AI Algorithms Enhance Threat Detection and Reinforce Data Protection

Ai is growing with a colossal speed which can't be ignored because Ai operates in numerus business sectors the security of data becomes very important. In old days, the security of data was a concern of big businesses because of the amounts of sensitive information they possess, but now its everyone's interest to protect data. Protecting computer systems from malware is a critical cybersecurity priority for both individual users and businesses, as even a single attack can lead to data breaches and significant losses. The prevalence of severe losses and frequent attacks underscores the necessity for accurate and timely detection methods. Current static and dynamic detection approaches often fall short, particularly in addressing zero-day attacks. Therefore, employing machine learning techniques becomes essential. This paper examines the key aspects and challenges of machine learning-based malware detection, while also exploring feature representation and classification methods for reinforcing data protection.

Authors

Institutions

Publication Details

Published
2024-11-21
DOI
https://doi.org/10.1109/telecom63374.2024.10812229
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Algorithms Enhance Threat Detection and Reinforce Data Protection

Maria Nenova, Kiril Ivanov, Elina Tlachenska
Adversarial Robustness in Machine Learning
article

AI Algorithms Enhance Threat Detection and Reinforce Data Protection

Maria Nenova, Kiril Ivanov, Elina Tlachenska
article en

Abstract

Ai is growing with a colossal speed which can't be ignored because Ai operates in numerus business sectors the security of data becomes very important. In old days, the security of data was a concern of big businesses because of the amounts of sensitive information they possess, but now its everyone's interest to protect data. Protecting computer systems from malware is a critical cybersecurity priority for both individual users and businesses, as even a single attack can lead to data breaches and significant losses. The prevalence of severe losses and frequent attacks underscores the necessity for accurate and timely detection methods. Current static and dynamic detection approaches often fall short, particularly in addressing zero-day attacks. Therefore, employing machine learning techniques becomes essential. This paper examines the key aspects and challenges of machine learning-based malware detection, while also exploring feature representation and classification methods for reinforcing data protection.

Technical University of Sofia (BG)
Climate action
Openalex Percentile: Top 78%
Adversarial Robustness in Machine Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.