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According to the Fire-eye's M-Trends Annual Threat Report 2022, there are many advanced persistent threat (APT) attacks that are currently in use, and such continuous and specialized APT attacks cause serious damages attacks. As APT attacks continue to be active, there is a need for countermeasures to detect new and existing malicious codes. An APT attack is a type of intelligent attack that analyzes the target and exploits its vulnerabilities. It attempts to achieve a specific purpose, and is persistent in continuously attacking and threatening the system. With this background, this paper analyzes attack scenarios based on attack cases by malicious code, and surveys and analyzes attack techniques used in attack cases. Based on the results of the analysis, we classify and analyze malicious code detection techniques into security management systems, pattern-based detection, heuristic-based detection, reputation-based detection, behavior-based detection, virtualization-based detection, anomaly detection, data analysis-based detection (big data-based, machine learning-based), and others. This paper is expected to serve as a useful reference for detecting and preventing malicious codes. Specifically, this article is a surveyed review article. [ABSTRACT FROM AUTHOR]

According to the Fire-eye’s M-Trends Annual Threat Report 2022, there are many advanced persistent threat (APT) attacks that are currently in use, and such continuous and specialized APT attacks cause serious damages attacks. As APT attacks continue to be active, there is a need for countermeasures to detect new and existing malicious codes. An APT attack is a type of intelligent attack that analyzes the target and exploits its vulnerabilities. It attempts to achieve a specific purpose, and is persistent in continuously attacking and threatening the system. With this background, this paper analyzes attack scenarios based on attack cases by malicious code, and surveys and analyzes attack techniques used in attack cases. Based on the results of the analysis, we classify and analyze malicious code detection techniques into security management systems, pattern-based detection, heuristic-based detection, reputation-based detection, behavior-based detection, virtualization-based detection, anomaly detection, data analysis-based detection (big data-based, machine learning-based), and others. This paper is expected to serve as a useful reference for detecting and preventing malicious codes. Specifically, this article is a surveyed review article.

Abstract: The double-random phase-encoding (DRPE) technique is a typical optical image encryption technique, which can also be used for image hiding. Usually, the secret image is encrypted with the DRPE technique and the encoded image is hidden into the host image via superimposition to obtain the stego-image. The attack technique on the DRPE-based image hiding method was proposed in this paper. Firstly, a randomly selected superimposition coefficient was used to approximate the original superimposition coefficient to extract the hidden encoded images from the stego-images approximately. Then, the chosen-plaintext attack technique on the DRPE-based optical image encryption technique was applied to recover the random phase masks used in the DRPE technique. The theoretical analysis indicated that, without considering the computational error, the recovered secret image via the proposed attack technique is identical to the original one. Even considering the computational error, it is identical to the secret image recovered with the original DRPE-based image hiding method, which demonstrates that the attack on the DRPE-based image hiding method is successfully achieved. The numerical simulation results demonstrated the correctness of the theoretical analysis. [Copyright &y& Elsevier]