THE METHODOLOGY FOR PROTECTING GRID ENVIRONMENTS FROM MALICIOUS CODE DURING THE EXECUTION OF COMPUTATIONAL TASKS

The article is dedicated to the protection of Grid environments from malicious code that can be integrated during the execution of computational tasks. The main issues are identified, including high risks of malicious interference that could disrupt the operation of computational nodes or paralyze the entire system. The problem of spreading malicious code, which can disable nodes, is considered separately. A protection methodology based on automatic source code verification is proposed, allowing the detection of harmful programs before execution without significant impact on performance. Approaches to threat detection, including static and dynamic code analysis, as well as the use of machine learning algorithms for attack prediction, are discussed. The integration of such methodologies will enhance the security of Grid environments and promote their application in science and industry. The memory management model describes cells that can be in the states of free, allocated, or erroneous, and considers the need to protect memory from unauthorized access. Transitions between states are made through defined operations that ensure data security. The protection architecture includes a secure library, a dynamic verification module, and an access monitor, allowing effective monitoring and protection of the system from malicious software. The security strategy is based on adaptive policy management and static verification to preve