首页|Findings in Artificial Intelligence Reported from University of Ha'il (SIFT: Sif ting file types-application of explainable artificial intelligence in cyber fore nsics)
Findings in Artificial Intelligence Reported from University of Ha'il (SIFT: Sif ting file types-application of explainable artificial intelligence in cyber fore nsics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting out of the University of Ha' il by NewsRx editors, research stated, "Artificial Intelligence (AI) is being ap plied to improve the efficiency of software systems used in various domains, esp eciAlly in the health and forensic sciences. Explainable AI (XAI) is one of the fields of AI that interprets and explains the methods used in AI." The news reporters obtained a quote from the research from University of Ha'il: "One of the techniques used in XAI to provide such interpretations is by computi ng the relevance of the input features to the output of an AI model. File fragme nt classification is one of the vital issues of file carving in Cyber Forensics (CF) and becomes chAllenging when the filesystem metadata is missing. Other majo r chAllenges it faces are: proliferation of file formats, file embeddings, autom ation, We leverage and utilize interpretations provided by XAI to optimize the c lassification of file fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a file fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the sel ected features to optimize file fragment classification. To improve multinomial classification, a Multilayer Perceptron model is developed and optimized with fi ve hidden layers, each layer with $$i\times n$$ i x n neurons, where i = the layer number a nd n = the total number of classes in the dataset."
University of Ha'ilArtificial Intellig enceEmerging TechnologiesMachine Learning
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