In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate and accuracy of the proposed model are higher than those of the known models. To show the effectiveness of the proposed model, 2 datasets with score and without score are created by using SCBM. In total, 6700 malware samples and 3000 benign samples are tested. The results are compared with those derived from n-gram and models from other studies in the literature. The test results show that, by combining the proposed model with an appropriate machine learning algorithm, the detection rate, false positive rate, and accuracy are measured as 99.9%, 0.2%, and 99.8%, respectively.

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.

Because the Internet makes human lives easier, many devices are connected to the Internet daily. The private data of individuals and large companies, including health-related data, user bank accounts, and military and manufacturing data, are increasingly accessible via the Internet. Because almost all data is now accessible through the Internet, protecting these valuable assets has become a major concern. The goal of cyber security is to protect such assets from unauthorized use. Attackers use automated tools and manual techniques to penetrate systems by exploiting existing vulnerabilities and software bugs. To provide good enough security; attack methodologies, vulnerability concepts and defence strategies should be thoroughly investigated. The main purpose of this study is to show that the patches released for existing vulnerabilities at the operating system (OS) level and in software programs does not completely prevent cyber-attack. Instead, producing specific patches for each company and fixing software bugs by being aware of the software running on each specific system can provide a better result. This study also demonstrates that firewalls, antivirus software, Windows Defender and other prevention techniques are not sufficient to prevent attacks. Instead, this study examines different aspects of penetration testing to determine vulnerable applications and hosts using the Nmap and Metasploit frameworks. For a test case, a virtualized system is used that includes different versions of Windows and Linux OS.

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.

Existing metamodeling approaches cannot effectively capture the properties of heterogeneous and multidimensional cyber-physical domains. To contribute to this area, a new metamodeling methodology for the development of metamodels with different mathematical semantics is proposed in this article. The new metamodeling architecture and the method for the development of metamodels as integrated formal systems are discussed. To prove the concept, a geometrical meta-metamodel, which allows linking physical properties of multidimensional domains with their spatial structures, is proposed.

Face recognition is biometric pattern recognition, which is more acceptable and convenient for users compared with other biometric recognition traits. Among many problems in face recognition system, pose problem is still considered as one of the major problem still unsolved in satisfactory level. This paper proposes a novel pose tolerant face recognition approach. Proposed system's contribution is a novel and efficient approach that includes feature extraction, pose transformation learning and recognition stages. In first stage, 2D PCA is used as robust feature extraction technique. The linear regression is used as efficient and accurate transformation learning technique to create frontal face image from a different posed face images in the second stage. In last stage, Mahalanobis distance is used for recognition. Experiments on FERET and FEI face databases demonstrate higher performance in comparison with traditional systems.

Intra prediction plays an important role in H.264/AVC. To reduce the complexity of the intra prediction of H.264/AVC, this paper proposes an efficient intra prediction algorithm. First, we determine if a macro block (MB) is flat or detailed rich by comparing its texture feature with dual thresholds criterion. Then, the flat MBs will be encoded in Intra 16×16 mode and those relatively rich MBs are encoded in Intra 4×4 mode. Second, an efficient Intra 4×4 mode selection algorithm is also proposed to reduce the number of candidates of intra prediction modes. The experimental results show that the encoding time of I-frame is reduced by an average of 30%, and both the increment in output bit rate and decrement in Peak Signal-to-Noise Ratio (PSNR) are negligible.

Existing metamodels are not able to effectively capture the knowledge of multidimensional cyber-physical domains. In order to contribute to this area the approach for development of metamodels for modelling the multidimensional cyber-physical systems is considered in the paper. The geometrical meta-metamodel, allowing to link the physical properties of domains with its spatial structure, is proposed. The implementation of the metamodelling approach in the Visual Environment for Cyber-Physical Modelling is considered. The example of using the geometrical meta-metamodel for metamaterials design is given. Investigation, modeling, design and implementation of cyber-physical systems have to be considered as a new and essential part of cyber worlds.

This paper proposes the design of specialized hardware, called Recovery Device, for a dual-redundant computer system that operates in real-time. Recovery Device executes all fault-tolerant services including fault detection, fault type determination, fault localization, recovery of system after temporary (transient) fault, and reconfiguration of system after permanent fault. The paper also proposes the algorithms for determination of fault type (whether the fault is temporary or permanent) and localization of faulty computer without using self-testing techniques and diagnosis routines. Determination of fault type allows us to eliminate only the computer with a permanent fault. In other words, the determination of fault type prevents the elimination of nonfaulty computer because of short temporary fault. On the other hand, localization of faulty computer without using self-testing techniques and diagnosis routines shortens the recovery point time period and reduces the probability that a fault will occur during the execution of fault-tolerant procedure. This is very important for real-time fault-tolerant systems. These contributions bring both an increase in system performance and an increase in the degree of system reliability.