Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times.

The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).

Ensuring robust cybersecurity in technological ecosystems involves addressing security vulnerabilities and cyberattacks. Although these two are closely related, they must be examined separately because the approaches and solutions for addressing them are quite different. Both security vulnerabilities and cyberattacks can be divided into the known and zero-day categories. This paper presents a systematic review that focuses on combating zero-day vulnerabilities and zero-day attacks. First, we established a taxonomy of current approaches and solutions for combating security vulnerabilities and cyberattacks. The focus was then shifted to zero-day threats. Zero-day solutions recently proposed in open-access articles indexed in the Web of Science (WoS) database were systematically analyzed. We searched for answers to the research questions of this systematic review, using inferences drawn from the reviewed articles. We examined the most adopted approaches, the most employed solutions, and the use of Artificial Intelligence (AI) in the reviewed articles to answer the research questions.

Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents’ states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

The use of classification models to predict the probability of an employee leaving the job can greatly improve the ability of human resources to intervene in a timely manner and rectify the situation to prevent attrition. After the so-called “The Great Resignation”, which took place after the pandemic period, studies on attrition have increased in order to regain the lost workforce and increase the loyalty of existing employees. In Turkey, however, studies in this field remain insufficient. The aim of this study is to predict the possible loss of employees, to take relevant measures and to reduce the financial losses of companies. The study was made available as SaaS (Software as a Service) based in order to facilitate accessibility to the target audience. For the analysis phase, the columns in the data set were analyzed with Pandas and Scikit-Learn library. Exploratory Data Analysis was performed by visualizing the analyzed data with Plotly and Seaborn libraries. Various insights were obtained with these inferences. The data was subjected to pre-processing stages such as cleaning, missing data completion, scaling, feature selection, data set balancing, dimension reduction, etc. to make it useful. Logistic Regression, KNN, SVM, Desicion Tree, Random Forest, ADABoost and Naive Bayes were used to train the model. In order to enrich the data set and increase the efficiency of the model, artificial intelligence-based synthetic data generation (Data Augmentation) was performed. In case of missing columns in the trained model, KNN-Data Imputation, one of the missing data completion methods, was used. In the optimization process of the model, hyperparameter optimizations were performed to achieve maximum efficiency with improvements. 5-fold crossvalidation prevented the model from over-learning. Performance was analyzed on the basis of Accuracy, Recall, Precision and F1-score metrics and the success criterion was determined as F1-Score. The model was presented as a web service with an accuracy rate of 0.99.