Effectively detecting intrusions in the computer networks still remains problematic. This is because cyber attackers are changing packet contents to disguise the intrusion detection system (IDS) recently. Besides, everyday a lot of new devices are added to the computer networks. These new devices are also raising security issues in the computer networks. To effectively manage the computer network flows and provide the security in advance; the components of the IDSs, the approaches and technologies that are used, the nature of the attacks, and the tools that are used needs to be examined deeply. This paper discusses intrusion detection technologies, methodologies, and approaches and also investigates new attack types, protection mechanisms, and recent scientific studies that have been made in this area. In addition, available datasets, well-known IDS tools, and advantages and disadvantages of particular IDSs are explained deeply. We believe that this scientific review study presents a road map for researchers and industry employees who focus on IDSs.

Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using human power to monitor them for special security purposes. Abnormal events deviate from normal patterns and are considered rare. Furthermore, collecting or producing data on these rare events and modeling abnormal data are difficult. Therefore, there is a need to develop an intelligent approach to overcome this challenge. Many research studies have been conducted on detecting abnormal events using machine learning and deep learning techniques. This study focused on abnormal event detection, particularly for video surveillance applications, and included an up-to-date state-of-the-art that extends previous related works. The major objective of this survey was to examine the existing machine learning and deep learning techniques in the literature and the datasets used to detect abnormal events in surveillance videos to show their advantages and disadvantages and summarize the literature studies, highlighting the major challenges.

Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.

Malware stands for malicious software, which is installed on a computer system without the knowledge of the system owner. It performs malicious actions such as stealing confidential information and allowing remote code execution, and it can cause denial of service. Recently, malware creators started to publish new malware, which can bypass anti-malware software, intrusion detection systems (IDS) and sandbox execution. Due to this evasion, the protection of computer networks and computerized systems against these programs has become one of the biggest challenges in the information security realm. This paper proposes a methodology to learn the well-known malware analysis and detection tools, to implement these tools on well-known malware and benign programs and to compare the obtained results. Further, this research will suggest to users how to analyze and detect existing and unknown malware. In a test case, 100 malware and 100 benign program samples were collected from different sources and analyzed under different versions of Windows machines. The test results indicated that it is almost impossible to detect malware by only using one tool. Using static and dynamic analysis tools together increased accuracy and the detection rate. The test results also showed that dynamic malware analysis tools outperformed static analysis tools.