IoT Network Traffic Fingerprinting for Resource-Efficient Malware Detection
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Solution Overview
Problem
Network-connected IoT devices face challenges in consistently detecting and protecting against security threats due to varying software or firmware versions, leading to vulnerabilities across interconnected networks, as traditional security mechanisms may be resource-intensive or costly for these devices.
Innovation Solution
A computer-implemented method using machine learning algorithms, such as autoencoders or restricted Boltzmann machines, to identify subsets of network traffic positions with low variability, generating executable code that consumes a determinate quantity of resources to detect deviations in network communication, thereby identifying security threats based on resource consumption patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security mechanisms (intrusion detection services, antimalware services, firewalls, antivirus services) are implemented on IoT devices, then security protection capability is improved, but device resource consumption increases and operational complexity increases
Solution Approach 1:
The patent extracts the security monitoring function from the IoT device itself and relocates it to an external network component. The device only generates lightweight traffic fingerprints, while the actual security analysis and threat detection are performed externally, eliminating the need for resource-intensive security software on the device.
Solution Approach 2:
The patent creates a universal security monitoring system that can protect multiple IoT devices with different firmware versions through a single external platform. The system analyzes traffic patterns from various device types and versions using unified machine learning models, providing broad security coverage without requiring device-specific security implementations.
2Reliability
If traditional security mechanisms are implemented on IoT devices, then security protection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent removes complex security software, intrusion detection services, and antivirus components from the IoT device. Only simple fingerprint generation logic remains on the device, while all complex security analysis functions are extracted and executed on external network infrastructure.
Solution Approach 2:
The IoT device automatically generates traffic fingerprints without requiring manual configuration or intervention. The external system autonomously performs security analysis, threat detection, and model updates, eliminating the need for users to manage complex security settings on resource-constrained devices.
3Reliability
If security monitoring is performed across diverse IoT device versions, then comprehensive threat detection is improved, but consistency of threat detection across versions deteriorates
Solution Approach 1:
The patent implements a universal machine learning-based analysis platform that processes traffic fingerprints from multiple IoT device versions using the same detection logic. The system adapts to different device types and firmware versions while maintaining consistent security policies and threat detection criteria across the entire device ecosystem.
Solution Approach 2:
The system dynamically adjusts analysis parameters and thresholds based on the specific device type and firmware version being monitored, while maintaining overall detection consistency. Machine learning models are trained on version-specific traffic patterns to achieve optimal detection performance for each device variant while preserving unified security standards.
Data Source
AI summary
A computer implemented method to identify a computer security threat based on communication of a network connected device via a computer network including receiving a plurality of blocks of network traffic from the device, each block including a sequence of network traffic data items being identifiable by a position in the sequence of the block; identifying a subset of positions occurring in every block for which a degree of variability of values of data items in each position of the subset meets a predetermined threshold; and generating executable code for performing a plurality of processing operations based on the identified subset of positions, the executable code consuming a determinate quantity of computing resources when executed for the received network traffic.