Internet of Things (IoT) applications are used in almost every part of our life, so it is important to protect the sensitive data and information that is transmitted over wireless networks such as images and documents. The IoT devices have limited computational resources; they are called limited devices due to their limited processors and memory size. Traditional encryption methods require a lot of computing power; therefore, it is difficult to implement traditional cryptographic algorithm on IoT processor. Finally, a new, lightweight encryption method based on the DNA sequence is proposed to suit the IoT devices in a way to make an easy and secure the communications among the IoT devices. DNA sequences are very random, so we have used it to make a strong secret key that is hard for attackers to break. The proposed method has an advantage in terms of efficiency and strength. Experiments and security tests show that the proposed encryption system not only has a good encryption effect and can withstand known attacks, but it is also fast enough for real-world use. The DNA key is used to encrypt files using two simple and reliable methods such as substitution and transposition procedures that meet IoT computational requirements. In addition, when compared with other encryption algorithms, the experimental results shows that the key size, encryption time, and distortion preparation are all superior.

Anomaly detection in attributed networks is challenging because it combines complex structural relationships with high-dimensional node attributes. Traditional methods rarely solve both problems simultaneously, making them less effective. We present a new approach for anomaly detection that uses adversarial variational graph autoencoders (VGAE) with attention-based residual modeling and contrastive learning to address these issues. The attention-based residual component enhances node embeddings by emphasizing critical connections and preserving essential structural details, improving anomaly detection by retaining subtle yet important information. VGAE is explicitly trained to combine the structure and features into a joint embedding space, while adversarial training prevents overfitting by making embeddings more robust against noise and pushing normal/abnormal nodes further apart. To improve these embeddings, we include the contrastive learning component to move nodes belonging to a community closer and separate them from other communities so that an intrusion or anomaly could be recognized as malicious not only at global levels but inside one or more specific subcommunities at local levels as well. Our method is also capable of dealing effectively with the challenge of imbalanced data in which normal patterns might dominate during training, thus incompetent to detect rare anomalies. Through the execution of extensive experimental investigations utilizing diverse real-world datasets, we establish that our methodology exceeds the performance of the existing premier anomaly detection techniques with respect to both AUC and average precision metrics. These characteristics bestow upon our model a dual advantage of efficacy and scalability, thereby facilitating its applicability to a myriad of practical scenarios, including but not limited to fraud detection in telecommunication networks, spammer hunting in social networks, or identifying errors/omissions of citation information.

Internet of Things (IoT), as a concept has envisioned devices around us and devices that is used on bodies to collaborate and co-exist between both devices and bodies in harmony - thereby enhancing the quality of life and living conditions directly or indirectly. Realization of this vision requires that IoT provides an open extensible platform for enabling huge network of machines to collaborate with one another. Huge networks such as these provide immense opportunities in a multitude of diverse fields with wide ranging applications. There are many technical, operational, application and management challenges in the areas of building this unique platform owing to the diversity in the kind of applications and the devices (both new and legacy) supporting them. This paper introduces the main challenges of IoT development, applications, and standardization. Finally, the paper concludes by discussing the opportunities and prospects of IoT.

Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge difference to their treatment. However, grade recognition is a challenging issue for radiologists involved in automated diagnosis and healthcare monitoring. Recent research has been motivated by the search for deep learning-based mechanisms for segmentation and grading to assist radiologists in diagnostic analysis. Segmentation refers to the identification and delineation of tumour regions in medical images, while classification classifies based on tumour characteristics, such as the size, location and enhancement pattern. The main aim of this research is to design and develop an intelligent model that can detect and grade tumours more effectively. This research develops an aggregated architecture called LGCNet, which combines a local context attention network and a global context attention network. LGCNet makes use of information extracted through the task, dimension and scale. Specifically, a global context attention network is developed for capturing multiple-scale features, and a local context attention network is designed for specific tasks. Thereafter, both networks are aggregated, and the learning network is designed to balance all the tasks by combining the loss functions of the classification and segmentation. The main advantage of LGCNet is its dedicated network for a specific task. The proposed model is evaluated by considering the BraTS2019 dataset with different metrics, such as the Dice score, sensitivity, specificity and Hausdorff score. Comparative analysis with the existing model shows marginal improvement and provides scope for further research into BT segmentation and classification. The scope of this study focuses on the BraTS2019 dataset, with future work aiming to extend the applicability of the model to different clinical and imaging environments.