Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Article Link Prediction for Completing Graphical Software Models Using Neural Networks(IEEE, 2023) Leblebici, Onur; Tuğlular, Tuğkan; Belli, FevziDeficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models. AuthorConference Object Kurt saldırıları için sentetik irislerde örnek seçilimi(IEEE, 2023) Akdeniz, Eyüp Kaan; Erdoğmuş, NesliIn this study, samples with higher potential to succeed in wolf attacks are picked among synthetically generated iris images, and the composed subset is shown to pose a more significant threat toward an iris recognition system backed by a Presentation Attack Detection (PAD) module with respect to randomly selected samples. Iris images generated by Deep Convolutional Generative Adversarial Networks (DCGAN) are firstly filtered by rejection sampling on PAD score distribution of real iris image PAD scores. Next, the probability of zero success in all attack attempts is calculated for each synthetic iris image, using real iris images in the training set, and match and non-match score distributions are calculated on those. Synthetic images with the lowest probabilities of zero success are included in the final set. Our hypothesis that this set would be more successful in wolf attacks is tested by comparing its spoofing performances with randomly selected sample sets.Conference Object Citation - Scopus: 1A Lightweight and Energy Efficient Secrecy Outage Probability-Based Friendly Jamming(IEEE, 2023) Yaman, Okan; Ayav, Tolga; Erten, Yusuf MuratThird parties and legitimate entities can reach and process users' private data through most wireless networks. However, attackers such as intruders and eavesdroppers may also try to exploit this property in communication. Hence, wireless networks are intrinsically more vulnerable to threats, unlike their wired alternatives. Cryptographic techniques are the conventional approaches to deal with that weakness. Nevertheless, they still need to meet the requirements of contemporary technologies, including IoT nodes with energy and processing power constraints. In that respect, friendly jamming (FJ) is one of the encouraging countermeasures to overcome the mentioned susceptibility since it has an energy-efficient and computation-friendly nature. However, that promising approach brings another challenge, applicability. Although various models exist against this issue, a lightweight scheme compliant with novel technologies is needed. Hence, we propose a more straightforward FJ model evaluated on cellular network-based simulations in this study. Moreover, introducing a lightweight secrecy outage probability definition increases robustness and energy efficiency. © 2023 IEEE.Article Citation - WoS: 3Citation - Scopus: 4Application of the Law of Minimum and Dissimilarity Analysis To Regression Test Case Prioritization(IEEE, 2023) Ufuktepe, Ekincan; Tuğlular, TuğkanRegression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods. To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.Conference Object Citation - WoS: 2Citation - Scopus: 2Effort Prediction With Limited Data: a Case Study for Data Warehouse Projects(IEEE, 2022) Unlu, Huseyin; Yildiz, Ali; Demirors, OnurOrganizations may create a sustainable competitive advantage against competitors by using data warehouse systems with which they can assess the current status of their operations at any moment. They can analyze trends and connections using up-to-date data. However, data warehouse projects tend to fail more often than other projects as it can be tough to estimate the effort required to build a data warehouse system. Functional size measurement is one of the methods used as an input for estimating the amount of work in a software project. In this study, we formed a measurement basis for DWH projects in an organization based on the COSMIC Functional Size Measurement Method. We mapped COSMIC rules on two different architectures used for DWH projects in the organization and measured the size of the projects. We calculated the productivity of the projects and compared them with the organization's previous projects and DWH projects in the ISBSG repository. We could not create an organization-wide effort estimation model as we had a limited number of projects. As an alternative, we evaluated the success of effort estimation using DWH projects in the ISBSG repository. We also reported the challenges we faced during the size measurement process.Article Citation - WoS: 16Citation - Scopus: 25A Privacy-Preserving Scheme for Smart Grid Using Trusted Execution Environment(IEEE, 2023) Akgün, Mete; Üstündağ Soykan, Elif; Soykan, GürkanThe increasing transformation from the legacy power grid to the smart grid brings new opportunities and challenges to power system operations. Bidirectional communications between home-area devices and the distribution system empower smart grid functionalities. More granular energy consumption data flows through the grid and enables better smart grid applications. This may also lead to privacy violations since the data can be used to infer the consumer's residential behavior, so-called power signature. Energy utilities mostly aggregate the data, especially if the data is shared with stakeholders for the management of market operations. Although this is a privacy-friendly approach, recent works show that this does not fully protect privacy. On the other hand, some applications, like nonintrusive load monitoring, require disaggregated data. Hence, the challenging problem is to find an efficient way to facilitate smart grid operations without sacrificing privacy. In this paper, we propose a privacy-preserving scheme that leverages consumer privacy without reducing accuracy for smart grid applications like load monitoring. In the proposed scheme, we use a trusted execution environment (TEE) to protect the privacy of the data collected from smart appliances (SAs). The scheme allows customer-oriented smart grid applications as the scheme does not use regular aggregation methods but instead uses customer-oriented aggregation to provide privacy. Hence the accuracy loss stemming from disaggregation is prevented. Our scheme protects the transferred consumption data all the way from SAs to Utility so that possible false data injection attacks on the smart meter that aims to deceive the energy request from the grid are also prevented. We conduct security and game-based privacy analysis under the threat model and provide performance analysis of our implementation. Our results demonstrate that the proposed method overperforms other privacy methods in terms of communication and computation cost. The execution time of aggregation for 10,000 customers, each has 20 SAs is approximately 1 second. The decryption operations performed on the TEE have a linear complexity e.g., 172800 operations take around 1 second while 1728000 operations take around 10 seconds. These results can scale up using cloud or hyper-scalers for real-world applications as our scheme performs offline aggregation.Conference Object Citation - Scopus: 3A Novel Countermeasure for Selective Forwarding Attacks in Iot Networks(IEEE, 2022) Yaman, Okan; Sokat, Barış; Ayav, Tolga; Erten, Yusuf MuratAs the Internet of Things (IoT) devices become more widespread there are rising public concerns about whether or not IoT devices and their services are secure. One of the major threats they face is selective forwarding attacks performed by malicious nodes. Although packets can be lost inherently due to network conditions, malicious nodes, such as those performing blackhole attacks, may deliberately drop some, but not all of them. Therefore, distinguishing these nodes from legitimate ones is not so easy. This study has proposed a lightweight countermeasure to deal with this kind of attack in IoT networks, using the standard IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The mechanism is based on Mobile Trusted Nodes (MTNs). For the given threat model, we showed that our model has robust detection accuracy and brings no additional overhead to the network. © 2022 IEEE.Article Citation - WoS: 2Citation - Scopus: 6Incremental Testing in Software Product Lines-An Event Based Approach(IEEE, 2023) Beyazıt, Mutlu; Tuğlular, Tuğkan; Öztürk Kaya, DilekOne way of developing fast, effective, and high-quality software products is to reuse previously developed software components and products. In the case of a product family, the software product line (SPL) approach can make reuse more effective. The goal of SPLs is faster development of low-cost and high-quality software products. This paper proposes an incremental model-based approach to test products in SPLs. The proposed approach utilizes event-based behavioral models of the SPL features. It reuses existing event-based feature models and event-based product models along with their test cases to generate test cases for each new product developed by adding a new feature to an existing product. Newly introduced featured event sequence graphs (FESGs) are used for behavioral feature and product modeling; thus, generated test cases are event sequences. The paper presents evaluations with three software product lines to validate the approach and analyze its characteristics by comparing it to the state-of-the-art ESG-based testing approach. Results show that the proposed incremental testing approach highly reuses the existing test sets as intended. Also, it is superior to the state-of-the-art approach in terms of fault detection effectiveness and test generation effort but inferior in terms of test set size and test execution effort.Article Citation - WoS: 3Citation - Scopus: 3A Domain-Specific Language for the Document-Based Model-Driven Engineering of Business Applications(IEEE, 2022) Leblebici, Onur; Kardaş, Geylani; Tuğlular, TuğkanTo facilitate the development of business applications, a domain-specific language (DSL), called DARC, is introduced in this paper. Business documents including the descriptions of the responsibilities, authorizations, and collaborations, are used as the first-class entities during model-driven engineering (MDE) with DARC. Hence the implementation of the business applications can be automatically achieved from the corresponding document models. The evaluation of using DARC DSL for the development of commercial business software was performed in an international sales, logistics, and service solution provider company. The results showed that the code for all business documents and more than 50% of the responsibility descriptions composing the business applications could be generated automatically by modeling with DARC. Finally, according to the users' feedback, the assessment clearly revealed the adoption of DARC features in terms of the DSL quality characteristics, namely functional suitability, usability, reliability, maintainability, productivity, extensibility, compatibility, and expressiveness.Editorial Message From the Symposium Chairs: Isssr 2022(IEEE, 2022) Wong, W. Eric; Wotawa, Franz; Zheng, Liwei; Tuğlular, TuğkanSecurity, safety, and reliability are the three most important attributes of a system. This is especially the case for mission-critical and life-threatening systems. As the complexity of many systems continues to grow, ensuring security, safety, and reliability has become very critical. Not only does existing technology and tool support need to be enhanced, but we also need new strategies and approaches to satisfy more strict requirements on system security, safety, and reliability. In response to this challenge, the Steering Committee of ISSSR has decided to change its conference name from International Symposium on System and Software Reliability to International Symposium on System Security, Safety, and Reliability. The acronym ISSSR is still the same, but the full name has been slightly modified to emphasize the importance of security, safety, and reliability of many systems.