This survey presents a comprehensive overview of Machine Learning (ML) methods for cybersecurity intrusion detection systems, with a specific focus on recent approaches based on Deep Learning (DL). The review analyzes recent methods with respect to their intrusion detection mechanisms, performance results, and limitations as well as whether they use benchmark databases to ensure a fair evaluation. In addition, a detailed investigation of benchmark datasets for cybersecurity is presented. This paper is intended to provide a road map for readers who would like to understand the potential of DL methods for cybersecurity and intrusion detection systems, along with a detailed analysis of the benchmark datasets used in the literature to train DL models.
Through this chapter, problems in cybersecurity and the potential AIbased solutions are first introduced. Then, several proposed methods based on Machine Learning in cybersecurity are discussed with examples to give detailed insights to the reader. Finally, the chapter is concluded with an overview of open topics as well as potential directions in cybersecurity within the scope of conclusions of researches in the literature. D. Gumusbas (B) • T.
Offline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.
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