# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
#### 4.2.3 Cybersecurity  
In the domain of cybersecurity within IT, where ensuring system integrity and protecting against cyber threats is paramount, the strategic selection of XAI frameworks is pivotal. Captum, ELI5, and Skater, along with the Anchors Approach, Layer-wise Relevance Propagation, and CIAMPs, emerge as the standout solutions, each offering distinct advantages tailored to the diverse needs of cybersecurity professionals. Captum's advanced attribution methods provide detailed insights into the model behavior, aiding in the identification and mitigation of vulnerabilities within complex IT systems. ELI5's intuitive interface facilitates the effective communication of model insights to non-experts, fostering collaboration and informed decision-making across IT teams. Additionally, Skater's comprehensive interpretations enable cybersecurity professionals to understand feature interactions and optimize the system security with precision. The Anchors Approach's concise IF-THEN rules and Layer-wise Relevance Propagation's relevant insights offer further clarity in terms of threat detection and mitigation. Furthermore, CIAMP's model-agnostic explanations empower cybersecurity professionals to comprehend diverse cybersecurity models and identify potential weaknesses or vulnerabilities, enhancing the overall security resilience. By harnessing the capabilities of these frameworks, cybersecurity practitioners can bolster their organization's defenses, detect threats, and safeguard sensitive information against cyber-attacks.  
In [41; 80], the authors concentrate on the examination of encrypted traffic, particularly to enhance the accurate detection of DoH (DNS Over HTTPS) attacks. They incorporate Explainable AI techniques using SHAPs, enabling the visualization of individual feature contributions regarding the model's classification decisions. Similarly, EXPLAIN-IT [41] tackles the YouTube video quality classification issue within encrypted traffic scenarios. The methodology deals with unlabeled data, creating meaningful clusters, and providing explanations of the clustering outcomes to end-users. They utilize LIMEs to interpret clusters, employing a local-based strategy. Similarly, ROULETTE [81] focuses on network traffic, specifically employing attention coupled with a multi-output deep learning strategy to better distinguish between the categories of network intrusions. For post hoc explanations, they employ visual explanation maps generated through Grad-CAM.  
In [82], a two-stage ML-based Wireless Network Intrusion Detection System (WNIDS) is deployed to enhance the detection of impersonation and injection attacks within a Wi-Fi network. XAI techniques are integrated to provide insights into the decisions made by the initial ML model, particularly regarding instances predicted as impersonation or injection attacks. By employing SHAPs, the features that exert a significant influence on these predictions are identified. Remarkably, this set of features closely aligns with those pinpointed by the feature selection method utilized for the second-stage ML model.