The Smart Handwritten Digits Recognition Using Machine Learning Algorithm

Mohamed, Nachaat; Josphineleela, R.; Madkar, Shankar Rakhamaji; Sena, Jessica; Alfurhood, Badria Sulaiman; Pant, Bhasker

doi:10.1109/icacite57410.2023.10182742

Cited by 5 publications

(1 citation statement)

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“…Network Traffic Analysis (NTA) plays a crucial role in supporting ML-based Network Intrusion Detection Systems (NIDS). By monitor-ing and extracting meaningful information from network traffic data, NTA enables the identification of significant features crucial for detecting zero-day attacks [25]. The application of Benford's law to identify these key features represents an innovative approach to optimizing ML models for NIDS [26].…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits

Mohamed,

Taherdoost,

Madanchian

2024

ICST Transactions on Scalable Information Systems

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This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical models to sophisticated deep learning frameworks and evaluate their effectiveness in identifying and addressing zero-day threats. The integration of ML with other cybersecurity mechanisms to develop adaptive, robust defense systems is also explored, alongside challenges such as data scarcity, false positives, and the constant arms race against cyber attackers. Special attention is given to innovative strategies that enhance real-time response and prediction capabilities. This review aims to synthesize current trends and anticipate future developments in machine learning technologies to better equip researchers, cybersecurity professionals, and policymakers in their ongoing battle against zero-day exploits.

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Section: Related Workmentioning

confidence: 99%