Extracting State Models for Black-Box Software Components.

Suman, Rajiv Ranjan; Mall, Rajib; Sukumaran, Srihari; Satpathy, Manoranjan

doi:10.5381/jot.2010.9.3.a3

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Machine-learning models are called black-box models because the internal algorithms used are complex, making it difficult to explain the reasoning behind the resulting predictions. However, there are thousands or millions of hyperparameters inside the model that are irrelevant to the input value but affect the predictive power [22]. So many studies have been conducted to interpret these prediction processes and validate the reliability of the results.…”

Section: Machine-learning Model and Explainable Aimentioning

confidence: 99%

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

2022

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Pedestrian-friendly cities are a recent global trend due to the various urbanization problems. Since humans are greatly influenced by sight while walking, this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. In this study, vast amounts of visual data were collected and analyzed using computer vision techniques. Furthermore, these data were analyzed through a machine learning prediction model and SHAP algorithm. As a result, every visual feature of the streetscape, for example, the visible area and urban design quality, had a greater effect on pedestrian satisfaction than any physical features. Therefore, to build a street with high pedestrian satisfaction, the perspective of pedestrians must be considered, and wide sidewalks, fewer lanes, and the proper arrangement of street furniture are required. In conclusion, visually, low enclosure, adequate complexity, and large green areas combine to create a highly satisfying pedestrian walkway. Through this study, we could suggest an approach from a visual perspective for the pedestrian environment of the street and see the possibility of using computer vision techniques.

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Section: Machine-learning Model and Explainable Aimentioning

confidence: 99%

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

2022

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“…AI technology can process a large amount of work in real-time, but it is difficult for users to trust it because the basis of and process for the results cannot be known [14]. An interpretation of an AI model should make it possible to overcome this limitation.…”

Section: Xaimentioning

confidence: 99%

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Lee

2022

Applied Sciences

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Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainability through an interpretation of AI prediction results and a reliability analysis of AI predictions based on explainable artificial intelligence (XAI). In addition, we propose a method for screening high-quality data that can efficiently detect false predictions based on reliability indicators. Through this, even a small security team can quickly respond to false predictions. To validate the proposed method, experiments were conducted using the IDS dataset and the malware dataset. AI errors were detected better than they could be by the existing AI models, with about 262% in the IDS dataset and 127% in the malware dataset from the top 10% of analysis targets. Therefore, the ability to respond to cyberattacks can be improved using the proposed method.

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“…Artificial intelligence technology can process a large amount of work in real time, but it is difficult for users to trust it because the basis and process for the result cannot be known [12]. Interpretation of the AI model should make it possible to overcome this limitation.…”

Section: B Explainable Artificial Intelligence (Xai)mentioning

confidence: 99%

Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

Kim

Lee

et al. 2021

IEEE Access

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Many previous studies have investigated applying artificial intelligence (AI) to cyber security. Despite considerable performance advantages, AI for cyber security requires final confirmation by an analyst, e.g. malware misdetection can cause significant adverse side effects. Thus, all AI predictions must be checked by a human analyst, which poses a major obstacle to AI expansion. This paper proposes a reliability indicator for AI prediction using explainable artificial intelligence and statistical analysis techniques. This will enable analysts with limited daily workload to focus upon valuable data, and quickly verify AI predictions. Analysts generally make decisions based on several features that they know exactly what they mean, rather than all available features. Since the proposed reliability indicator is calculated using features meaningful to analysts, it can be easily understood and hence speed final decisions. To verify the performance of the proposed method, an experiment was conducted using the IDS dataset and the malware dataset. The AI error was detected better than the existing AI model at about 114% in IDS and 95% in malware. Thus, cyberattack response could be greatly improved by adopting the proposed method.

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Extracting State Models for Black-Box Software Components.

Cited by 11 publications

References 16 publications

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

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