Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

Software-defined networking (SDN) has been recognized for its potential in network programming and centralized control. However, this advancement brings forth critical security vulnerabilities. It is essential to understand that vulnerabilities, by their inherent nature, may lead to potential attacks if not addressed timely and appropriately. In this paper, we introduce a novel multi-modal deep transfer learning (MMDTL) framework tailored for effective attack detection in SDN environments that helps us to investigate a diverse spectrum of attack types. MMDTL framework comprehensively incorporates diverse data modalities — encompassing network traffic patterns, system logs, and user behavior analytic. A pivotal feature of this framework is its transfer learning approach, which enables the assimilation of insights from pre-trained models that subsequently increases the detection performance of attacks. We rigorously analyze the proposed framework with experiments on the intrusion detection evaluation dataset (CIC-IDS2017). Analyses results show the superiority of our framework with a detection accuracy 99.97%.Thus, MMDTL framework has a significant potential to support security in SDNs.

Afet sırasında ve sonrasında bir bölgedeki iletişim sistemlerinin çalışmaya devam etmesi hayati öneme sahip konulardan biridir. Afet ve diğer acil durum zamanlarında telekomünikasyon altyapısında oluşan hasarların tespiti ve tamiri oldukça büyük vakit kaybına neden olabileceği için tercih edilmemektedir. Ayrıca ortaya çıkabilecek hizmet kayıpları da afet bölgesindeki yardım ekiplerinin yaşayabileceği bir durum olmaktadır. Gerek afetlerden önce uyarı sistemlerinin tasarlanması gerekse de afet sürecinde iletişim sürekliliğinin sağlanması çok büyük öneme sahip olmaktadır. Böylece özellikle kaçınılmaz olan doğal afetlerden kaynaklanabilecek zararlar indirgenmiş olmaktadır. Bu amaç doğrultusunda gerçekleştirilen teknik ve bilimsel araştırmalar çok önemlidir. Araştırmaların kapsamında afet uyarı sistemleri, bir afetin etkilemeyeceği iletişim ağları ve alternatif haberleşme sistemleri bulunmaktadır. Bu çalışmada, son yıllarda çeşitli araştırmacılar tarafından yapılan afet ve acil durum yönetim uygulamaları hakkında bir derleme yapılmaktadır. Teknolojik açıdan farklı sistemlerin neler olduğu ve bunların işleyişi açıklanmaktadır. Bu çalışma ile aynı çatı altına alınan literatür örnekleri özellikle bilgisayar mühendisliği alanındaki gelişmeleri disiplinler arası bir platforma taşımış olmaktadır.

Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR.

Dijital dönüşümün hızla yaygınlaşması ile işlenen verilerin boyutları ve hacimleri de artmıştır. Büyük verileri işlemek, doğruluğu yüksek analizleri kısa sürede ve daha az kaynak kullanarak yapmak için yeni yöntem ve algoritmalar geliştirilmiştir. Bu çalışmada makine öğrenmesi ve derin öğrenme tekniklerinden ARIMA (Otoregresif Entegre Hareketli Ortalama), SARIMA (Mevsimsel ARIMA), Prophet (Facebook), XGBoost (En Büyük Eğim Artırma), LSTM (Uzun-Kısa Süreli Bellek), RNN (Yinelemeli Sinir Ağı) ve GRU (Geçitli Tekrarlayan Birim) algoritmaları kullanılarak tüketicilerin kontör tüketimleri zaman serileri yardımıyla tahmin edilmeye çalışılmıştır. Modelleri karşılaştırmak için MAE (Ortalama Mutlak Hata), MAPE (Ortalama Mutlak Yüzde Hata), RMSE (Kök Ortalama Karesel Hata) ve Determinasyon Katsayısı (R^2) kullanılmıştır. Elde edilen ölçüm sonuçlarına göre zaman serileri tahminlemesinde derin öğrenme tekniklerinin makine öğrenmesi yöntemlerine göre daha iyi sonuçlar verdiği gözlemlenmiştir. Çalışmada zaman serileri tahminlemesi üzerine dokuz farklı makine ve derin öğrenme yöntemi kullanılarak kapsamlı bir inceleme yapılmıştır. Literatürdeki benzer çalışmalar ile kıyaslandığında bu çalışmada konu oldukça geniş bir perspektiften incelenmiştir.