Client-Side Bot Detector Using Dynamic IP Profile Comparison
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Solution Overview
Problem
Existing methods fail to effectively detect software robots (bots) in client computers, as they often operate stealthily and can infect systems without users' knowledge, making it difficult to identify and prevent malicious activities such as spamming and phishing.
Innovation Solution
A client-side bot detector is implemented, which receives bot event profiles indicating IP addresses and time frames of malicious activities and compares them against a dynamic IP assignment profile to determine if the client computer is infected by a bot, utilizing dynamic IP addresses and bot event profiles to identify compromised systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used, then the detection process is simple, but the detection effectiveness is poor because bots operate stealthily and users are unaware of infections
Solution Approach 1:
The system performs preliminary actions by maintaining a profile of legitimate dynamic IP address assignments before bot infection occurs. This baseline profile allows the detection mechanism to identify anomalies when IP addresses are assigned in unusual patterns or time frames, enabling early detection of bot infections before they fully compromise the system.
Solution Approach 2:
The detection system implements feedback by continuously comparing current IP address assignment patterns against the stored profile of legitimate assignments. When discrepancies are found—such as IP addresses assigned outside expected time frames or patterns—the system generates alerts, providing feedback that enables timely detection and response to bot infections.
2Measurement precision
If dynamic IP tracking is implemented, then bot detection accuracy improves, but system complexity increases due to profile maintenance and data comparison requirements
Solution Approach 1:
The system creates a copy or profile of legitimate IP address assignment patterns stored in memory. This profile serves as a reference model that captures the normal behavior of dynamic IP assignments. By comparing actual IP assignments against this copied profile rather than analyzing raw data in real-time, the system achieves high detection accuracy while reducing computational complexity.
3Speed
If real-time IP address monitoring is performed, then bot detection responsiveness increases, but energy consumption and processing resources increase
Solution Approach 1:
The system performs preliminary action by pre-establishing the IP assignment profile before monitoring begins. This allows the real-time monitoring phase to simply compare current IP assignments against the pre-computed profile rather than performing complex analysis during each monitoring cycle, significantly reducing processing resource consumption while maintaining rapid detection capability.
Data Source
AI summary
Software robots (“bots”) may be detected in a client computer using a client-side bot detector. The client-side bot detector may be configured to receive bot event profiles indicating IP (Internet Protocol) addresses involved in malicious online activities perpetrated by bots and time frames when the malicious online activities occurred. The client-side bot detector may determine dynamic IP addresses that have been dynamically assigned to the client computer by consulting a dynamic IP assignment profile of the client computer. The client-side bot detector may compare the bot event profiles against the dynamic IP assignment profile of the client computer to determine if the client computer is infected by a bot.