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    Deceptive Patch Solutions for Protecting Industrial Control Systems Based on Discovered Vulnerabilities
    (2024) Dinler, Özlem Batur
    An increase has been observed in concerns about cyber security threats in smart energy management on a global scale. Industrial Control Systems, or simply ICSs, are frequently present in industries and essential infrastructures, e.g., water treatment facilities, nuclear and thermal plants, heavy industries, power production, and distribution systems. ICS devices are high-risk targets for attacks and exploitation with significant security difficulties for ICS vendors and asset owners. Like many consumer electronics, industrial systems are susceptible to a bevy of vulnerabilities that hackers can exploit to launch cyber attacks. Extensive use of ICSs in Critical Infrastructures (CI) increases the vulnerability of CI to cyber attacks and makes their protection a critical subject. This study first contributes to a novel line of research considering how deception can be used by defenders in strategic terms with the objective of introducing uncertainty into an adversary’s perception of a system patch management process in order to protect ICSs. Thus, we mention the advantages of patch models to improve the vulnerabilities of ICSs. We explore deceptive patch management models for the purpose of providing better insight into developing future cyber security techniques for ICS attacks. We propose deceptive patch management solutions as case studies for common ICS attacks.
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    Detection of Android Based Applications with Traditional Metaheuristic Algorithms
    (2023) Beştaş, Mehmet Şirin; Dinler, Özlem Batur
    The widespread use of devices connected to Android systems in various areas of human life has made it an attractive target for bad actors. In this context, the development of mechanisms that can detect Android malware is among the most effective techniques to protect against various attacks. Feature selection is extremely to reduce the size of the dataset and improve computational efficiency while maintaining the accuracy of the performance model. Therefore, in this study, the five most widely used conventional metaheuristic algorithms for feature selection in the literature, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Differential Evolution (DE), was used to select features that best represent benign and malicious applications on Android. The efficiency of these algorithms was evaluated on the Drebin-215 and MalGenome-215 dataset using five different machine learning (ML) method including Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM). According to the results obtained from the experiments, DE-based feature selection and RF classifier are found to have better accuracy. According to the findings obtained from the experiments, it was seen that DE-based feature selection and RF method had better accuracy rate.
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    ETKİN ELDE SEÇMELİ KARMA TOPLAYICI DEVRELERİN TASARIMI
    (2017) Dinler, Özlem Batur; Sertbaş, Ahmet
    Farklı toplayıcı devrelerin birleştirilmesiyle oluşturulan karma toplayıcılar, çok geniş ölçekte tümleştirme (VLSI-Very Large Scale Integration) devre tasarımına uygun, temel ve önemli aritmetik işlem birimidir. Paralel toplama işlemine dayanan elde seçmeli toplayıcı (CSLACarry Select Adder) devreleri, düzenli ve modüler olması nedeniyle, karma toplayıcı devre tasarımlarında sıklıkla kullanılmaktadır. Bu nedenle, yüksek performanslı VLSI devre tasarımı için CSLA toplayıcıları büyük önem kazanmıştır. Bu durum, araştırmacıları CSLA tabanlı karma toplayıcı devrelerin tasarım algoritmalarını geliştirmeye yöneltmiştir. Bu çalışmada, çeşitli elde iletimli toplayıcı devre yapılarını (CPACarry Propagate Adder), Temel Ünite (TU) ile birlikte kullanarak yüksek performanslı elde seçmeli (CSLA) karma toplayıcı devre tasarımları geliştirilmiştir. Bu amaçla, klasik elde iletimli toplayıcı yapıları olarak bilinen Elde Dalgalı Toplayıcı (RCA/ Ripple Carry Adder), Elde Atlamalı Toplayıcı (CSKA/Carry Skip Adder) ve Brent-Kung Paralel Önek Toplayıcı (paralel prefix adder) yapıları Temel Ünite (Basic Unit) ile birlikte kullanılarak karma toplayıcı devreleri oluşturulmuştur. Bu makalede incelenen CSLA/Brent_Kung-TU elde seçmeli karma toplayıcı devrelerinin 16 bit, 32 bit, 64 bit operand uzunlukları için devre tasarımları gerçeklenerek performans analizleri yapılmıştır. Bu çalışmada önerilen yeni Elde Seçmeli Karma Toplayıcı devrelerinin klasik Elde İletimli Toplayıcı ve klasik Elde Seçmeli Toplayıcı devrelerine göre daha etkin tasarımlar oldukları görülmüştür