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Home/Authors/Maaz Hassan

Maaz Hassan

2 indexed papers

Recent (6 mo)
2
With code
0
Influential cites
0
Benchmarked
0

Publications per year

2
26

Top categories

Crypto×2AI×2ML×2

Frequent co-authors

Ambreen Aslam2×
Bibi Zahra2×
Muhammad Khuram Shahzad2×

Research Timeline

2026
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interpretability.

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interpretability.

Highlighted terms show continued research focus across papers

Papers

cs.CRcs.AIcs.LGRecentMay 28, 2026

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

Ambreen Aslam, Maaz Hassan, Bibi Zahra, Muhammad Khuram Shahzad

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

View →
cs.CRcs.AIcs.LGRecentMay 28, 2026

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

Ambreen Aslam, Maaz Hassan, Bibi Zahra, Muhammad Khuram Shahzad

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

View →