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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

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Evaluating Word Embedding Feature Extraction Techniques for Host-Based Intrusion Detection Systems.

Paul K Mvula¹, Paula Branco¹, Guy-Vincent Jourdan¹

¹School of Electrical Engineering and Computer Science (EECS), University of Ottawa, 800 King Edward Avenue, Ottawa, K1N 6N5 ON Canada.

|April 10, 2023

Summary

This summary is machine-generated.

This study addresses inconsistencies in machine learning models for host-level intrusion detection caused by Word2Vec and GloVe. Concatenating embeddings from both methods improves accuracy and reduces data leakage.

Keywords:

Cyber-security Feature extraction Intrusion detection Syscall traces Word embedding

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Host-level intrusion and anomaly detection heavily relies on system call trace analysis.
Sequence-model-based feature extractors like Word2Vec and GloVe capture semantic relationships but introduce data inconsistencies.

Purpose of the Study:

To identify and address research challenges posed by Word2Vec and GloVe in system call trace analysis.
To evaluate new feature sets for improved accuracy in anomaly detection.

Main Methods:

Experimental analysis of Word2Vec and GloVe for feature extraction from system call traces.
Assessment of new feature sets to mitigate inconsistencies.
Comparison of different embedding vector combination strategies.

Main Results:

Word2Vec was found to introduce more duplicated samples compared to GloVe.
Concatenating embedding vectors from Word2Vec and GloVe achieved the highest balanced accuracy.
The proposed approach mitigated data leakage and enhanced model performance.

Conclusions:

The combination of Word2Vec and GloVe embeddings offers a robust solution to feature inconsistencies in system call analysis.
This integrated approach significantly improves the performance of host-level intrusion and anomaly detection systems.