HAVTIL) has in recent years pursued targeted knowledge development and follow-up of company\'s digitalisation initiatives. This paper explores data collected through HAVTIL\'s audits of the development and use of automated systems within well operations. The analysis of the data resulted in the identification of five main topics related to the implementation of digital technologies. The five main topics were organisational complexity, follow-up and implementation of technology, analysis and documentation, user-interface and alarms and competence and training. Overall, the results support research findings within human factors and technology development, pointing out that there is a lack of focus on human factors in both development projects and in operations. In addition, this paper provides insight into how digitalisation initiatives are followed-up and explores the results from the analysis in light of the current developments in the industry.
To investigate automated operations and human performance, three audits were performed by the Norwegian Ocean Industry Authority (HAVTIL). These audits have been used as case studies and the basis for this paper. Results from the analysis support research findings within the field of human factors and technology development, pointing out that there is a lack of focus on human factors in both development projects and in operations.

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

As cyber-attacks increase in unencrypted communication environments such as the traditional Internet, protected communication channels based on cryptographic protocols, such as transport layer security (TLS), have been introduced to the Internet. Accordingly, attackers have been carrying out cyber-attacks by hiding themselves in protected communication channels. However, the nature of channels protected by cryptographic protocols makes it difficult to distinguish between normal and malicious network traffic behaviors. This means that traditional anomaly detection models with features from packets extracted a deep packet inspection (DPI) have been neutralized. Recently, studies on anomaly detection using artificial intelligence (AI) and statistical characteristics of traffic have been proposed as an alternative. In this review, we provide a systematic review for AI-based anomaly detection techniques over encrypted traffic. We set several research questions on the review topic and collected research according to eligibility criteria. Through the screening process and quality assessment, 30 research articles were selected with high suitability to be included in the review from the collected literature. We reviewed the selected research in terms of dataset, feature extraction, feature selection, preprocessing, anomaly detection algorithm, and performance indicators. As a result of the literature review, it was confirmed that various techniques used for AI-based anomaly detection over encrypted traffic were used. Some techniques are similar to those used for AI-based anomaly detection over unencrypted traffic, but some technologies are different from those used for unencrypted traffic.

UNASSIGNED: Despite the increasing use of domestic social robots by older adults, there remains a significant knowledge gap regarding attitudes, concerns, and potential adoption behavior in this population. This study aims to categorize older adults into distinct technology adoption groups based on their attitudes toward domestic social robots and their behavior in using the existing technology.
UNASSIGNED: An exploratory qualitative research design was used, involving semi-structured interviews with 24 retired Slovenian older adults aged 65 years or older, conducted between 26 June and 14 September 2023.
UNASSIGNED: Four distinct groups of older adults were identified: (1) Cautious Optimists, (2) Skeptical Traditionalists, (3) Positive Optimists, and (4) Technophiles based on eight characteristics.
UNASSIGNED: These groups can be aligned with the categories of the Diffusion of Innovation Theory. Privacy and security concerns, influenced by varying levels of familiarity with the technology, pose barriers to adoption. Perceived utility and ease of use vary considerably between groups, highlighting the importance of taking into account the different older adults. The role of social influence in the adoption process is complex, with some groups being more receptive to external opinions, while others exhibit more autonomous decision-making.