Using deep learning to solve computer security challenges: a survey

Choi, Yoon Ho; Liu, Peng; Shang, Zitong; Wang, Haizhou; Wang, Zhilong; Zhang, Lan; Zhou, Junwei; Zou, Qingtian

doi:10.1186/s42400-020-00055-5

Cited by 29 publications

(14 citation statements)

References 47 publications

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“…Our work covers partly the same primary studies, but our work discusses in addition the analysis of object code and the differences of the underlying deep learning architecture while this work focuses on deep neural networks. Computer security issues investigated by deep learning is the topic of the work by Choi et al (2020). Their range of covered topics is much broader.…”

Section: Related Workmentioning

confidence: 99%

Deep security analysis of program code

2021

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Due to the continuous digitalization of our society, distributed and web-based applications become omnipresent and making them more secure gains paramount relevance. Deep learning (DL) and its representation learning approach are increasingly been proposed for program code analysis potentially providing a powerful means in making software systems less vulnerable. This systematic literature review (SLR) is aiming for a thorough analysis and comparison of 32 primary studies on DL-based vulnerability analysis of program code. We found a rich variety of proposed analysis approaches, code embeddings and network topologies. We discuss these techniques and alternatives in detail. By compiling commonalities and differences in the approaches, we identify the current state of research in this area and discuss future directions. We also provide an overview of publicly available datasets in order to foster a stronger benchmarking of approaches. This SLR provides an overview and starting point for researchers interested in deep vulnerability analysis on program code.

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Section: Related Workmentioning

confidence: 99%

Deep security analysis of program code

2021

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“…They match the code blocks by using the regular matching through a huge rule base, which has a large improvement space [10]. With the development of code vectorization, auditing models of machine learning are proposed and optimized [11,12].…”

Section: Code Auditing With Machine Learningmentioning

confidence: 99%

Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining

Wang

Miao

et al. 2020

Applied Sciences

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The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operation sequence of source code and built a model to acquire the information that is most closely related to the XSS attack in the data stream. The method proposed can significantly improve the recall rate of vulnerability samples. Compared with related audit methods, our method has high reusability and excellent performance. Our classification model achieved an F1 score of 0.92, a recall rate of 0.98 (vulnerable sample), and an area under curve (AUC) of 0.97 on the test dataset.

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“…For that purpose, we use the chest X-Ray imaging method to study the case in detail where studies revealed that deeper analysis of chest X-Ray found the ability to identify the disease correctly, the specificity of which reaches 90% [9]. Big data, deep learning and artificial intelligence [43] are used in innumerable fields of study, analysis and prediction in the recent times, be it the healthcare sector [10,6,34], security [12,19], recommendation systems [3], surveillance bots [44] or dealing with microscopy images [14] and cardiovascular problems [41]. In this paper, we discuss the various deep learning models and build models that could distinctly classify an X-ray into regular pneumonia, Covid-19 pneumonia or a patient without pneumonia.…”

Section: Introduction Covid-19 (Corona Virus Disease 2019mentioning

confidence: 99%

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

Saha

Gourisaria

Harshvardhan

2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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Corona Virus has caused a disruption to the normalcy in the world and require thorough patient management. Looking at the present scenario and the kind of pandemic that it has turned out to be, this paper aims at providing an enhancement to the RT-PCR way of testing and uses the chest X-Rays to detect the presence and severity of the corona virus in a body to successfully differentiate between pneumonia and the Covid-19. The main motive is to help doctors and medical experts with an advance aid to the nursing of critically affected patients. This is feasible because the X-Ray machines are widely available throughout the country and they can assist in the advanced detection of the disease. Around 6000 images of the three kinds of chest X-Rays of patients with pneumonia, Covid, as well as completely normal patients, were used in the process. The paper concludes with the explicit comparison of all the models and their results. Primarily, a simple CNN model was opted for the scrutiny and then later on VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2 pre-trained models were utilised for anatomizing Covid-19 with respect to pneumonia and normal cases. In case of CNN, the maximum accuracy that was attained was 95.30% whereas, for the VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2, the maximum accuracies in correctly predicting the diseases were 95.63% , 96.02% , 94.82% , 95.23% and 93.39% respectively.

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Using deep learning to solve computer security challenges: a survey

Cited by 29 publications

References 47 publications

Deep security analysis of program code

Deep security analysis of program code

Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

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