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Reducing Security Risks in Information Technology Contracts
Published in Michael R. Overly, A Guide to IT Contracting, 2021
If object code distribution is not possible, the company should consider: – Utilizing a source code obfuscator (i.e., scramble the symbols, code, and data of a software, rendering it impossible to reverse-engineer, while preserving the application’s functionality).
Scalable Malware Detection System Using Distributed Deep Learning
Published in Cybernetics and Systems, 2023
Malware analysis is a difficult undertaking, and the following are some of the most prevalent difficulties encountered: - A signature-based method is used by the majority of malware detection tools. The suspicious binary file’s hash value is compared to their signature database. Despite its simplicity, the signature-based technique is incapable of identifying novel malware threats.To change the structure and pattern of a malware program to evade detection, the malware developers use code obfuscation. It is difficult for a malware analyst to decode or reverse engineer the obfuscated code.There is always the need to keep an eye on the live network. Live network monitoring, on the other hand, has never been an easy task. Traditional malware detection systems are incapable of handling large amounts of streaming data. Monitoring petabytes and exabytes of real-time streaming data through the network is a challenging scalability and performance issue.Data are communicated in many different formats by various devices linked to the network. The malware detection system must be capable of interpreting a variety of data formats, which is not an easy task. This topic has been studied by several researchers. However, it continues to be a difficult task.While certain machine learning and deep learning-based malware detection systems have demonstrated promising results in malware detection, one of the primary obstacles is the system’s learning time and detection delay.