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    Assessment of environmental factors affecting software reliability: a survey study
    (Tubitak Scientific & Technological Research Council Turkey, 2020) Ozcan, Alper; Catal, Cagatay; Togay, Cengiz; Tekinerdogan, Bedir; Donmez, Emrah
    Currently, many systems depend on software, and software reliability as such has become one of the key challenges. Several studies have been carried out that focus on the impact of external environmental factors that impact software reliability. These studies, however, were all carried out in the same geographical context. Given the rapid developments in software engineering, this study aims to identify and reinvestigate the environmental factors that impact software reliability by also considering a different context. The environmental factors that have an impact on software reliability as reported in earlier studies have been analyzed and synthesized. Subsequently, a survey study is conducted to analyze the impact of 32 environmental factors from the perspective of multiple stakeholders. Several statistical analysis methods were applied for the analysis. Data were collected from 24 organizations and 70 software professionals. Most factors shown in top 10 lists of previous studies remain in the top 10 in our study, but their order is different. Testing coverage is now the most significant factor and testing effort is considered as the second most significant factor. The environmental factors defined previously retain their impact. The ordering of the importance of the environmental factors has changed though.
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    A hybrid DNN-LSTM model for detecting phishing URLs
    (Springer London Ltd, 2023) Ozcan, Alper; Catal, Cagatay; Donmez, Emrah; Senturk, Behcet
    Phishing is an attack targeting to imitate the official websites of corporations such as banks, e-commerce, financial institutions, and governmental institutions. Phishing websites aim to access and retrieve users' important information such as personal identification, social security number, password, e-mail, credit card, and other account information. Several anti-phishing techniques have been developed to cope with the increasing number of phishing attacks so far. Machine learning and particularly, deep learning algorithms are nowadays the most crucial techniques used to detect and prevent phishing attacks because of their strong learning abilities on massive datasets and their state-of-the-art results in many classification problems. Previously, two types of feature extraction techniques [i.e., character embedding-based and manual natural language processing (NLP) feature extraction] were used in isolation. However, researchers did not consolidate these features and therefore, the performance was not remarkable. Unlike previous works, our study presented an approach that utilizes both feature extraction techniques. We discussed how to combine these feature extraction techniques to fully utilize from the available data. This paper proposes hybrid deep learning models based on long short-term memory and deep neural network algorithms for detecting phishing uniform resource locator and evaluates the performance of the models on phishing datasets. The proposed hybrid deep learning models utilize both character embedding and NLP features, thereby simultaneously exploiting deep connections between characters and revealing NLP-based high-level connections. Experimental results showed that the proposed models achieve superior performance than the other phishing detection models in terms of accuracy metric.