The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.