XAI for intrusion detection system: comparing explanations based on global and local scope

Hariharan, Swetha; Robinson, Reece; Prasad, Rendhir R.; Thomas, Ciza; Balakrishnan, N.

doi:10.1007/s11416-022-00441-2

Cited by 25 publications

(13 citation statements)

References 20 publications

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“…These tools can help us obtain trends in the predictions from the trained models to explain the decisions made by the model. LIME and SHAP could be used for multi-class classification (with more than two classes) [ 31 ] , regression [ 32 ] and other types of applications such as image processing using CNNs [ 33 ] , etc. Since both tools have to run the trained model several times to produce explanations, it may not be useful for real-time explanations.…”

Section: Resultsmentioning

confidence: 99%

Interpretable AI for bio-medical applications

Sathyan¹,

Weinberg²,

Cohen³

2022

Complex Eng Syst

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This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model.

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Section: Resultsmentioning

confidence: 99%

Interpretable AI for bio-medical applications

Sathyan¹,

Weinberg²,

Cohen³

2022

Complex Eng Syst

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“…In this study, we combine our past achievements with recent XAI-based analysis methodologies. We shaped our study's approach in light of our results [1]- [16]. We designed system constructs to measure and analyze acceleration and angular velocity data using general human dynamics and statistical approaches [17]- [27].…”

Section: A Methodsmentioning

confidence: 99%

Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data

Kawakura¹,

Hirafuji

Ninomiya

et al. 2022

EJAI

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We use explainable artificial intelligence (XAI) based on Explain Like I’m 5 (ELI5), Partial Dependency Plot box (PDPbox), and Skater to analyze diverse physical agricultural (agri-) worker datasets. We have developed various promising body-sensing systems to enhance agri-technical advancement, training and worker development, and security. This includes wearable sensing systems (WSSs) that can capture real-time three-axis acceleration and angular velocity data related to agri-worker motion by analyzing human dynamics and statistics in different agri-environments, such as fields, meadows, and gardens. After investigating the obtained time-series data using a novel program written in Python, we discuss our findings and recommendations with real agri-workers and managers. In this study, we use XAI and visualization to analyze diverse data of experienced and inexperienced agri-workers to develop an applied method for agri-directors to train agri-workers.

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“…Explanation of how an algorithm reach a specific decision is important in analysing the decision making process of autonomous agent. Studies in Explainable Artificial Intelligence (XAl) is an evolving field which gives insight into the decision taking process of autonomous agents [27]. The factors affecting algorithmic decisions and bias introduced by algorithms are also relevant in this regard.…”

Section: Emotional Decision Makingmentioning

confidence: 99%

Ontology-Based Emotional Decision-Making in Self-Evolving Defensive Agents

Prasad¹,

Thomas

2022

IEEE Access

Self Cite

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Responding instantaneously in an unprecedented situation is inevitable to mitigate zero day attacks in the cyber world. This situation has strong resemblance with the fight for survival in biological world. Both being complex systems, the biological world and the cyber world have structural similarities. Emotions are the key factor used by biological beings to take intuitive decisions, which are key to survival in the natural world. Inspired from the idea of emotional decision making in natural world and the structural similarity shown by biological and cyber worlds, this paper proposes an ontological mechanism to implement artificial emotion-based decision making for autonomous agents in cyber security scenario. Emotions are modelled as manifestation of the goals of agents. Stimuli and response are connected through emotions using the ontological structure. The mechanism is proven useful in eliciting appropriate response for the stimuli using ontological reasoning through SPARQL queries.

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XAI for intrusion detection system: comparing explanations based on global and local scope

Cited by 25 publications

References 20 publications

Interpretable AI for bio-medical applications

Interpretable AI for bio-medical applications

Adaptations of Explainable Artificial Intelligence (XAI) to Agricultural Data Models with ELI5, PDPbox, and Skater using Diverse Agricultural Worker Data

Ontology-Based Emotional Decision-Making in Self-Evolving Defensive Agents

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