Malware represents a real threat to information systems, because of the damage it causes. This threat is growing today, as these programs take on more complex forms. This means they escape traditional malware detectio...Malware represents a real threat to information systems, because of the damage it causes. This threat is growing today, as these programs take on more complex forms. This means they escape traditional malware detection methods. Hence the need for artificial intelligence, more specifically Deep Learning, which could detect malware more effectively. In this article, we’ve proposed a model for malware detection using artificial neural networks. Our approach used data from the characteristics of machines, particularly computers, to train our Deep Learning algorithm. This model demonstrated an accuracy of around 83% in predicting the presence of malware on a machine. Thus, the use of artificial neural networks for malware detection has shown his ability to assimilate complex, non-linear patterns from data.展开更多
In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as constru...In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.展开更多
文摘Malware represents a real threat to information systems, because of the damage it causes. This threat is growing today, as these programs take on more complex forms. This means they escape traditional malware detection methods. Hence the need for artificial intelligence, more specifically Deep Learning, which could detect malware more effectively. In this article, we’ve proposed a model for malware detection using artificial neural networks. Our approach used data from the characteristics of machines, particularly computers, to train our Deep Learning algorithm. This model demonstrated an accuracy of around 83% in predicting the presence of malware on a machine. Thus, the use of artificial neural networks for malware detection has shown his ability to assimilate complex, non-linear patterns from data.
文摘In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.
