This communication problem has migrated into the A high-velocity paradigm shift towards Explainable arena of machine learning (ML) and artificial Artificial Intelligence (XAI) has emerged in recent intelligence (AI) in recent times, giving rise to the need years. Highly complex Machine Learning (ML) models for and subsequent emergence of Explainable AI have flourished in many tasks of intelligence, and the (XAI). XAI has arisen from growing discontent with questions have started to shift away from traditional "black box" models, often in the form of neural metrics of validity towards something deeper: What is networks and other emergent, dynamic models (e.g., this model telling me about my data, and how is it agent-based simulation, genetic algorithms) that arriving at these conclusions? Inconsistencies between generate outcomes lacking in transparency. This has XAI and modeling techniques can have the undesirable also been studied through the lens of general machine effect of casting doubt upon the efficacy of these learning, where classic methods also face an explainability approaches. To address these problems, interpretability crisis for high dimensional inputs [1].

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of why one instance is an anomaly? and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and compared. The overall accuracy and F score of OPT_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.

I recently fell in love with Explainable AI (XAI). XAI is a set of methods aimed at making increasingly complex machine learning (ML) models understandable by humans. XAI could help bridge the gap between AI and humans. That is very much needed as the gap is widening. Machine learning is proving incredibly successful in tackling problems from cancer diagnostics to fraud detection.

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI). XAI is a complex subject and as far as I can see, I have not yet seen a taxonomy of XAI. Model-agnostic Explainability Approaches are designed to be flexible and do not depend on the intrinsic architecture of a model(such as Random forest). These approaches solely relate the inputs to the outputs. Model agnistic approaches could be explanation by simplification, explanation by feature relevance or explanation by visualizations.