A comparative study to deep learning for pattern recognition, by using online and batch learning; taking cybersecurity as a case

Djellali, Choukri; Adda, Mehdi; Moutacalli, Mohamed Tarik

doi:10.1145/3341161.3343533

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…That is, the training dataset is having the target vector. Whereas in Unsupervised Learning, algorithms learn from the training data but without any target vector available (Sharma et al, 2016;Martínez et al, 2019;Apruzzese et al, 2018;Hu and Tan, 2017;Yavanoglu and Aydos, 2017;Djellali et al, 2019). Different algorithms and computation approaches are used in supervised techniques.…”

Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

“…With these experiments, (Alom et al, 2015) could identify unknown attacks and, after 50 iterations, achieved 97.5% of accuracy. Djellali et al (2019), designed two deep learning techniques as Batch Gradient Descent and Stochastic Gradient Descent which are compared and tested on a resampling method for cybersecurity. Batch Gradient Descent is an iterative technique that uses complete input training patterns in order to optimize a cost function.…”

Section: Deep Learning Solutions To Cyber Securitymentioning

confidence: 99%

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Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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Section: Machine Learning-based Approaches For Cyber Security Problemsmentioning

confidence: 99%

Section: Deep Learning Solutions To Cyber Securitymentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

A comparative study to deep learning for pattern recognition, by using online and batch learning; taking cybersecurity as a case

Cited by 1 publication

References 8 publications

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

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