2,445 research outputs found
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
With massive data being generated daily and the ever-increasing interconnectivity of the worldâs Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of attacks are classified. We demonstrate the advantage of the unsupervised representation learning model in binary intrusion detection tasks. Besides, we alleviate the data imbalance problem with SVM-SMOTE oversampling technique in 4-class classification and further demonstrate the effectiveness and the drawback of the oversampling mechanism with a deep neural network as a base model. Index TermsâIntrusio
Bayesian Networks for Interpretable Cyberattack Detection
The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) â probabilistic graphical models consisting of a set of variables and their conditional dependencies â for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things
It is critical to secure the Industrial Internet of Things (IIoT) devices
because of potentially devastating consequences in case of an attack. Machine
learning and big data analytics are the two powerful leverages for analyzing
and securing the Internet of Things (IoT) technology. By extension, these
techniques can help improve the security of the IIoT systems as well. In this
paper, we first present common IIoT protocols and their associated
vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the
utilization of machine learning in countering these susceptibilities. Following
that, a literature review of the available intrusion detection solutions using
machine learning models is presented. Finally, we discuss our case study, which
includes details of a real-world testbed that we have built to conduct
cyber-attacks and to design an intrusion detection system (IDS). We deploy
backdoor, command injection, and Structured Query Language (SQL) injection
attacks against the system and demonstrate how a machine learning based anomaly
detection system can perform well in detecting these attacks. We have evaluated
the performance through representative metrics to have a fair point of view on
the effectiveness of the methods
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