Yazar "Catal, Cagatay" seçeneğine göre listele
Listeleniyor 1 - 8 / 8
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A Firewall Policy Anomaly Detection Framework for Reliable Network Security(IEE, 2021) Togay, Cengiz; Kaşif, Ahmet; Catal, Cagatay; Tekinerdogan, BedirOne 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.Öğe Analysis of cyber security knowledge gaps based on cyber security body of knowledge(Springer, 2023) Catal, Cagatay; Ozcan, Alper; Donmez, Emrah; Kaşif, AhmetDue 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.Öğe Analysis of cyber security knowledge gaps based on cyber security body of knowledge (Sep, 10.1007/s10639-022-11261-8, 2022)(Springer, 2022) Catal, Cagatay; Ozcan, Alper; Donmez, Emrah; Kaşif, AhmetThe correct affiliation of Emrah Donmez is "Department of Software Engineering, Bandirma Onyedi Eylul University, Balikesir, Turkey".The original version has been corrected.Öğe Correction to: Analysis of cyber security knowledge gaps based on cyber security body of knowledge (Education and Information Technologies, (2023), 28, 2, (1809-1831), 10.1007/s10639-022-11261-8)(Springer, 2023) Catal, Cagatay; Özcan, Alper; Dönmez, Emrah; Kasif, AhmetThe correct affiliation of Emrah Donmez is "Department of Software Engineering, Bandirma Onyedi Eylul University, Balikesir, Turkey".The original version has been corrected. © 2022 Springer Science+Business Media, LLC, part of Springer Nature.Öğe Deep learning-based modelling of pyrolysis(Springer, 2024) Özcan, Alper; Kasif, Ahmet; Sezgin, İsmail Veli; Catal, Cagatay; Sanwal, Muhammad; Merdun, HasanPyrolysis is one of the thermochemical methods used to produce value-added products from biomass. Thermogravimetric analysis (TGA) is frequently used to examine the energy potential and thermal behavior of biomass, coal, and their blends. The investigation of the TGA data using Artificial Neural Networks (ANN) is one of the most important research areas in recent years. While there are different research papers on the use of Machine Learning (ML) in this field, there is a lack of systematic application of deep learning (DL) algorithms. As such, we applied DL algorithms together with ML algorithms to evaluate the predictive performance of thermal behaviors of proposed bioenergy sources. Thermal behavior of tomato, pepper, eggplant, squash, and cucumber harvest wastes, the equal mass (20%) mixture of them, and the blends of the mixture with coal in the ratios of 20, 33, and 50% under nitrogen atmosphere were investigated by the TGA and ML models. Based on the pyrolysis thermal behavior of the harvest wastes, the eggplant, pepper, tomato, and 5-biomass mixture had the highest conversion potential. According to the thermal behavior of co-pyrolysis of coal and harvest waste mixtures, it had positive effects on pyrolysis conversion degrees and temperature range compared to the coal, and therefore, they can be used as alternative sources for energy production. The MSE and R2 scores of Bi-directional LSTM demonstrate that an improved performance can be obtained with DL based solutions. Promising results were obtained when the Bi-directional LSTM is applied for modeling the pyrolysis. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.Öğe Federated Learning for Smart and Sustainable Aquaponics: A Decentralized AI Approach for Urban Resilience(Springer-Verlag Singapore Pte Ltd, 2025) Kasif, Ahmet; Catal, CagatayThe majority of machine learning models rely on centralized methods, which require large data transfers to central repositories. Federated learning, a decentralized machine learning approach, offers a solution by enabling local computations and aggregating local models to build a global model. While federated learning has been applied in some agricultural domains, its use in aquaponics remains unexplored. Aquaponics, a sustainable agricultural method combining fish farming and soil-less plant production, presents unique opportunities for federated learning applications, particularly in urban farming environments. By using edge-based federated learning, we improve scalability, data privacy, and sustainability and also, reduce data transmission needs in smart urban agriculture. This research, a collaboration between research teams from three countries, highlights how federated learning and deep learning can enhance environmental monitoring and sustainability in urban resilience strategies, particularly in smart agriculture. The Flower framework was used to implement federated learning, and ResNet-18 was employed for fish disease detection. This paper introduces novel contributions in federated learning and deep learning techniques for the management of aquaponics systems, highlighting the potential of these technologies to optimize aquaponics systems' efficiency.Öğe Hierarchical multi-head attention LSTM for polyphonic symbolic melody generation(Springer, 2024) Kasif, Ahmet; Sevgen, Selcuk; Ozcan, Alper; Catal, CagatayCreating symbolic melodies with machine learning is challenging because it requires an understanding of musical structure and the handling of inter-dependencies and long-term dependencies. Learning the relationship between events that occur far apart in time in music poses a considerable challenge for machine learning models. Another notable feature of music is that notes must account for several inter-dependencies, including melodic, harmonic, and rhythmic aspects. Baseline methods, such as RNNs, LSTMs, and GRUs, often struggle to capture these dependencies, resulting in the generation of musically incoherent or repetitive melodies. As such, in this study, a hierarchical multi-head attention LSTM model is proposed for creating polyphonic symbolic melodies. This enables our model to generate more complex and expressive melodies than previous methods, while still being musically coherent. The model allows learning of long-term dependencies at different levels of abstraction, while retaining the ability to form inter-dependencies. The study has been conducted on two major symbolic music datasets, MAESTRO and Classical-Music MIDI, which feature musical content encoded on MIDI. The artistic nature of music poses a challenge to evaluating the generated content and qualitative analysis are often not enough. Thus, human listening tests are conducted to strengthen the evaluation. Qualitative analysis conducted on the generated melodies shows significantly improved loss scores on MSE over baseline methods, and is able to generate melodies that were both musically coherent and expressive. The listening tests conducted using Likert-scale support the qualitative results and provide better statistical scores over baseline methods.Öğe Temporal fusion transformer-based prediction in aquaponics(Springer, 2023) Metin, Ahmet; Kasif, Ahmet; Catal, CagatayAquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for vegetation. Fluctuations in the ammonia levels affect the generated nitrate levels and influence farm yields. The sensor-based autonomous control of aquaponics can offer a highly rewarding solution, which can enable much more efficient ecosystems. Also, manual control of the whole aquaponics operation is prone to human error. Artificial Intelligence-powered Internet of Things solutions can reduce human intervention to a certain extent, realizing more scalable environments to handle the food production problem. In this research, an attention-based Temporal Fusion Transformers deep learning model was proposed and validated to forecast nitrate levels in an aquaponics environment. An aquaponics dataset with temporal features and a high number of input lines has been employed for validation and extensive analysis. Experimental results demonstrate significant improvements of the proposed model over baseline models in terms of MAE, MSE, and Explained Variance metrics considering one-hour sequences. Utilizing the proposed solution can help enhance the automation of aquaponics environments.
