4 research outputs found
Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data
Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based on change monitoring of critical files and malware signatures, respectively, some recent research, utilizing relatively vanilla deep learning (DL) methods, has demonstrated promising anomaly-based detection results that already have practical applicability due low false positive rate (FPR). More complex DL methods typically provide better results in natural language processing and image recognition tasks. In this paper, we analyze applicability of more complex dual-flow DL methods, such as long short-term memory fully convolutional network (LSTM-FCN), gated recurrent unit (GRU)-FCN, and several others, for the task specified on the attack-caused Windows OS system calls traces dataset (AWSCTD) and compare it with vanilla single-flow convolutional neural network (CNN) models. The results obtained do not demonstrate any advantages of dual-flow models while processing univariate times series data and introducing unnecessary level of complexity, increasing training, and anomaly detection time, which is crucial in the intrusion containment process. On the other hand, the newly tested AWSCTD-CNN-static (S) single-flow model demonstrated three times better training and testing times, preserving the high detection accuracy.This article belongs to the Special Issue Machine Learning for Cybersecurity Threats, Challenges, and Opportunitie
NLP Methods in Host-based Intrusion Detection Systems: A Systematic Review and Future Directions
Host based Intrusion Detection System (HIDS) is an effective last line of
defense for defending against cyber security attacks after perimeter defenses
(e.g., Network based Intrusion Detection System and Firewall) have failed or
been bypassed. HIDS is widely adopted in the industry as HIDS is ranked among
the top two most used security tools by Security Operation Centers (SOC) of
organizations. Although effective and efficient HIDS is highly desirable for
industrial organizations, the evolution of increasingly complex attack patterns
causes several challenges resulting in performance degradation of HIDS (e.g.,
high false alert rate creating alert fatigue for SOC staff). Since Natural
Language Processing (NLP) methods are better suited for identifying complex
attack patterns, an increasing number of HIDS are leveraging the advances in
NLP that have shown effective and efficient performance in precisely detecting
low footprint, zero day attacks and predicting the next steps of attackers.
This active research trend of using NLP in HIDS demands a synthesized and
comprehensive body of knowledge of NLP based HIDS. Thus, we conducted a
systematic review of the literature on the end to end pipeline of the use of
NLP in HIDS development. For the end to end NLP based HIDS development
pipeline, we identify, taxonomically categorize and systematically compare the
state of the art of NLP methods usage in HIDS, attacks detected by these NLP
methods, datasets and evaluation metrics which are used to evaluate the NLP
based HIDS. We highlight the relevant prevalent practices, considerations,
advantages and limitations to support the HIDS developers. We also outline the
future research directions for the NLP based HIDS development
Metai ir dienos. Vilniaus Gedimino technikos universitetas 2019 m.
Leidinyje aprašomi Vilniaus Gedimino technikos universiteto 2019 metų svarbiausieji įvykiai,
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