A survey of network anomaly detection techniques

Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

Serious concerns regarding vulnerability and security have been raised as a result of the constant growth of computer networks. Intrusion detection systems (IDS) have been adopted by netwo...

An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network

Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities

In this article, we investigate deep-learning-based omni intrusion detection system (IDS) for supervisory control and data acquisition (SCADA) networks that are capable of detecting both temporally uncorrelated and correlated attacks. Regarding the IDSs developed in this article, a feedforward neural network (FNN) can detect temporally uncorrelated attacks at an F1 of 99.967±0.005% but correlated attacks as low as 58±2%. In contrast, long short-term memory (LSTM) detects correlated attacks at 99.56±0.01% while uncorrelated attacks at 99.3±0.1%. Combining LSTM and FNN through an ensemble approach further improves the IDS performance with F1 of 99.68±0.04% regardless the temporal correlations among the data packets.

Omni SCADA Intrusion Detection Using Deep Learning Algorithms

Intrusion Detection is the task of detecting, preventing and possibly reacting to the attack and intrusions in a network based computer systems. In the literature several machine-learning paradigms have been proposed for developing an Intrusion Detection System. This paper proposes an Artificial Neural Network approach for Intrusion Detection. A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify the intrusions using a profile data set (ten percent of the KDD Cup 99 Data) with the information related to the computer network during Normal behavior and during Intrusive (Abnormal) behavior. Test result shows that the proposed approach works well in detecting different attacks accurately with less false positive and negative rate and it is comparable to those reported in the literature.

Intrusion Detection using Artificial Neural Network

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Intrusion Detection using Artificial Neural Network