In recent years, due to the emergence of boundless communication paradigm and increased number of networked digital devices, there is a growing concern about cybersecurity which tries to preserve either the information or the communication technology of the system. Intruders discover new attack types day by day, therefore to prevent these attacks firstly they need to be identified correctly by the used intrusion detection systems (IDSs), and then proper responses should be given. IDSs, which play a very crucial role for the security of the network, consist of three main components: data collection, feature selection/conversion and decision engine. The last component directly affects the efficiency of the system and use of machine learning techniques is one of most promising research areas. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate with its distinctive learning mechanism. Consequently, it has been started to use in IDS systems. In this paper, it is aimed to survey deep learning based intrusion detection system approach by making a comparative work of the literature and by giving the background knowledge either in deep learning algorithms or in intrusion detection systems.

Phishing websites are malicious sites which impersonate as legitimate web pages and they aim to reveal users important information such as user id, password, and credit card information. Detection of these phishing sites is a very challenging problem because phishing is mainly a semantics-based attack, which especially abuses human vulnerabilities, however not network or system vulnerabilities. As a software detection scheme, two main approaches are widely used: blacklists/whitelists and machine learning approaches. Machine learning solutions are able to detect zero-hour phishing attacks and they have superior adaption for new types of phishing attacks, therefore they are mainly preferred. To use this type of solution features of input must be selected carefully. The whole performance of the solution depends on these features. Therefore, in this paper, it is aimed to list and identify the important features for machine learning-based detection of phishing websites.

In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.

Gerçek zamanlı çalışan sistemlerde görüntü işleme uygulamaları yapmak son zamanlarda oldukça popüler olan bir konu haline gelmiştir. Yapay zekâ alanının alt dallarından biri olan derin öğrenme yöntemleri ve görüntülerden nesne tespiti yapma alanında kullanılan görüntü işleme algoritmaları birlikte kullanılarak, otonom otomobiller, otonom insansız hava araçları, yardımcı robot teknolojileri, engelli ve yaşlı bireyler için asistan teknolojileri gibi birçok alanda uygulamalar geliştirilmektedir. Yapılan çalışmada, görme engelli bireyler, otonom araçlar ve robotlar tarafından kullanılabilecek yardımcı bir teknoloji sistemi tasarlamak için dokunsal parke yüzeylerinin derin öğrenme yöntemleriyle tespit edilmesi gerçekleştirilmiştir. Geleneksel görüntü işleme algoritmalarının aksine bu çalışmada derin öğrenme yöntemleri ile görüntü işleme algoritmaları birlikte kullanılmıştır. Nesne tespit etme yöntemleri içinde en iyi yöntemlerden biri olan You Only Look Once-V3(YOLO-V3) modeli DenseNet modeli ile birleştirilerek YOLOV3-Dense modeli oluşturulmuştur. YOLO-V2, YOLO-V3 ve YOLOV3Dense modelleri tarafımızca oluşturulmuş olan ve içerisinde 4580 etiketli görsel bulunan Marmara Dokunsal Parke Yüzeyi(MDPY) veri seti üzerinde ayrı ayrı eğitildikten sonra performansları test veri seti üzerinde birbirleri ile karşılaştırılmıştır. %89 F1-skor, %92 ortalama hassasiyet ve %81 IoU değerleri ile YOLOV3-Dense modelinin dokunsal parke yüzeyi tespit etmede diğer modellerden daha iyi olduğu gözlemlenmiştir. Saniyede 60 kare çalışma hızı ile YOLOV3-Dense modeli gerçek zamanlı çalışan sistemlerde de kullanılabilmektedir.