Adaptive Network Security Policy for Peripheral Devices
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Solution Overview
Problem
Existing network security measures fail to effectively protect against novel attacks from peripheral devices, often inadvertently restricting legitimate connections while unable to detect and prevent malicious activities.
Innovation Solution
A computer-implemented method using machine learning to continuously update network security policies by adjusting weighting coefficients based on threat factors associated with peripheral device connection requests, automatically approving or denying connections based on calculated threat scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static connection policies are used to block known malware, then known threats are protected against, but novel attacks and legitimate connections cannot be detected
Solution Approach 1:
The patent implements dynamic security policies that automatically adapt to new threat patterns through continuous learning from connection data. The system transitions from static blocklists to dynamic decision-making that updates based on observed behavior, enabling detection of novel attacks while maintaining protection against known threats.
Solution Approach 2:
The system incorporates feedback loops where connection data and threat observations are continuously fed back into the policy generation process. This feedback mechanism enables the system to learn from actual network behavior and refine its security decisions, improving both detection capability and protection effectiveness over time.
2Reliability
If strict connection policies are enforced to prevent attacks, then security is improved, but legitimate peripheral device connections are unnecessarily denied
Solution Approach 1:
The patent applies differentiated security measures based on the specific characteristics of each connection request. Instead of uniform blocking, the system evaluates individual factors such as device type, connection context, and threat indicators to apply appropriate security controls, allowing legitimate connections while blocking malicious ones.
Solution Approach 2:
The system dynamically adjusts security policy parameters based on real-time analysis of connection requests. By modifying policy strictness and thresholds based on observed patterns and threat levels, the system maintains high security while ensuring legitimate operations are not unduly restricted.
3Reliability
If manual policy updates are performed to respond to new threats, then security responses are targeted, but response time is delayed and administrative workload increases
Solution Approach 1:
The system performs self-updating of security policies through automated machine learning algorithms that continuously analyze connection data and generate policy recommendations. This self-service capability eliminates the need for manual intervention in threat response, enabling immediate action against new threats while reducing administrative burden.
Solution Approach 2:
The system proactively identifies and prepares security responses before manual administrators can act. By continuously monitoring and pre-generating policy updates based on emerging threat patterns, the system anticipates security needs and prepares responses in advance, reducing response time when threats materialize.
Data Source
AI summary
The present disclosure relates to securing networks against attacks launched via connection of peripheral devices to networked devices. According to one aspect, there is provided a computer-implemented method of automatically updating a network security policy, the method comprising: running a machine learning algorithm to continuously update a plurality of weighting coefficients associated with a respective plurality of threat factors, the threat factors each having values defined for each of a plurality of requests for respective peripheral devices to connect to one or more networked devices which are communicably coupled to a secure network; and automatically updating a security policy associated with the secure network in respect of a particular threat factor when that threat factor's associated weighting coefficient changes by more than a predetermined amount in a predetermined period, wherein requests for peripheral devices to connect to the networked devices are automatically approved or denied in dependence on that policy.