NSNAD: negative selection-based network anomaly detection approach with relevant feature subset

Aissa, Naila Belhadj; Gabsi, Mohamed; Derhab, Abdelouahid

doi:10.1007/s00521-019-04396-2

Cited by 26 publications

(9 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aissa et al proposed a negative selection-based network anomaly detection approach with relevant feature subset and applied it to the field of network anomaly detection. The algorithm uses the feature selector based on variation coefficient ranking to select features, which shortens the learning and classification time (reduces the time complexity) [27]. Abid et al designed a fault diagnosis algorithm based on negative selection theory.…”

Section: Related Workmentioning

confidence: 99%

Continual learning fault diagnosis method based on grid-based artificial immune system

Sun¹,

Wang²,

Liu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

In recent years, continual learning for class increments has attracted much attention. Continual Learning Classification Method (CLCM) based on artificial immune system can identify unknown faults during testing. However, CLCM still has the problem of excessive running consumption. Therefore, it is crucial to improve the efficiency of the immune algorithm and take advantage of its continual learning mechanism in the field of fault diagnosis. In this paper, a continual learning fault diagnosis method based on sparse grid and artificial immune system (SGCM) is proposed, which inspired by grid-based technique and continual learning classification method based on artificial immune system. Firstly, a new cell generation strategy is proposed to reduce the time complexity and improve the diagnosis efficiency, therefore, the problem of dimension explosion can be avoided. Besides, memory cells coding capabilities of SGCM rises the utilization rate of cells so as to simplify the calculation of affinity. At the same time, the conceived cell backtracking strategy enhances the continual learning ability of the algorithm so that new fault types can be quickly identified though the existing learning results. Ultimately, the model adaptive adjustment method inspired by single-layer feed forward neural network improves the generalization power and the accuracy of classification. We conduct experiments on well-known datasets from UCI repository to assess the performance of SGCM. To evaluate fault diagnosis performance of SGCM, experiments on reciprocating compressor experimental dataset and XJTU-SY rolling element bearing datasets were performed. The results show that SGCM is a fast fault diagnosis method with low time complexity and continual learning ability.

show abstract

Section: Related Workmentioning

confidence: 99%

Continual learning fault diagnosis method based on grid-based artificial immune system

Sun¹,

Wang²,

Liu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…AIS have seen two generations of algorithms developed (Greensmith, Whitbrook & Aickelin, 2010). First generation models were designed around general abstractions of traditional immune models, such as negative selection (Belhadj Aissa, Guerroumi & Derhab, 2020), clonal selection (Lysenko, Bobrovnikova & Savenko, 2018), and immune networks (Shi et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Multiresolution dendritic cell algorithm for network anomaly detection

Limon-Cantu¹,

Alarcón-Aquino²

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

show abstract

“…AIS algorithms mainly include negative selection algorithms (NSA) [11], artificial immune networks [10], clonal selection algorithms [13], danger theory, dendritic cell algorithms [1], etc. AIS also has many successful applications, such as anomaly detection [3], computer security [16], data mining [7], control algorithm [32,33], and fault diagnosis [4,15,29]. Among them, NSA deals well with the problem of more normal data and less abnormal data and has attracted wide attention in anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

A negative selection algorithm with hypercube interface detectors for anomaly detection

Gu,

Li,

Liu

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

NSNAD: negative selection-based network anomaly detection approach with relevant feature subset

Cited by 26 publications

References 65 publications

Continual learning fault diagnosis method based on grid-based artificial immune system

Continual learning fault diagnosis method based on grid-based artificial immune system

Multiresolution dendritic cell algorithm for network anomaly detection

A negative selection algorithm with hypercube interface detectors for anomaly detection

Contact Info

Product

Resources

About