The swift development of Internet of Things (IoT) technology has led to the expanded application of sensor networks in smart cities. Streetlights, as a vital aspect of smart city infrastructure, serve as a reflection of a town’s development. While streetlights are crucial for ensuring optimal visibility, road security, and public space utilization at night, they contribute significantly to electricity consumption. Governments across the globe are committed to reducing residential and industrial electricity usage. Regarding electricity usage, the Intelligent Street Lights System based on LED lights is a crucial concept today. This system functions by switching on the LED lights when a vehicle approaches and adjusting them to a dimmed or off state when no vehicle is present. The primary focus of this research paper, implementing an Intelligent Street Lights System based on LED lights, has emerged as a critical approach to achieving energy efficiency and cost reduction. Experimental findings demonstrate the potential for up to 80% energy savings compared to traditional streetlamp systems. Additionally, the system enables remote monitoring and intelligent management of urban street light conditions through terminal devices.

One of the core ideas of Industry 4.0 has been the use of digital twin networks (DTNs). A DTN facilitates the co-evolution of real and virtual things through the use of DT modelling, interactions, computation, and information analysis systems. A DT simulates product lifecycles to forecast and optimize manufacturing systems and component behavior. Industry and Academia have been developing digital twin (DT) technology for real-time remote monitoring and control, transport risk assessment, and intelligent scheduling in the smart industry. This study aims to design and simulate a comprehensive digital twin model connecting three factories to a single server. It incorporates remote network control, IoT integration, advanced networking protocols, and security measures. The model utilizes the Open Shortest Path First (OSPF) routing protocol for seamless network connectivity within the interconnected factories. The Access Control List (ACL) and authentication, authorization, and accounting (AAA) mechanisms ensure secure access and prevent unauthorized entry. The digital twin model is simulated using Cisco Packet Tracer, validating its functionality in network connectivity, security, remote control, and motor efficiency monitoring. The results demonstrate the successful integration and operation of the model in smart industries. The networked factories exhibit improved operational efficiency, enhanced security, and proactive maintenance.

One of the biggest cybersecurity challenges in recent years has been the risk that insiders pose. Internet consumers are susceptible to exploitation due to the exponential growth of network usage. Malware attacks are a major concern in the digital world. The potential occurrence of this threat necessitates specialized detection techniques and equipment, including the capacity to facilitate the precise and rapid detection of an insider threat. In this research, we propose a machine learning algorithm using a neural network to enhance malware detection accuracy in response to insider threats. A feature extraction, anomaly detection, and classification workflow are also proposed. We use the CERT4.2 dataset and preprocess the data by encoding text strings and differentiating threat and non-threat records. Our developed machine learning model incorporates numerous dense layers, ReLU activation functions, and dropout layers for regularization. The model attempts to detect and classify internal threats in the dataset with precision. We employed random forest, naive Bayes, KNN, SVM, decision tree, logical regression, and the gradient boosting algorithm to compare our proposed model with other classification techniques. Based on the results of the experiments, the proposed method functions properly and can detect malware more effectively and with 100% accuracy.

Deep fake technology has significantly advanced the creation of synthetic images and videos, sparking widespread concerns about its potential misuse in spreading misinformation, violating privacy, and enabling identity theft. As these manipulations be-come increasingly sophisticated, the development of reliable detection methods has become a pressing necessity. This research tackles this challenge by proposing a robust deep fake detection pipeline, leveraging a custom dataset created using Roboflow. The dataset is divided into two primary classes: real and fake, with the fake class further categorized into three subtypes based on complexity: easy fake, mid fake, and hard fake. Easy fake images involve basic manipulations that are easily identifiable by the human eye, while mid fake images combine AI-generated and human-generated elements, and hard fake images are entirely AI-generated, posing significant challenges for detection. To ensure authenticity and diversity, real images were collected from personal networks and online repositories. We trained and evaluated four YOLO-based models YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for the detection task. YOLOv8 emerged as the top-performing model, achieving an accuracy of 96.2% in distinguishing between real and fake images. Finally, addressing ethical considerations and developing countermeasures to mitigate the societal impact of deep fakes should remain a priority for future research.