Yazar "Kaşif, Ahmet" seçeneğine göre listele

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    (Ieee, 2018) Kaşif, Ahmet; Bilgin, Turgay Tugay
    In this study, universities are clustered based on the quantity and title of the academics employed by Turkish Universities. The data comes from both state and private universities and vocational schools. For clustering, partitional and hierarchical clustering methods were used. The dataset was obtained from the YOK academic search website using "web scraping" techniques implemented by the authors. Agglomerative clustering technique was found to yield better results regarding cluster sizes and intracluster distances. The Silhouette coefficient is used for clustering quality measurement. As a result of the study, Turkish universities were mainly composed of four clusters. These are instructor dominant universities, just instructor based vocational schools, professor and associate professor dominated universities and research assistant dominated universities. The results of this study may be used for academic staff employment planning in Turkish Universities.
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    (MDPI, 2021) Ozcan A.; Catal C.; Kaşif, Ahmet
    Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.
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    (Springer, 2022) Catal, Cagatay; Ozcan, Alper; Donmez, Emrah; Kaşif, Ahmet
    The correct affiliation of Emrah Donmez is "Department of Software Engineering, Bandirma Onyedi Eylul University, Balikesir, Turkey".The original version has been corrected.
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    (Springer, 2023) Catal, Cagatay; Ozcan, Alper; Donmez, Emrah; Kaşif, Ahmet
    Due to the increasing number of cyber incidents and overwhelming skills shortage, it is required to evaluate the knowledge gap between cyber security education and industrial needs. As such, the objective of this study is to identify the knowledge gaps in cyber security graduates who join the cyber security workforce. We designed and performed an opinion survey by using the Cyber Security Knowledge Areas (KAs) specified in the Cyber Security Body of Knowledge (CyBOK) that comprises 19 KAs. Our data was gathered from practitioners who work in cyber security organizations. The knowledge gap was measured and evaluated by acknowledging the assumption for employing sequent data as nominal data and improved it by deploying chi-squared test. Analyses demonstrate that there is a gap that can be utilized to enhance the quality of education. According to acquired final results, three key KAs with the highest knowledge gap are Web and Mobile Security, Security Operations and Incident Management. Also, Cyber-Physical Systems (CPS), Software Lifecycles, and Vulnerabilities are the knowledge areas with largest difference in perception of importance between less and more experienced personnel. We discuss several suggestions to improve the cyber security curriculum in order to minimize the knowledge gaps. There is an expanding demand for executive cyber security personnel in industry. High-quality university education is required to improve the qualification of upcoming workforce. The capability and capacity of the national cyber security workforce is crucial for nations and security organizations. A wide range of skills, namely technical skills, implementation skills, management skills, and soft skills are required in new cyber security graduates. The use of each CyBOK KA in the industry was measured in response to the extent of learning in university environments. This is the first study conducted in this field, it is considered that this research can inspire the way for further researches.
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    (IEE, 2021) Togay, Cengiz; Kaşif, Ahmet; Catal, Cagatay; Tekinerdogan, Bedir
    One of the key challenges in computer networks is network security. For securing the network, various solutions have been proposed, including network security protocols and firewalls. In the case of so-called packet-filtering firewalls, policy rules are implemented to monitor changes to the network and preserve the required security level. Due to the dramatic increase of devices, however, and herewith the rapid increase of the size of the policy rules, firewall policy anomalies occur more frequently. This requires careful implementation of the policy rules to ensure cost-efficient solutions for anomaly detection to support network security. In this study, we present an anomaly detection framework for detecting intrafirewall policy anomaly rules. The framework supports the simulation of packets through the firewall ruleset for validating and enhancing the security level of the network. The framework is validated using four different types of firewall policy anomalies. Experimental results demonstrate that the framework is effective and efficient in detecting firewall policy anomalies.
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    (Institute of Electrical and Electronics Engineers Inc., 2021) Kaşif, Ahmet; Toğay, Cengiz; Levi, Albert
    The Internet of Things (IoT) technology has entered our lives with industry and smart home technologies, and today it has started to be used in fields such as health, finance, transportation, energy and space research. Existing security solutions for IoT devices with limited hardware capacity do not provide integrated protection. In this study, it is aimed to increase the security of the IoT devices in the local network and the confidentiality of the data produced within the system by supporting the multiple SSID feature of the routers with the controller application placed on the gateway. Wireless communication security and packet transmission performance at the physical, network and application layers of the proposed architecture have been tested in real world conditions. Another contribution of the presented study is to limit the communication of devices with other devices in their own networks and with the external network in the light of the information defined by the manufacturer on a device basis. The results show that the proposed system offers a secure and performance efficient solution for protecting IoT environments in the local network.
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    (Institute of Electrical and Electronics Engineers Inc., 2020) Kaşif, Ahmet; Ortaç, Gizem; Ibis, E.; Bilgin, Turgay Tugay
    Organic crop production is an important technique which can help increase the quality and throughput of food production. In this study, similarity analysis of organic farming crop data of Turkish cities has been performed. The dataset has been collected from the web site of Turkish Ministry of Agriculture and Forestry. Some pre-processing techniques have been applied. Dynamic Time Warping distance has been used as a similarity metric. Results show that Dynamic Time Warping similarity is suitable for similarity detection of organic crop production. © 2020 IEEE.