Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.
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In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection.
Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.
Ranked #1 on Intrusion Detection on 20NEWS (using extra training data)
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
Ranked #1 on Network Intrusion Detection on KDD
This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?
Ranked #1 on Intrusion Detection on CIC-DDoS
Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields.
From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in the scope of anomaly detection, we propose two extensions that allow to firstly overcome the previously mention limitation and secondly to provide it with some supervised learning capability.