Polymorphic Malware Detection via Quantum Hash State Analysis
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Solution Overview
Problem
Current systems face challenges in detecting polymorphic malicious content that uses techniques to change its hash values and evade detection as it propagates through networks, making it difficult to identify and prevent malware spread.
Innovation Solution
A system utilizing a quantum optimization algorithm to determine hash value states of an electronic file across network devices, comparing hash values to identify polymorphic malware and initiating an intrusion detection protocol to deny access and quarantine the file, leveraging the capabilities of quantum computing for enhanced detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hash comparison methods are used to detect malware, then the detection process is simple and fast, but polymorphic malware can easily evade detection by changing its hash value
Solution Approach 1:
The system dynamically adapts the detection approach based on whether the malware is identified as polymorphic. For standard files, simple hash comparison is used. When polymorphic characteristics are detected (mismatched hashes at different network devices), the system activates quantum optimization algorithms to dynamically determine possible hash states, transforming a static detection system into a dynamic one that responds to threat complexity.
Solution Approach 2:
The system changes the detection parameter from a single fixed hash value to multiple possible hash states. By using quantum optimization to determine probable hash states that polymorphic malware might transform into, the system expands the detection parameter space, allowing it to match malware even when it changes its hash value through polymorphic transformation.
2Measurement precision
If quantum optimization algorithms are used to determine hash value states for polymorphic malware detection, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary hash comparison checks at network devices before activating the full quantum optimization process. By first checking if hashes match at different devices and identifying polymorphic characteristics early, the system prepares for the more intensive quantum computation only when necessary, reducing overall processing time by avoiding unnecessary quantum optimization for non-polymorphic files.
Solution Approach 2:
The system introduces an intermediary classification step that determines whether a file exhibits polymorphic behavior before applying quantum optimization. This intermediary layer acts as a filter, directing only suspicious polymorphic files to the computationally intensive quantum algorithm, thereby reducing the overall processing time and resource consumption.
3Loss of information
If polymorphic malware is allowed to propagate through the network to gather data about its transformations, then better detection patterns can be identified, but network security is compromised
Solution Approach 1:
The system applies preliminary anti-action by detecting polymorphic characteristics and activating quantum optimization to determine possible hash states before the malware can complete its propagation cycle. By proactively identifying and blocking polymorphic malware based on detected transformation patterns and predicted hash states, the system prevents further propagation and data collection opportunities for the malware.
Solution Approach 2:
The system implements feedback by continuously monitoring hash values across network devices and using quantum optimization results to update detection patterns. When polymorphic malware is detected, the system feeds back the observed transformations and predicted hash states into the detection algorithm, improving future detection accuracy while blocking the malware, thus gaining intelligence without allowing harmful propagation.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods and computer program products for enhanced detection of polymorphic malicious content within an entity. In this regard, the present invention receives information associated with an incidence of an electronic file; receives an first hash value of the electronic file from a first network device and a second hash value of the electronic file from a second network device; compares the first hash value with the second hash value; determines that the electronic file is polymorphic based on at least the match; initiates an execution of a quantum optimization algorithm using a quantum optimizer to determine one or more hash value states; receive information associated with an incidence of the electronic file at the third network device; determine that the electronic file is malware; and initiate an intrusion detection protocol configured to deny the electronic file access to the third network device.