A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems

Abstract

Intrusion detection systems are devoted to monitor a network with aims at finding and avoiding anomalous events. In particular, we focus on misuse detection systems, which are trained to identify several known types of attacks. These can be unauthorized accesses, or denial of service attacks, among others. Whenever it scans a trace of a suspicious event, it is programmed to trigger an alert and/or to block this dangerous access to the system. Depending on the security policies of the network, the administrator may seek different requirements that will have a strong dependency on the behavior of the intrusion detection system. For a given application, the cost of raising false alarms could be higher than carrying out a preventive access lock. In other scenarios, there could be a necessity of correctly identifying the exact type of cyber attack to proceed in a given way. In this paper, we propose a multi-objective evolutionary fuzzy Communicated by V. Loia.

Building an efficient intrusion detection system based on feature selection and ensemble classifier

“…More specifically, for each dataset, we provide the confusion matrix derived from the testing process of CFS-BA-Ensemble, and compare the performance of the proposed algorithm with no feature selection and some state-of-the-art methods in terms of several detection metrics, including Accuracy (Acc), precision, Detection Rate (DR), F-Measure, Attack Detection Rate (ADR), and False Alarm Rate (FAR). The mathematical calculations of the utilized evaluation metrics are explained in [25].…”

A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids

“…In [106], Elhag et al combine a multi-objective evolutionary algorithm and fuzzy association rule learning to extract a more compact and informative rule set in comparison with other similar rule learning algorithms. The so-called multi-objective evolutionary fuzzy system can be trained using different performance metrics as objectives to adapt to user's requirement in accordance with detection trade-offs, and hence is better suited for its individual applications.…”

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

“…K-Map is useful for distinction of the data but it is not supported for large volume of data. Support Vector Machine (SVM) [19][20][21][22][23] [ [25][26][27][28][29][30][31][32]: is beneficial for classification and reformation of data, but at the same time SVM only cannot powerfully in recognizing about new data. Given a set of trained-data-set which require more computational time for big-data [4,12].…”

“…Fuzzy Logic based techniques found best in detection of intrusion for as compared to Data Mining, K-Map [21], MLP [25][26][27][28][29][30], Random Forest [25][26][27][28][29][30], SVM [32,33], Neural Network [31][32][33] and dimensionality reduction [35][36][37].…”