Abstract:
The rapid evolution of Artificial Intelligence (AI) has transformed the cyber threat landscape, enabling attackers to
generate sophisticated and automated attacks that bypass traditional security mechanisms. This study proposes a holistic
multimodal AI-Based Intrusion Detection System (AI-IDS) aimed at strengthening trust in intelligent systems by effectively
detecting AI-generated cyber-attacks across three dimensions: network traffic, malicious URLs, and phishing emails. The
proposed framework integrates an XGBoost-based Network Intrusion Detection System trained on the CSE-CIC-IDS2018
dataset with SMOTE-based balancing, an XGBoost-driven malicious URL detection module using lexical features, and
a hybrid CNN-LSTM model for identifying AI-generated phishing emails through semantic analysis. Experimental
results demonstrate strong performance, achieving 97% accuracy for network intrusion detection, 90.71% accuracy for
malicious URL detection, and 99.1% accuracy with an AUC of 0.997 for phishing email detection. A Streamlit-based
prototype secured via Cloudflare Tunnel illustrates practical deployment within a Zero Trust environment.
