Browsing by Author "Peynirci, Gokcer"

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    Conference Object
    Citation - Scopus: 1
    An Energy Conservative Wireless Sensor Network Model for Object Tracking
    (IEEE, 2014) Gokcer Peynirci; Ilker Korkmaz; Muharrem Gurgen; Gurgen, Muharrem; Korkmaz, Ilker; Peynirci, Gokcer
    This study aims to find the relationship between energy consumption level and object tracking success in an object tracking sensor network (OTSN). Convenient use of energy proposes a great challenge for wireless sensor network (WSN) design and the balance between successful object tracking and low energy consumption is a tight one. To address this issue we propose a new network operation scheme for object tracking implement this scheme in Network Simulator 2 (ns-2) and present the obtained results of the conducted simulation experiments. The simulation results show that the proposed method can be used to track objects in a WSN network in an energy conservative manner.
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    Article
    Citation - WoS: 8
    Citation - Scopus: 14
    Feature Selection for Malware Detection on the Android Platform Based on Differences of IDF Values
    (SCIENCE PRESS, 2020) Gokcer Peynirci; Mete Eminagaoglu; Korhan Karabulut; Eminagaoglu, Mete; Peynirci, Gokcer; Karabulut, Korhan
    Android is the mobile operating system most frequently targeted by malware in the smartphone ecosystem with a market share significantly higher than its competitors and a much larger total number of applications. Detection of malware before being published on official or unofficial application markets is critically important due to the typical end users' widespread security inadequacy. In this paper a novel feature selection method is proposed along with an Android malware detection approach. The feature selection method proposed in this study makes use of permissions API calls and strings as features which are statically extractable from the Android executables (APK files) and it can be used in a machine learning process with different algorithms to detect malware on the Android platform. A novel document frequencybased approach namely Delta IDF was designed and implemented for feature selection. Delta IDF was tested upon three universal benchmark datasets that contain Android malware samples and highly promising results were obtained by using several binary classification algorithms.