Abstract
Host-based Intrusion Detection Using Signature-based and AI-driven Anomaly Detection Methods
Highlights
In the context of information systems, an intrusion can be defined as any attempt to gain unauthorised access and potentially cause damage to any given system
Recent research in anomaly-based host-based intrusion detection systems (HIDS) algorithms has focused on the application of Neural Networks (NNs) and Deep Learning (DL) algorithms with the purpose of system call language-modelling in order to predict if a sequence of system calls is normal or anomalous
The authors attempt to tackle the problem of high false-alarm rates by using an ensemble method of multiple thresholding classifiers, using the rectified linear units (ReLU) method.[25]. They compare three LSTM solutions with a k-nearest neighbour and a k-means clustering classifier, and the results show the superiority of their method
Summary
In the context of information systems, an intrusion can be defined as any attempt to gain unauthorised access and potentially cause damage to any given system This means that any attack that may pose a threat to the confidentiality, integrity, or availability of information meets the definition of an intrusion. Because the capabilities of an IDS are primarily dependent on the data that is available to it, the location of the IDS is an important architectural decision This is the main difference between network-based intrusion detection systems (NIDS) versus host-based intrusion detection systems (HIDS). Both approaches are presented, albeit the latter are the ones that we mainly focus on in this study.
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