AI for Threat Detection and Mitigation: Using AI to identify and respond to cybersecurity threats in real-time.

As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional security systems struggle to keep up with the volume, variety, and velocity of attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by enabling the automated detection, analysis, and mitigation of threats in real-time. By leveraging machine learning (ML) algorithms, natural language processing (NLP), and anomaly detection, AI can process vast amounts of data, identify patterns, and respond to potential threats faster and more accurately than conventional methods. This paper explores the role of AI in modern cybersecurity, focusing on its applications in threat detection and mitigation. It examines how AI systems, such as intrusion detection systems (IDS), security information and event management (SIEM) platforms, and endpoint protection tools, are being used to combat cyber threats. The paper also discusses the challenges associated with implementing AI in cybersecurity, including false positives, adversarial attacks, and the need for continuous training, and offers insights into future trends in AI-driven threat mitigation. Keywords: Cyber Security, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Deep Learning.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2024-12-19
DOI
https://doi.org/10.55041/ijsrem10936
Citations
4
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI for Threat Detection and Mitigation: Using AI to identify and respond to cybersecurity threats in real-time.

Gaurav Kashyap
4 citations
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Network Security and Intrusion Detection
1.40
article

AI for Threat Detection and Mitigation: Using AI to identify and respond to cybersecurity threats in real-time.

Gaurav Kashyap
article en
4 citations

Abstract

As the digital landscape evolves and cyber threats become increasingly sophisticated, traditional security systems struggle to keep up with the volume, variety, and velocity of attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by enabling the automated detection, analysis, and mitigation of threats in real-time. By leveraging machine learning (ML) algorithms, natural language processing (NLP), and anomaly detection, AI can process vast amounts of data, identify patterns, and respond to potential threats faster and more accurately than conventional methods. This paper explores the role of AI in modern cybersecurity, focusing on its applications in threat detection and mitigation. It examines how AI systems, such as intrusion detection systems (IDS), security information and event management (SIEM) platforms, and endpoint protection tools, are being used to combat cyber threats. The paper also discusses the challenges associated with implementing AI in cybersecurity, including false positives, adversarial attacks, and the need for continuous training, and offers insights into future trends in AI-driven threat mitigation. Keywords: Cyber Security, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Deep Learning.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 08(12)
Openalex Percentile: Top 16%
Network Security and Intrusion Detection
1.40
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.