Yazar "Dener, Murat" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Android Malware Analysis and Benchmarking with Deep Learning
    (2021) Sönmez, Yusuf; Kural, Taylan; Dener, Murat
    Android operating system has been widely used in mobile phones, televisions, smart watches, cars and other Internet of Things applications with its open source structure and wide application market. This widespread use and open-source nature make this operating system and its devices easy and lucrative targets for cyber attackers. One of the most used methods often preferred by attackers is to install malware applications on user devices. As the number of malware programs is increasing, the traditional methods can be insufficient in detecting. Machine learning-based and deep learning-based methods have achieved promising results in malware detection and classification. Deep learning-based methods have an increasing use in malware detection, thanks to the low need for domain expertise and their feature extracting capabilities. Convolutional neural networks (CNN) are popular deep learning methods that are widely used in visual analysis of malware by transforming them to images. In this study, a batch fine-tune transfer learning method was proposed and used on popular CNN models, Xception, ResNet, VGG, Inception, MobileNet, DenseNet, NasNet, EfficientNet. According to the results, the models were analyzed and compared with metrics like accuracy, specificity, recall, precision, F1-score.
  • Küçük Resim
    Öğe
    Performance Analysis of Machine Learning Algorithms for Malware Detection by Using CICMalDroid2020 Dataset
    (2021) Sönmez, Yusuf; Salman, Meltem; Dener, Murat
    In parallel with the developments in technology, access to information has become easier. Although this situation has a positive effect on our lives, it is an inevitable fact that information has become a target by malicious people. Theft of information and its use as a threat by these people have caused concerns about information security. Malware developed for these purposes poses a great danger to the security of information. In the face of this situation, which increases as access to information becomes easier, researchers have accelerated their work on detecting and preventing malware and ensuring information security. In the literature, it is seen that the detection of malicious software has been carried out with different studies. In this study, malware detection was carried out using the WEKA program. The effects of different machine learning classifiers, feature extraction and the parameters that affect the performance of the classification that gives the best result were examined in the analyzes made with the CICMalDroid2020 dataset. The results are presented in detail.