Biblio

Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.

The enormous growth of Internet-based traffic exposes corporate networks with a wide variety of vulnerabilities. Intrusive traffics are affecting the normal functionality of network's operation by consuming corporate resources and time. Efficient ways of identifying, protecting, and mitigating from intrusive incidents enhance productivity. As Intrusion Detection System (IDS) is hosted in the network and at the user machine level to oversee the malicious traffic in the network and at the individual computer, it is one of the critical components of a network and host security. Unsupervised anomaly traffic detection techniques are improving over time. This research aims to find an efficient classifier that detects anomaly traffic from NSL-KDD dataset with high accuracy level and minimal error rate by experimenting with five machine learning techniques. Five binary classifiers: Stochastic Gradient Decent, Random Forests, Logistic Regression, Support Vector Machine, and Sequential Model are tested and validated to produce the result. The outcome demonstrates that Random Forest Classifier outperforms the other four classifiers with and without applying the normalization process to the dataset.

The heart of any enterprise is its databases where the application data is stored. Organizations frequently place certain access control mechanisms to prevent access by unauthorized employees. However, there is persistent concern about malicious insiders. Anomaly-based intrusion detection systems are known to have the potential to detect insider attacks. Accurate modelling of insiders behaviour within the framework of Relational Database Management Systems (RDBMS) requires attention. The majority of past research considers SQL queries in isolation when modelling insiders behaviour. However, a query in isolation can be safe, while a sequence of queries might result in malicious access. In this work, we consider sequences of SQL queries when modelling behaviours to detect malicious RDBMS accesses using frequent and rare item-sets mining. Preliminary results demonstrate that the proposed approach has the potential to detect malicious RDBMS accesses by insiders.

Supervisory Control and Data Acquisition (SCADA) systems complexity and interconnectivity increase in recent years have exposed the SCADA networks to numerous potential vulnerabilities. Several studies have shown that anomaly-based Intrusion Detection Systems (IDS) achieves improved performance to identify unknown or zero-day attacks. In this paper, we propose a hybrid model for anomaly-based intrusion detection in SCADA networks using machine learning approach. In the first part, we present a robust hybrid model for anomaly-based intrusion detection in SCADA networks. Finally, we present a feature selection model for anomaly-based intrusion detection in SCADA networks by removing redundant and irrelevant features. Irrelevant features in the dataset can affect modeling power and reduce predictive accuracy. These models were evaluated using an industrial control system dataset developed at the Distributed Analytics and Security Institute Mississippi State University Starkville, MS, USA. The experimental results show that our proposed model has a key effect in reducing the time and computational complexity and achieved improved accuracy and detection rate. The accuracy of our proposed model was measured as 99.5 % for specific-attack-labeled.