Applications of Machine Learning in Cyber Security Domain

Iyer, Sailesh; Rajagopal, Sridaran

doi:10.4018/978-1-5225-9611-0.ch004

Cited by 10 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major drawback of the support vector machine is that it consumes an immense amount of space and time. SVM requires data trained on different time intervals to produce better results for a dynamic dataset [83].…”

Section: Support Vector Machinementioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

View full text Add to dashboard Cite

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

View full text Add to dashboard Cite

show abstract

“…When 'n' dimensional space is considered, there occurs possibility of 2 n attribute subsets termed as subspace, these subspaces increase in number exponentially as the dimensionality increases [24][25].…”

Section: Related Workmentioning

confidence: 99%

Cyber Threat Detection Based On Angle-Based Subspace Anomaly For Machine Learning Applications

Revathy

2022

Preprint

View full text Add to dashboard Cite

In real time applications related to social media, conventional anomaly detection techniques are not applicable as the accuracy is degraded due to higher dimensionality thereby hampering these applications leading to cyber threat. This paper focuses on an approach that can select feature subspaces of social media which have meaningful information and thereby conduct anomaly detection in the projected subspace correspondingly. Major goal is to maintain the accuracy of detection in the circumstance of high dimensionality detecting cyber threat. This approach determines the angle between any of the two lines for one of the anomaly candidate specifically where the first line is in connection with relevant data points along with centers of adjacent points and the other line is any parallel axis line. For a particular candidate in social media, any dimension having smallest angle with the first line is chosen as subspace parallel to the axis. In the projected subspace, the local outlierness is measured for an object by introducing normalized values of Mahalanobis distance. Artificial datasets are constructed for comparing proposed approach of detecting cyber threats in social media comprehensively and found to be accurate for machine learning applications.

show abstract

“…Indeed, ML techniques are adopted in multidisciplinary problems. The fields of application include, for example, biomedical problems (Kourou et al, 2014), cyber security (Ford & Siraj, 2014), and finally, manufacturing processes (Rai et al, 2021). In this last field, ML becomes strategic to face fundamental issues related to the quality of products and efficiency of the systems.…”

Section: Introductionmentioning

confidence: 99%

Application of Mask R-CNN for Defect Detection in Printed Circuit Board manufacturing

Calabrese,

Agnusdei,

Fontana

et al. 2023

Preprint

View full text Add to dashboard Cite

In the last decades, machine vision and Machine Learning (ML) techniques have seen significant improvements in the development of new algorithms thanks to the increment of hardware performance. For this reason, applying computer vision for specific technological problems became an important opportunity to introduce some significant improvements in the manufacturing context. Indeed, several studies on the application of ML in the manufacturing process are available. A good application of ML is monitoring the qualitative aspects of a manufacturing process. This paper proposes a preliminary study to analyze the ML capabilities to perform Automated Optical Inspection (AOI) for quality control in the manufacturing of Printed Circuit Boards (PCBs). In this specific case, the target has been to investigate the performance of the method Mask R-CNN to individuate the main PCB defects after the manufacturing process. This study has been performed considering an available open-source dataset employed by other ML techniques. For this reason, this study has aimed to verify the effectiveness of the adopted ML solution to manage this application. The chosen open-source dataset individuates the opportune class of products and related defects for the context of interest. In this specific case, this work has been carried out to gather know-how for further activity related to AOI for quality control in the assembly of PCBs employed in the aerospace field.

show abstract

Applications of Machine Learning in Cyber Security Domain

Cited by 10 publications

References 19 publications

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Cyber Threat Detection Based On Angle-Based Subspace Anomaly For Machine Learning Applications

Application of Mask R-CNN for Defect Detection in Printed Circuit Board manufacturing

Contact Info

Product

Resources

About