Internet of Things applications are on the rise within different areas of health and medical service. With this rapid rise of these applications, threat actors become more interested in targeting such devices. Within the health and medical context, there are particular challenges in privacy and security. In this paper, we present an explainable machine learning model designed to detect multi-protocol network-based attacks on Internet-of-Medical-Things with high accuracy. The proposed system was trained and tested using CICIoMT-2024 dataset. The proposed system delivered an accuracy exceeding $99.9 \%$, with an $F_{1}$ score exceeding 0.99. To increase trust in the obtained results, the proposed system was explained using SHAP values to provide insights into the most impactful features, and the nature of their impact on the system’s decisions.

The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems. Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This article addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.

In recent years, mobile devices have become commonly used not only for voice communications but also to play a major role in our daily activities. Accordingly, the number of mobile users and the number of mobile applications (apps) have increased exponentially. With a wide user base exceeding 2 billion users, Android is the most popular operating system worldwide, which makes it a frequent target for malicious actors. Adware is a form of malware that downloads and displays unwanted advertisements, which are often offensive and always unsolicited. This paper presents a machine learning-based system (AdStop) that detects Android adware by examining the features in the flow of network traffic. The design goals of AdStop are high accuracy, high speed, and good generalizability beyond the training dataset. A feature reduction stage was implemented to increase the accuracy of Adware detection and reduce the time overhead. The number of relevant features used in training was reduced from 79 to 13 to improve the efficiency and simplify the deployment of AdStop. In experiments, the tool had an accuracy of 98.02% with a false positive rate of 2% and a false negative rate of 1.9%. The time overhead was 5.54 s for training and 9.36 µs for a single instance in the testing phase. In tests, AdStop outperformed other methods described in the literature. It is an accurate and lightweight tool for detecting mobile adware.

the rapid growth of the Internet usage has caused problem on Internet protocol address space. To solve the space issue of Internet Protocol version 4 addresses, Internet Protocol version 6 was created to expand the availability of address spaces. Internet Protocol version 6 is designed to overcome the main limitations of Internet Protocol version 4 including the lack of security and the exhaustion of Internet Protocol address space. Internet Protocol version 6 protocols are not well supported by Network Intrusion Detection System, as is the case with Internet Protocol version 4 protocols. Several data mining techniques have been introduced to improve the classification mechanism of Intrusion detection system. In addition, extensive researches indicated that there is no Intrusion Detection systems for Internet Protocol version 6 using advanced machine-learning techniques to ward distributed denial of service attacks. With the increasing adoption of Internet Protocol version 6, Internet Protocol version 6-unique security issues become more urgent to address. Unlike Internet Protocol version 4, Internet Protocol version 6 relies on Internet Control Message Protocol version 6 in neighbor discovery. This means that blocking Internet Control Message Protocol version 6 traffic to reduce the possibility of using it as an attack tool, is not a viable option in most scenarios. One of the security threats posed by Internet Control Message Protocol version 6 is its possible use in Denial of Service attacks. This paper introduces a machine-learning based system to detect Distributed Denial of Service attacks that employ Neighbor Discovery protocol by using Machine learning techniques, due to the severity of the attacks and the importance of Neighbor Discovery protocol in Internet Protocol version 6. Decision tree algorithm and Random Forest Algorithm have given the highest accuracy result in comparison to the other algorithms.