The main purpose of the present study is to investigate the possible application of decision tree in landslide susceptibility assessment. The study area having a surface area of 174.8 km² locates at the northern coast of the Sea of Marmara and western part of Istanbul metropolitan area. When applying data mining and extracting decision tree, geological formations, altitude, slope, plan curvature, profile curvature, heat load and stream power index parameters are taken into consideration as landslide conditioning factors. Using the predicted values, the landslide susceptibility map of the study area is produced. The AUC value of the produced landslide susceptibility map has been obtained as 89.6%. According to the results of the AUC evaluation, the produced map has exhibited a good enough performance.

Advances in Industry 4.0, IoT and mobile systems have led to an increase in the number of malware threats that target these systems. The research shows that classification via the use of computer vision and machine learning methods over byte-level images extracted from malware files could be an effective static solution. In this study, in order to detect malware, we have employed various contemporary convolutional neural networks (Resnet, Inception, DenseNet, VGG, AlexNet) that have proven success in image classification problem and compared their predictive performance along with duration of model production and inference. In addition, a novel malware data set involving 8750 training and 3644 test instances over 25 different classes was proposed and used. As a result of the experiments carried out with 3-channel (RGB) images obtained, the highest success in terms of accuracy was determined as 97.48% by using DenseNet networks.

Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks. This study conducted a large-scale anti-phishing training with more than 31000 participants and 144 different simulated phishing attacks to develop a data-driven model to classify how users would perceive a phishing simulation. Furthermore, we analyze the results of our large-scale anti-phishing training and give novel insights into users’ click behavior. Analyzing our anti-phishing training data, we find out that 66% of users do not fall victim to credential-based phishing attacks even after being exposed to twelve weeks of phishing simulations. To further enhance the phishing awareness-training effectiveness, we developed a novel manifold learning-powered machine learning model that can predict how many people would fall for a phishing simulation using the several structural and state-of-the-art NLP features extracted from the emails. In this way, we present a systematic approach for the training implementers to estimate the average “convincing power” of the emails prior to rolling out. Moreover, we revealed the top-most vital factors in the classification. In addition, our model presents significant benefits over traditional rule-based approaches in classifying the difficulty of phishing simulations. Our results clearly show that anti-phishing training should focus on the training of individual users rather than on large user groups. Additionally, we present a promising generic machine learning model for predicting phishing susceptibility.

Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods