UAV Based 5G Network: A Practical Survey Study

Unmanned aerial vehicles (UAVs) are anticipated to significantly contribute to the development of new wireless networks that could handle high-speed transmissions and enable wireless broadcasts. When compared to communications that rely on permanent infrastructure, UAVs offer a number of advantages, including flexible deployment, dependable line-of-sight (LoS) connection links, and more design degrees of freedom because of controlled mobility. Unmanned aerial vehicles (UAVs) combined with 5G networks and Internet of Things (IoT) components have the potential to completely transform a variety of industries. UAVs may transfer massive volumes of data in real-time by utilizing the low latency and high-speed abilities of 5G networks, opening up a variety of applications like remote sensing, precision farming, and disaster response. This study of UAV communication with regard to 5G/B5G WLANs is presented in this research. The three UAV-assisted MEC network scenarios also include the specifics for the allocation of resources and optimization. We also concentrate on the case where a UAV does task computation in addition to serving as a MEC server to examine wind farm turbines. This paper covers the key implementation difficulties of UAV-assisted MEC, such as optimum UAV deployment, wind models, and coupled trajectory-computation performance optimization, in order to promote widespread implementations of UAV-assisted MEC in practice. The primary problem for 5G and beyond 5G (B5G) is delivering broadband access to various device kinds. Prior to discussing associated research issues faced by the developing integrated network design, we first provide a brief overview of the background information as well as the networks that integrate space, aviation, and land

5G Network Management, Orchestration, and Architecture: A Practical Study of the MonB5G project

The cellular device explosion in the past few decades has created many different opportunities for development for future generations. The 5G network offers a greater speed in the transmissions, a lower latency, and therefore greater capacity for remote execution. The benefits of AI for 5G network slicing orchestration and management will be discussed in this survey paper. We will study these topics in light of the EU-funded MonB5G project that works towards providing zero-touch management and orchestration in the support of network slicing at massive scales for 5G LTE and beyond

A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE's hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.This work was made possible by NPRP Grant # NPRP9-005-1-002 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu

Penilaian tingkat kesehatan pada perusahaan perbankan syariah menggunakan metode RGEC: Studi pada Bank Mandiri Syariah, Bank Muamalat Indonesia dan Bank BNI Syariah

INDONESIA: Melalui Bank Indonesia pada tahun 2011 Pemerintah telah menetapkan bahwa bank wajib melakukan penilaian tingkat kesehatan bank secara individual dengan menggunakan metode RGEC. Penelitian ini bertujuan untuk mengetahui tingkat kesehatan dari Bank Mandiri Syariah, Bank Muamalat Indonesia dan Bank BNI Syariah. Metode penelitian yang digunakan adalah kuantitatif. Pengukuran kinerja keuangan dilakukan menggunakan metode RGEC. Pengambilan sampel pada penelitian ini menggunakan menggunakan purposive sampling sehingga didapatkan 3 bank umum syariah yang sesuai dengan kriteria penelitian. Penelitian ini menggunakan metode kuantitatif non statistik deskriptif, dan data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh melalui laporan keuangan masing-masing bank. Berdasarkan hasil pengukuran kinerja keuangan yang ditinjau dari aspek RGEC pada periode 2018-2019 yang meliputi NPF, FDR, ROA, NOM. CAR dan GCG dapat diperoleh kesimpulan bahwa kinerja ketiga bank dinilai baik, namun Bank Muamalat Indonesia mendapat predikat kurang baik dalam rasio ROA pada tahun 2018-2019, sedangkan Bank BNI Syariah dan Bank Mandiri Syariah mendapat predikat yang baik dalam semua rasio. ENGLISH: Through Bank Indonesia in 2011, the Government has determined that banks are required to assess the health of individual banks by using RGEC Method. This study aims to determine the health level of Bank Syariah Mandiri, Bank Muamalat Indonesia and Bank BNI Syariah. The research method used is quantitative. The Assessment of financial performance is carried out using the RGEC method. The sampling in this study using purposive sampling method to obtain three Islamic banks following the criteria. This study used a quantitative non-descriptive statistics method, and the data used in this research is secondary data obtained through the financial statements of each bank. Based on the results of the assessment of financial performance in terms of aspects RGEC in the 2018-2019 period covering NPF, FDR, ROA, NOM. CAR and GCG can be concluded that the performances of the three banks are considered good, but the Bank Muamalat Indonesia received the title less well in ROA in the 2018 and 2019 period, while Bank BNI Syariah and Bank Mandiri Syariah received the title well in all ratios. ARABIC: من خلال بنك اندونيسيا في سنة ٢٠١١، وقد نص على الحكومة أن يطلب من البنوك لتقييم سلامة البنوك بشكل فردي باستخدام أسلوب ملف خطر و الحوكمة الشّركة الجيدة و أرباح و عاصمة. يهدف هذا البحث إلى تحديد مستوى الصحة بنك مانديري الشرعية و بنك معاملات أندونيسيا وبنك نيجارا اندونيسيا الشريعة. منهج البحث المستخدم هو الكمي. يتم قياس الأداء المالي باستخدام أسلوب ملف خطر و الحوكمة الشّركة الجيدة و أرباح و عاصمة. أخذ العينات في هذا البحث باستخدام أخذ العينات هادفة من أجل الحصول على ٣ بنوك التجارية الإسلامية التي تطابق معايير البحث. يستخدم هذا البحث الأساليب الوصفية الكمية غير الإحصائية. والبيانات المستخدمة في هذه الدراسة البيانات الثانوية التي تم الحصول عليها من خلال البيانات المالية لكل بنك. بناءً على نتائج قياس الأداء المالي من حيث أسلوب ملف خطر و الحوكمة الشّركة الجيدة و أرباح و عاصمة من سنة ٢٠١٨-٢٠١٩. والتي تشمل التمويل المتعثر، ونسبة التمويل إلى الدين، والعائد على الأصول، وصافي هامش التشغيل، ونسبة كفاية رأس المال، والحوكمة الجيدة للشركات، يمكن الاستنتاج أن أداء البنوك الثلاثة يعتبر جيدًا، ومع ذلك، تلقى بنك معاملات اندونيسيا المسند سيئة في العائد على الموجودات نسبة من سنة ٢٠١٨-٢٠١٩، في حين حصل بنك نيجارا اندونيسيا الشريعة و بنك مانديري الشريعة المسند جيد في جميع النسب

Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach

Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding makes us develop a hypothesis that most classical machine learning models, such as Random Forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems.Comment: 17 pages, 3 figure

Intrusion detection and prevention of web service attacks for software as a service:Fuzzy association rules vs fuzzy associative patterns

Cloud computing inherits all the systems, networks as well asWeb Services’ security vulnerabilities, in particular for software as a service (SaaS), where business applications or services are provided over the Cloud as Web Service (WS). Hence, WS-based applications must be protected against loss of integrity, confidentiality and availability when they are deployed over to the Cloud environment. Many existing IDP systems address only attacks mostly occurring at PaaS and IaaS. In this paper, we present our fuzzy association rule-based (FAR) and fuzzy associative pattern-based (FAP) intrusion detection and prevention (IDP) systems in defending against WS attacks at the SaaS level. Our experimental results have validated the capabilities of these two IDP systems in terms of detection of known attacks and prediction of newvariant attacks with accuracy close to 100%. For each transaction transacted over the Cloud platform, detection, prevention or prediction is carried out in less than five seconds. For load and volume testing on the SaaS where the system is under stress (at a work load of 5000 concurrent users submitting normal, suspicious and malicious transactions over a time interval of 300 seconds), the FAR IDP system provides close to 95% service availability to normal transactions. Future work involves determining more quality attributes besides service availability, such as latency, throughput and accountability for a more trustworthy SaaS

RANCANG BANGUN APLIKASI E-VOTING BERBASIS WEB PADA PEMILIHAN KETUA DAN WAKIL KETUA OSIS DI SMK IBNU KHOLDUN AL HASYIMI

Proses pemilihan Ketua dan Wakil Ketua Osis di SMK Ibnu Kholdun Al Hasyimi masih bersifat konvensional, proses ini terlalu banyak dana yang harus dikeluarkan dan juga banyak siswa/siswi yang tidak bisa menyumbangkan hak suaranya dikarenakan sakit atau tidak bisa hadir karena alasan sesuatu hal. Seiring perkembangan teknologi yang canggih di zaman yang modern ini peneliti ingin mengimplementasikan pemilihan umum yang berbasis website atau yang disebut dengan e-voting. Penelitian ini bertujuan untuk merancang aplikasi e-voting bebasis web dan menguji kelayakan produk aplikasi e-voting dalam pemilihan ketua dan wakil ketua OSIS. Aplikasi E-voting yang berbasis PHP MYSQL yang dikembangkan menggunakan metode Prototype dapat mempermudah pemilihan, mengurangi pemanipulasian data, mengurangi pemilihan 2 kali dan serta mempercepat perhitungan dan pelaporan. Berdasarkan dari hasil pengujian black box testing yaitu fungsi-fungsi yang ada dalam sistem e-voting setelah di uji coba yaitu menyatakan berhasil sehingga dapat diimplementasikan kedalam pemilihan Ketua dan Wakil Ketua OSIS di SMK Ibnu Kholdun Al Hasyimi dan berdasarkan uji kelayakan yang dilakukann oleh 7 orang pemilih/user, tingkat kelayakan sistem e-voting berbasis web sebesar 84,17 % artinya sangat layak digunakan pada pemilihan Ketua dan Wakil Ketua OSIS di SMK Ibnu Kholdun Al Hasyim

Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method

Overall, 5G networks are expected to become the backbone of many critical IT applications. With 5G, new tech advancements and innovation are expected; 5G currently operates on software-defined networking. This enables 5G to implement network slicing to meet the unique requirements of every application. As a result, 5G is more flexible and scalable than 4G LTE and previous generations. To avoid the growing risks of hacking, 5G cybersecurity needs some significant improvements. Some security concerns involve the network itself, while others focus on the devices connected to 5G. Both aspects present a risk to consumers, governments, and businesses alike. There is currently no real-time vulnerability assessment framework that specifically addresses 5G Edge networks, with regard to their real-time scalability and dynamic nature. This paper studies the vulnerability assessment in the 5G networks and develops an optimized dynamic method that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the hexagonal fuzzy numbers to accurately analyze the vulnerabilities in 5G networks. The proposed method considers both the vulnerability and 5G network dynamic factors such as latency and accessibility to find the potential attack graph paths where the attack might propagate in the network and quantifies the attack cost and security level of the network. We test and validate the proposed method using our 5G testbed and we compare the optimized method to the classical TOPSIS and the known vulnerability scanner tool, Nessus