Developing Cross-Domain Host-Based Intrusion Detection

Abstract

Digital transformation has continued to have a remarkable impact on industries, creating new possibilities and improving the performance of existing ones. Recently, we have seen more deployments of cyber-physical systems and the Internet of Things (IoT) as in no other time. However, cybersecurity is often an afterthought in the design and implementation of many systems; therefore, there usually is an introduction of new attack surfaces as new systems and applications are being deployed. Machine learning has been helpful in creating intrusion detection models, but it is impractical to create attack detection models with acceptable performance for every single computing infrastructure and the various attack scenarios due to the cost of collecting quality labeled data and training models. Hence, there is a need to develop models that can take advantage of knowledge available in a high resource source domain to improve performance of a low resource target domain model. In this work, we propose a novel cross-domain deep learning-based approach for attack detection in Host-based Intrusion Detection Systems (HIDS). Specifically, we developed a method for candidate source domain selection from among a group of potential source domains by computing the similarity score a target domain records when paired with a potential source domain. Then, using different word embedding space combination techniques and transfer learning approach, we leverage the knowledge from a well performing source domain model to improve the performance of a similar model in the target domain. To evaluate our proposed approach, we used Leipzig Intrusion Detection Dataset (LID-DS), a HIDS dataset recorded on a modern operating system that consists of different attack scenarios. Our proposed cross-domain approach recorded significant improvement in the target domains when compared with the results from in-domain approach experiments. Based on the result, the F2-score of the target domain CWE-307 improved from 80% in the in-domain approach to 87% in the cross-domain approach while the target domain CVE-2014-0160 improved from 13% to 85%.

Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection

“…Research studies have indicated that SVM and DT are among the most promising methods for anomaly detection. SVM and DT algorithms have been widely used in HIDSs due to their effectiveness in detecting and classifying intrusions [29][30][31]. In the context of HIDSs, SVM can learn the patterns and characteristics of known attacks and identify similar patterns in real-time system behavior, enabling the detection of unknown or novel attacks [29].…”

“…For almost two decades, researchers working in the field of intrusion detection have been using a publicly available Linux-based KDD98 data set [34][35][36]. However, that database is from 1998 and has lost its completeness and quality.…”