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AI-Based Multi Cyber Threat Detection

Mrs. K. Sai Veena   , Mrs. Dr. V. Neelima
Assistant Professor  ,Associate Professor

Dept. of CSE (AI & ML)

Jyothishmathi Institute of Technology and Science Karimnagar, Telangana, India

N. Nagaraju ,P. Meghana
Dept. of CSE (AI & ML)

  Jyothishmathi Institute of Technology and Science

Karimnagar, Telangana, India

nimmanagaraju6@gmail.com

  palthepumeghana618@gmail.com

B. Sowmya ,G. Charvaka
Dept. of CSE (AI & ML)

Jyothishmathi Institute of Technology and Science

Karimnagar, Telangana, India

balesowmyabalesowmya@gmail.com

charvaka01@gmail.com

Abstract—The rapid expansion of digital communication plat- forms, cloud computing, online banking, and social networking services has significantly increased the scale and complexity of cyber threats. Modern attackers no longer depend on simple malware or static phishing emails; instead, they use advanced techniques such as social engineering, deepfake media, and malicious QR codes to deceive users and bypass conventional security mechanisms. These attacks are often carefully crafted and multi-vector in nature, making traditional rule-based and signature-based cybersecurity solutions increasingly ineffective. Furthermore, most existing security systems are designed to detect only a single type of threat, which limits their ability to provide comprehensive protection in real-world environments. This paper presents an AI-based multi cyber threat detection framework that integrates machine learning, deep learning, and natural language processing to provide real-time and in- telligent security. The proposed system analyzes textual data from emails and messages, visual content from images and videos, and encoded information extracted from QR codes in order to detect phishing attempts, identify manipulated deepfake media, and recognize malicious QR-based exploits. Experimental evaluation shows that the proposed approach achieves improved accuracy, precision, and recall compared to traditional single- threat detection systems, while maintaining low computational latency, making it suitable for deployment in modern digital infrastructures.

Index Terms—Cyber security, phishing detection, deepfake de- tection, QR code attacks, social engineering, artificial intelligence, deep learning, natural language processing