A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

Toldinas, Jevgenijus; Venčkauskas, Algimantas; Damaševičius, Robertas; Grigaliūnas, Šarūnas; Morkevičius, Nerijus; Baranauskas, Edgaras

doi:10.3390/electronics10151854

Cited by 80 publications

(43 citation statements)

References 40 publications

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“…The random forest tree model achieved 97%. In another study [ 34 ], the DT, Naive Bayes, and random forest machine learning algorithms were used to detect Android attacks. The information gain method was used to select the significant features.…”

Section: Background Of Studymentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

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With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.

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Section: Background Of Studymentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

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“…Finally, Toldinas et al [54] used a multistage deep learning image recognition approach for network intrusion detection. The network characteristics are converted into fourchannel pictures (Red, Green, Blue, and Alpha).…”

Section: Related Workmentioning

confidence: 99%

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

et al. 2021

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In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

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“…In addition, it helps to find correlation coefficients for two time series datasets. In 2021, Toldinas et al proposed an NIDS using multistage deep learning image recognition [18]. They transformed network traffic features into four-channel (red, green, blue, and alpha) images and classified the images using a pre-trained ResNet50 model.…”

Section: Network Intrusion Detectionmentioning

confidence: 99%

Advanced Intrusion Detection Combining Signature-Based and Behavior-Based Detection Methods

2022

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Recently, devices in real-time systems, such as residential facilities, vehicles, factories, and social infrastructure, have been increasingly connected to communication networks. Although these devices provide administrative convenience and enable the development of more sophisticated control systems, critical cybersecurity concerns and challenges remain. In this paper, we propose a hybrid anomaly detection method that combines statistical filtering and a composite autoencoder to effectively detect anomalous behaviors possibly caused by malicious activity in order to mitigate the risk of cyberattacks. We used the SWaT dataset, which was collected from a real water treatment system, to conduct a case study of cyberattacks on industrial control systems to validate the performance of the proposed approach. We then evaluated the performance of the proposed hybrid detection method on a dataset with two time window settings for the composite autoencoder. According to the experimental results, the proposed method improved the precision, recall, and F1-score by up to 0.008, 0.067, and 0.039, respectively, compared to an autoencoder-only approach. Moreover, we evaluated the computational cost of the proposed method in terms of execution time. The execution time of the proposed method was reduced by up to 8.03% compared to that of an autoencoder-only approach. Through the experimental results, we show that the proposed method detected more anomalies than an autoencoder-only detection approach and it also operated significantly faster.

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A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

Cited by 80 publications

References 40 publications

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Advanced Intrusion Detection Combining Signature-Based and Behavior-Based Detection Methods

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