Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

Ghanem, Waheed Ali H. M.; El-Ebiary, Yousef A. Baker; Abdulnab, Mohamed; Tubishat, Mohammad; Alduais, Nayef Abdulwahab Mohammed; Nasser, Abdullah B.; Abdullah, Nibras; Al-wesabi, Ola A.

doi:10.1007/978-981-33-6835-4_26

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…This FF that can be utilized to assess the quality of the solutions to minimize the values obtained. In essence, this training method is similar to the previous studies [37,38]. Supposing input nodes number is N, H denote hidden nodes, and O denote output nodes, then the output i th hidden node is computed as:…”

Section: Fitness Function(ff)mentioning

confidence: 99%

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Ghaleb¹,

Mohamad²,

Fadzli³

et al. 2022

Computers, Materials &Amp; Continua

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Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users' time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and GOA produce an artificial neural network (ANN) model, referred to (GOAMLP). Two corpora are applied Spam Base and UK-2011 Web spam for this approach. Finally, the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

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Section: Fitness Function(ff)mentioning

confidence: 99%

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Ghaleb¹,

Mohamad²,

Fadzli³

et al. 2022

Computers, Materials &Amp; Continua

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“…Alamiedy et al [3] proposed an improved anomaly-based IDS model based on a multi-objective gray wolf optimization (GWO) algorithm, in which GWO is used as a feature selection technique. Ghanem et al [7] have proposed a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method.…”

Section: Related Workmentioning

confidence: 99%

Detect & Reject for Transferability of Black-Box Adversarial Attacks Against Network Intrusion Detection Systems

Debicha

Debatty

Dricot

et al. 2021

Communications in Computer and Information Science

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In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying deceptive input. Research in computer vision, where this vulnerability was first discovered, has shown that adversarial images designed to fool a specific model can deceive other machine learning models. In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. Furthermore, we analyze the robustness of the ensemble intrusion detection system, which is notorious for its better accuracy compared to a single model, against the transferability of adversarial attacks. Finally, we examine Detect & Reject as a defensive mechanism to limit the effect of the transferability property of adversarial network traffic against machine learning-based intrusion detection systems.

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“…These methods are commonly divided in trajectory, population, natural and non-natural (Abdel-Basset, Abdel-Fatah & Sangaiah, 2018). Contemporary methods have focused on the improvement of feature selection as part of anomaly detection models, such as Deep Neural Networks (DNN), Long-Short Term Memroy (LSTM), Deep Belief Networks (DBN) and Multi-Layer Perceptron(MLP) (Ghanem et al, 2021;Elmasry, Akbulut & Zaim, 2020). Natural (or bio-inspired methods) include Evolutionary Algorithms (EA), such as Genetic Algorithms (GA) and have been used to improve the problem search space, and used in tandem with Whale Optimization Algorithm (Tao, Sun & Sun, 2018), as well as a method for feature selection and parameter optimization to improve Support Vector Machine (SVM) efficiency (Vijayanand & Devaraj, 2020).…”

Section: Related Workmentioning

confidence: 99%

Multiresolution dendritic cell algorithm for network anomaly detection

Limon-Cantu¹,

Alarcón-Aquino²

2021

PeerJ Computer Science

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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.

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Metaheuristic Based IDS Using Multi-objective Wrapper Feature Selection and Neural Network Classification

Cited by 9 publications

References 22 publications

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Detect & Reject for Transferability of Black-Box Adversarial Attacks Against Network Intrusion Detection Systems

Multiresolution dendritic cell algorithm for network anomaly detection

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