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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior. [ABSTRACT FROM AUTHOR]

The email system is a globally distributed communication infrastructure service that involves multiple actors playing different roles to ensure end-to-end mail delivery. It is an indispensable method of communicating that is changing how people share data and information. As a result, it facilitates effective and efficient communication, especially in business, as well as convenience, accessibility, and replication. Today, email can send more than just text files; it can also send audio, video, photos, and other files with extensions. With email becoming ubiquitous in all aspects of our lives, enhancing its security, operating procedures, and forensic investigation has become essential. The purpose of this paper is to review some real email forensic incidents and the tools and techniques that have been proposed. A discussion of the major threats to email as well as techniques to mitigate them will also be provided. A comparison study was made of several techniques and analysis tools used in email forensics. In addition, this paper compares the available software tools for email forensics based on factors such as language interface, user interface, programming language, creation of image files, calculation of hash value, cost, and advantages. [ABSTRACT FROM AUTHOR]

Websites are becoming increasingly effective communication tools. Nevertheless, web applications are vulnerable to attack and can give attackers access to sensitive information or unauthorized access to accounts. The number of vulnerabilities in web applications has increased dramatically over the past decade. Many are due to improper validation and sanitization of input. Identifying these vulnerabilities is essential for developing high-quality, secure web applications. Whenever a website is released to the public, it is required to have had penetration testing to a certain standard to ensure the security of the information. Application-level security vulnerability detection is possible for many commercial and open-source applications. However, developers are curious about which tools detect security vulnerabilities and how quickly they do so. The purpose of this study is to discuss penetration testing and how it can be implemented. This paper also explores the hazards and vulnerabilities associated with the web environment as well as the protective measures that can be taken. In addition, a comprehensive review and comparison of common web penetration testing tools is provided. The aim of this paper is to help web penetration testers choose a technology that is optimal for their requirements. The paper also sets out to guide and provide recommendations to users for choosing the best web penetration test tool and increasing their awareness of secure web environments. The study results indicate that not all web penetration testing tools offer the same features and that combining analysis tools can provide detailed information about web vulnerabilities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) interconnects physical and virtual objects embedded with sensors, software, and other technologies, which exchange data using the Internet. This technology allows billions of devices and people to communicate, share data, and personalize services to make our lives easier. Despite the multiple benefits offered by IoT, it may also represent a critical issue due its lack of information security. Since the number of IoT devices has been rapidly increasing all over the world, they have become a target for many attackers, who try to steal sensitive information and compromise people's privacy. As part of the IoT environment, data and services should be protected with features such as confidentiality, accuracy, comprehensiveness, authentication, access control, availability, and privacy. Cybersecurity threats are unique to the Internet of Things, which has unique characteristics and limitations. In consideration of this, a variety of threats and attacks are being launched daily against IoT. Therefore, it is important to identify these types of threats and find solutions to mitigate their risks. Therefore, in this paper, we reviewed and identified the most common threats in the IoT environment, and we classified these threats based on three layers of IoT architecture. In addition, we discussed the most common countermeasures to control the IoT threats and mitigation techniques that can be used to mitigate these threats by reviewing the related publications, as well as analyzing the popular application-layer protocols employed in IoT environments and their security risks and challenges. [ABSTRACT FROM AUTHOR]