A Review on Application of GANs in Cybersecurity Domain

Arora, Aayush; Shantanu, _

doi:10.1080/02564602.2020.1854058

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…The KNN technique is an easy and effective way to classify multiclass objects by using Euclidean distances in the feature space and the proximity of training samples. 20,21 Considering a group of observations and target values (X 1 , Y 1 ), (X 2 , Y 2 ) … (X n , Y n ), where the set of observations, X n Z d and target values Y n (0, 1). As a result, for a particular size of n, k-NN evaluates the k nearest points of the test sequence inside the training samples and uses the label classes to predict the vector test class.…”

Section: K-nearest Neighbormentioning

confidence: 99%

“…The KNN technique is an easy and effective way to classify multiclass objects by using Euclidean distances in the feature space and the proximity of training samples 20,21 . Considering a group of observations and target values (X 1 , Y 1 ), (X 2 , Y 2 ) … (X n , Y n ), where the set of observations, X n є Z d and target values Y n є (0, 1).…”

Section: Background and Preliminariesmentioning

confidence: 99%

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A feed forward deep neural network model using feature selection for cloud intrusion detection system

Sharma,

Singh

2023

Concurrency and Computation

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SummaryThe rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets.

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Section: K-nearest Neighbormentioning

confidence: 99%

Section: Background and Preliminariesmentioning

confidence: 99%

A feed forward deep neural network model using feature selection for cloud intrusion detection system

Sharma,

Singh

2023

Concurrency and Computation

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“…Miao et al [63] used crowdsourcing to demonstrate the effectiveness of generative poisoning attack especially when the data points/datasets are huge/large. Arora et al [64] presented a systematic literature review of GANs applications in the cybersecurity domain, including an analysis of specific extended GAN frameworks and currently used stable cybersecurity datasets. Their work compared how security professionals are employing GANs to produce amazing results in fields, such as Intrusion Detection, Steganography, Password Cracking, and Anomaly Generation.…”

Section: Related Workmentioning

confidence: 99%

Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network

Liu,

Hu,

Liu

et al. 2023

IEEE Access

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Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing. In this paper, we focus on the adversarial attack, in particular the poisoning attack, against the network intrusion detection system (NIDS), which is often viewed as the first line of defense against cyber threats. We develop an Anomaly-based intrusion on IoT networks Generative Adversarial Network (AIGAN), which uses deep learning techniques to generate adversarial data and to conduct an anomaly attack on IoT networks. To evaluate the effectiveness of our generator, we measure the similarities between real and fake data using the Jaccard similarity index, in addition comparing the F1-scores from four generic algorithms: multilayer perception, logistic regression, decision tree, random forest. We contrast the performance of ten machine learning classifiers experimented on two real IoT datasets and their fake adversarial samples. Our work highlights a vulnerable side of NIDS created by machine learning when attacked with adversarial perturbation.

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“…This GAN uses an enhanced domainattention module (DAM) to create a longer range dependency between two domains while using fewer parameters and less memory. Additionally, GANs are becoming more and more popular in the fields of medical image processing for tasks like segmentation and classification [33], cybersecurity [34], time series and sequence generation [35] as well as for speech processing [36]. GANs have been used in the field of medical imaging for analyzing images from radiography, computerized tomography (CT) scans, and magnetic resonance imaging (MRI).…”

Section: Related Workmentioning

confidence: 99%

Mathematical analysis of loss function of GAN and its loss function variants

2022

IJATEE

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Generative adversarial networks (GANs) have turned up as the most widely used approaches for creating realistic samples. They're the effective latent variable models for learning complex real distributions. However, despite their enormous success and popularity, the process of training GANs remains challenging and suffers from a number of failures. These failures include mode collapse where the generator generates the same set of output for different inputs which finally leads to loss of diversity; non-convergence because of the diverging and oscillatory behaviors of both generator and discriminator while being trained; and vanishing or exploding gradients due to which either no learning or extremely slow learning takes place. In the past years, a variety of strategies for stabilizing GAN training have been explored which includes modified architectures, loss functions, and other methods. The choice of loss function has been found to be the most crucial part of the GAN model because it influences the vanishing gradient and model collapse directly. Viewing these loss functions as divergence minimization has provided a rich avenue of development. All of these factors make GAN training inherently unstable, and this instability is difficult to comprehend mathematically. This paper intends to provide a thorough mathematical explanation of these divergence minimization functions. It illustrates in great detail the two variants of the loss functions of the original GAN, their optimization to kullback-leibler (KL) divergence and jensen-shannon (JS) divergence along with their shortcomings. It also describes the loss functions of the different loss function GAN variants that have been proposed to mitigate these shortcomings as well as their minimization. The original GAN and its loss function variants have also been implemented using the standard MNIST, Fashion-MNIST, and CIFAR-10 datasets.

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A Review on Application of GANs in Cybersecurity Domain

Cited by 16 publications

References 9 publications

A feed forward deep neural network model using feature selection for cloud intrusion detection system

A feed forward deep neural network model using feature selection for cloud intrusion detection system

Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network

Mathematical analysis of loss function of GAN and its loss function variants

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