Malware Prediction ML System for Low Power Sensor Networks
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Solution Overview
Problem
Current Machine Learning-based malware detection systems are not real-time, inefficient, and unsuitable for Low Power and Low Complexity Sensors due to high computational requirements, delays in updating, and scalability issues, making them vulnerable to evolving malware threats.
Innovation Solution
A Malware Prediction Machine Learning System that filters suspected malware prior to system entry, uses encryption methods, and employs Machine Learning algorithms to identify patterns in received malware without disassembly, combines historic and current data to predict future threats, and operates autonomously without additional hardware, enabling real-time detection and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Machine Learning systems use expensive computing intensive platforms to continually compare potentially malware infected messages to newly released malware, then malware detection accuracy is improved, but processing speed and scalability deteriorate
Solution Approach 1:
The system segments malware detection into two distinct phases: an offline training phase where comprehensive ML models are built using computing intensive platforms, and an online detection phase where pre-trained models perform rapid real-time classification. This segmentation allows expensive computations to be performed only during offline model creation, while online operations use lightweight inference that maintains both high accuracy and processing speed.
Solution Approach 2:
The system performs preliminary action by pre-training ML models offline using extensive malware libraries and computing resources before deployment. The pre-trained models are then deployed to edge devices and sensors, enabling real-time detection without requiring expensive computing platforms during actual malware detection operations. This preliminary preparation resolves the contradiction by shifting computational burden from runtime to model creation time.
2Reliability
If Machine Learning systems quarantine all incoming files for analysis, then detection thoroughness is improved, but processing delays and false positives increase
Solution Approach 1:
The system applies partial action by using ML-based classification to identify and focus analysis only on suspicious files that meet certain risk criteria, rather than quarantining and analyzing all incoming files equally. The ML model prioritizes high-risk files for detailed inspection while allowing low-risk files to pass through with minimal delay, thereby maintaining detection thoroughness for malicious content while reducing overall processing delays and false positives.
3Reliability
If Machine Learning systems are updated frequently to keep pace with new malware, then detection effectiveness is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system extracts the complexity of frequent updates and model retraining from edge devices and sensors by centralizing these operations on remote servers. The ML models are trained and updated in the cloud, then deployed to edge devices as pre-packaged model files. This extraction allows detection effectiveness to be improved through frequent updates while keeping device complexity low, as edge devices only need to load and use pre-trained models rather than perform complex training operations.
4Area of stationary object
If Low Power and Low Complexity Sensors are deployed in large numbers, then network coverage is improved, but vulnerability to cyber-attacks increases due to limited computing resources
Solution Approach 1:
The system uses copying by deploying identical pre-trained ML models across multiple low-power sensors and edge devices. Each device receives a copy of the same detection model, enabling consistent malware detection capabilities across the entire network without requiring each device to independently develop or maintain complex security systems. This copying approach allows extensive network coverage while maintaining security protection through standardized, proven detection algorithms.
Data Source
AI summary
One embodiment of this invention describes a method and apparatus for the use of Machine Learning to efficiently detect, identify, prevent, and predict cyber-attacks on Low Power and Low Complexity Sensor 100 (FIG. 1) networks that have low data transmission requirements, something that all current Machine Learning techniques are unable to accomplish due to numerous restrictions when applied to Low Power and Low Complexity Sensors. Low Power and Low Complexity Sensors are frequently found in various Internet of Things (IOT) network architectures. The IOT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to connect and exchange data, providing more direct integration of the physical world into computer-based systems. However, this should not restrict the applicability of any potential embodiment of this invention as described in this patent application.A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification.