A multi-layer perceptron approach for flow-based anomaly detection

Efferen, Lennart Van; Ali-Eldin, Amr

doi:10.1109/isncc.2017.8072036

Cited by 29 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[24,57,91]. Multi-layer perceptron (MLP) [163], convolutional neural network (CNN) [164], recurrent neural network (RNN) and long-shortterm memory (LSTM) are the popular approaches used in deep learning modeling [23,124,164]. In these deeplearning models, many hidden layers can be used to complete the overall computing process.…”

Section: Machine Learning-based Modelingmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

View full text Add to dashboard Cite

Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

show abstract

Section: Machine Learning-based Modelingmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

View full text Add to dashboard Cite

show abstract

“…A multi-layer perceptron-based feedforward neural network for detecting intrusion with sigmoid activation function and backpropagation learning algorithm has been proposed in [17]. The approach has achieved on the 93.29% UNSW-NB15 dataset.…”

Section: Related Workmentioning

confidence: 99%

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Sharma

Giri

Granmo

et al. 2019

EURASIP J. on Info. Security

View full text Add to dashboard Cite

Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

show abstract

“…Moreover, feature selection has been one of the concerns. Several studies in anomaly detection with the IDS machine learning approach have been carried out, including [4]. In that research, a flow-based IDS uses two machine learning methods: J48 Decision Tree and Multi-layer perceptron (MLP).…”

Section: Related Workmentioning

confidence: 99%

“…The implementation of this machine learning method can be done using a data mining approach to recognize the patterns of attacks. Some research on data mining for IDS have been carried out, including research conducted by [4]. It is a flow-based IDS using two machine learning methods: J48 Decision Tree and Multi-layer Perceptron (MLP).…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis-Based Approach Features Selection on Machine Learning for Detecting Intrusion

Aziz¹,

Ahmad²

2019

IJIES

View full text Add to dashboard Cite

Various machine learning technology approaches have been applied to intrusion detection system (IDS). To get optimal results, it needs to take several stages for processing the traffics. Among them is the feature selection method, where irrelevant and redundant features are removed. In the previous research, the system is developed based on feature grouping that used a clustering approach as the evaluation criteria. In this research, we propose a method for improving the performance of machine learning with the feature selection approach based on feature clustering. We propose cluster based feature selection derived from the value of mutual information and Pearson correlation. The cluster hierarchy is used in forming filters that are used to create selected and reduced clusters. In developing the cluster hierarchy, single, complete, and average linkage method are used to determine the formation of the best feature clusters. The classification method with Support Vector Machine (SVM), Naïve Bayes, and J48 decision tree are applied to observe the performance of the proposed feature selection. Based on the experimental results, we find that the highest accuracy (i.e., 99.842%) is obtained when a single linkage in the J48 classification is implemented in the Kyoto 2006 dataset.

show abstract

A multi-layer perceptron approach for flow-based anomaly detection

Cited by 29 publications

References 17 publications

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Cluster Analysis-Based Approach Features Selection on Machine Learning for Detecting Intrusion

Contact Info

Product

Resources

About