LSTM Neural Network for Anomalous Website Sequence Detection
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Solution Overview
Problem
Current methods for detecting suspicious websites in proxy data streams often fail to effectively utilize temporal characteristics and are computationally expensive, particularly when dealing with unlabeled datasets and high volumes of data, leading to inefficiencies in anomaly detection.
Innovation Solution
A system and method utilizing a Long-Short-Term-Memory (LSTM) neural network to analyze sequences of websites, combined with a Window-based approach and a Random Forest classifier, to detect anomalous sequences by predicting the probability of website transitions and filtering out benign websites, while considering the temporal nature of data and reducing noise in large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LSTM neural network is used to detect anomalous sequences, then detection precision is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary actions by pre-processing proxy logs into structured sequences, pre-training the LSTM model on historical data, and establishing baseline anomaly thresholds before actual detection. This preparation work reduces the computational burden during real-time detection operations.
Solution Approach 2:
The detection process is segmented into distinct phases: data collection and pre-processing, LSTM model training, anomaly detection execution, and result analysis. The proxy log data is also segmented into discrete sequences of website URLs, allowing the LSTM model to process manageable chunks rather than overwhelming raw datasets.
2Measurement precision
If temporal characteristics are considered in anomaly detection, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The LSTM neural network acts as an intermediary that automatically captures and processes temporal characteristics from proxy log sequences. Instead of manually engineering temporal features, the LSTM learns temporal patterns internally, simplifying the system architecture while maintaining high detection accuracy.
Solution Approach 2:
Traditional mechanical approaches to temporal analysis (manual feature extraction, rule-based temporal patterns) are replaced with the neural network-based LSTM model. This substitution allows the system to automatically learn and adapt to temporal patterns in user browsing behavior without complex manual configuration.
3Quantity of substance
If proxy logs are analyzed in bulk, then detection coverage is improved, but processing time increases
Solution Approach 1:
The system implements periodic action by processing proxy logs in continuous batches or streams rather than attempting to analyze all data simultaneously. The LSTM model processes sequences of URLs in a streaming fashion, allowing the system to maintain high detection coverage while managing processing time through periodic batch operations.
Data Source
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AI summary
A system for detecting suspicious websites in proxy's data streams, comprising a data collection and pre-processing module for receiving data from proxy logs and transforming the data into temporal website sequences of length n, where each sequence is from a specific user and removing rare websites that appear only once; a training module being a neural network for receiving each the sequence and performing a training phase, during which each sequence corresponds to a user ID and generating a language model for predicting the next token (website) in each sequence; an anomaly detection module for receiving all the sequences and feeding the sequences into the trained model; providing by the model, for every sequence, a probability score representing how probable the sequence is; classifying the sequence as suspicious if the score is above a specific threshold t; an alerting module having an alert-logic for outputting alerts based on the number of suspicious websites of a user and the number of websites that were blocked by the proxy.