How XAI can support Human Oversight in network traffic analysis.?
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eXplainable Artificial Intelligence (XAI) plays a crucial role in supporting Human Oversight in network traffic analysis by providing interpretable explanations for the decisions made by complex Machine Learning and Deep Learning models. XAI techniques like Deep SHAP, LIME, and ELI5 help break the black-box nature of these models, enabling humans to understand and trust the results. In the context of traffic accident anticipation, XAI methods like Grad-CAM generate human-interpretable saliency maps, aiding in explaining the decision-making process of accident anticipation models. Moreover, the proposal of a network traffic intrusion detection method (XAI-IDS) leverages interpretable artificial intelligence to detect malicious traffic intrusion, addressing concerns regarding the black-box effect in critical network environments. Overall, XAI enhances transparency, reliability, and human oversight in network traffic analysis.
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| Papers (4) | Insight |
|---|---|
Open access•Posted Content | Explainable AI (XAI) with Grad-CAM enhances human oversight in traffic accident anticipation by providing interpretable saliency maps, aiding in understanding and trust in AI-driven decisions. |
| XAI in IoT network attack detection provides human-level explanations through LIME, SHAP, and ELI5, enhancing transparency and reliability in network traffic analysis. | |
26 Jul 2021 3 Citations | XAI techniques like Deep SHAP can provide global interpretations of deep learning traffic classifiers, aiding human oversight by quantifying the importance of different modalities and inputs in classification outcomes. |
17 Dec 2022 1 Citations | XAI in IoT network attack detection provides human-level explanations through LIME, SHAP, and ELI5, enhancing transparency and reliability in network traffic analysis. |
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