Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

In recent years, with the continuous development of internet of things (IoT) technology, many fields have benefited a lot, including the maritime transportation system (MTS). But there are also corresponding risks, such as security and privacy, interference attacks, ransomware attacks, and so on. How to ensure the reliability and efficiency of information transmission is very important for maritime transportation system. In order to solve this problem, we propose an IoT-enabled maritime transport communication system, which is a distributed system composed of base stations and offshore buoys, and uses the unique structure of the blockchain to solve the problems of security and reliability in the network. There are two main advantages: First, the decentralized network is reliable and can handle node failures. Second, the use of blockchain technology can integrate computing resources into the entire network to support different tasks, while taking into account information security and transaction security. On this basis, with the help of edge computing technology, we have also improved the energy efficiency and performance of IoT devices in the system.

A growing kind of communication, the Internet of Things (IoT), links all network-capable devices worldwide. The most recent phase of the Internet of Things (IoT), known as the Internet of Medical Things (IoMT), is one that is rapidly catching researchers’ interest. The centralized storage system houses the substantial amount of medical data created by IoMT. However, centralizing sensitive patient data creates a single point of failure and raises privacy and security issues. In order to assure honesty, reliability, and safety while determining an entity’s trustworthiness, trust information is required to be safely transmitted and preserved. We propose a safe trust management solution based on blockchain technology. The proposed solution gathers node trust data using both time- and event-driven methods that are used to compute the trust of a node based on the threshold value. We also confirm the validity of data by varying the threshold value during transmission and authentication and employing a membership mechanism for authentication. The trust scores for each node are securely stored in an array, and the Maximum subarray (Kadane) Algorithm is used to compute the threshold value. The blockchain network receives and stores the IoMT score in the trusted list. Due to its distributed structure, capacity to maintain safeguards, and resiliency against a variety of threats with low overhead. Our approach is viable, deployable, and appropriate and also includes safety features like tamper-proofing, attack resistance, dependability, and low capabilities for IoT in smart hospitals.