Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have / Ministry of Higher Education in Egypt and the Egyptian Cultural Centre and Educational Bureau in London

In this Internet and big data era, resource management has become a crucial task to ensure the quality of service for users in modern wireless networks. Accurate and rapid Internet traffic data is essential for many applications in computer networking to enable high networking performance. Such applications facilitate admission control, congestion control, anomaly detection, and bandwidth allocation. In radio networks, these mechanisms are typically handled by features such as Carrier Aggregation, Inter-Frequency Handover, and Predictive Scheduling. Since these mechanisms often take time and cost radio resources, it is desirable to only enable them for users expected to gain from them. The problem of network traffic flow prediction is forecasting aspects of an ongoing traffic flow to mobilize networking mechanisms that ensures both user experience quality and resource management. The expected size of an active traffic flow, its expected duration, and the anticipated amount of packets within the flow are some of the aspects. Additionally, forecasting individual packet sizes and arrival times can also be beneficial. The wide-spread availability of Internet flow data allows machine learning algorithms to learn the complex relationships in network traffic and form models capable of forecasting traffic flows. This study proposes a deep-learning-based flow prediction method, established using a residual neural network (ResNet) for regression. The proposed model architecture demonstrates the ability to accurately predict the packet count, size, and duration of flows using only the information available at the arrival of the first packet. Additionally, the proposed method manages to outperform traditional machine learning methods such as linear regression and decision trees, in addition to conventional deep neural networks. The results indicate that the proposed method is able to predict the general magnitude of flows with high accuracy, providing precise magnitude classifications. / I denna Internet och data era har resurshantering blivit allt mer avgörande för att säkerställa tjänstekvaliteten för användare i moderna trådlösa nätverk. Noggrann och hastig Internet-trafikinformation är avgörande för många applikationer inom datanätverk för att möjliggöra hög nätverksprestanda. Sådana applikationer underlättar kontroll av behörighet, kontroller av trängsel, detektering av avvikelser och allokering av bandbredd. I radionätverk hanteras dessa mekanismer vanligtvis av funktioner som Carrier Aggregation, Inter- Frequency Handover och Predictive Scheduling. Eftersom dessa funktioner ofta tar tid och kostar resurser så är det önskvärt att nätverk endast möjliggör sådana funktioner för användare som förväntas dra nytta av dem. Prediktering av trafikflöden i nätverk grundar sig i att förutsäga aspekter av ett pågående trafikflöde för att kunna mobilisera nätverksfunktioner som säkerställer både kvaliteten för användare samt resurshantering. Den förväntade storleken på ett aktivt trafikflöde, dess varaktighet och mängden paket inom flödet är några av dessa aspekter. Det kan dessutom vara fördelaktigt att förutsäga individuella paketstorlekar och ankomsttider. Den stora tillgången till data med nätverks-flöden gör det möjligt för maskininlärningsmetoder att lära sig de komplexa förhållandena i nätverkstrafik och därigenom formulera modeller som kan förutsäga flöden i nätverk. Denna studie föreslår en djupinlärningsbaserad metod för att prediktera flöden i nätverk, med hjälp av ett anpassat neuralt nätverk som utnyttjar genvägar i modellens konstruktion (ResNet). Den föreslagna modell-arkitekturen visar sig nöjaktigt kunna förutsäga antalet paket, storlek och varaktighet för flöden med endast den information som är tillgänglig från det första paketet. Dessutom lyckas den föreslagna metoden att överträffa både traditionella maskininlärningsmetoder som linjär regression och beslutsträd, samt konventionella djupa neurala nätverk. Resultaten indikerar att den föreslagna metoden kan förutsäga den allmänna storleken på flödens egenskaper med hög noggrannhet, givet att IP-adresser är tillgängliga.

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Everyone is in need for their own privacy and data protection, since encryption transmission was becoming common. Fully Homomorphic Encryption (FHE) has received increased attention because of its capability to execute calculations over the encoded domain. Through using FHE approach, model training can be properly outsourced. The goal of FHE is to enable computations on encrypted files without decoding aside from the end outcome. The CKKS scheme is used in FHE.Network threats are serious danger to credential information, which enable an unauthorised user to extract important and sensitive data by evaluating the information of computations done on raw data. Thus the study provided an efficient solution to the problem of privacy protection in data-driven applications using Machine Learning. The study used an encrypted NSL KDD dataset. Machine learning-based techniques have emerged as a significant trend for detecting malicious attack. Thus, Random Forest (RF) is proposed for the detection of malicious attacks on Homomorphic encrypted data in the cloud server. Logistic Regression (LR) machine learning model is used to predict encrypted data on cloud server. Regardless of the distributed setting, the technique may retain the accuracy and integrity of the previous methods to obtain the final results.

no / Multimedia applications in wireless networks are usually categorized into various classes according to their traffic patterns and differentiated Quality-of-Service (QoS) requirements. The traffic of heterogeneous multimedia applications often exhibits the Long-Range Dependent (LRD) and Short-Range Dependent (SRD) properties, respectively. The integrated scheduling scheme that combines Priority Queuing (PQ) and Generalized Processor Sharing (GPS) within a hierarchical structure, referred to as PQ-GPS, has been identified as an efficient mechanism for QoS differentiation in wireless networks and attracted significant research efforts. However, due to the high complexity and interdependent relationship among traffic flows, modelling of the integrated scheduling scheme poses great challenges. To address this challenging and important research problem, we develop an original analytical model for PQ-GPS systems under heterogeneous LRD and SRD traffic. A cost-effective flow decomposition approach is proposed to equivalently divide the integrated scheduling system into a group of Single-Server Single-Queue (SSSQ) systems. The expressions for calculating the queue length distribution and loss probability of individual traffic flows are further derived. After validating its accuracy, the developed model is adopted as an efficient performance tool to investigate the important issues of resource allocation and call admission control in the integrated scheduling system under QoS constraints.

This thesis addresses the critical issue of security vulnerabilities within the Internet of Things (IoT) ecosystem, with a particular emphasis on everyday devices such as refrigerators, vacuum cleaners, and cameras. The widespread adoption of IoT devices across various sectors has raised significant concerns regarding their security, underscoring the need for more effective penetration testing methods to mitigate potential cyberattacks. In response to this need, the first part of this thesis presents an approach to creating a penetration testing environment specifically tailored for IoT devices. Unlike existing studies that primarily focus on isolated or specific device testing, this work integrates various common household IoT appliances into a single testbed, enabling the testing of a complex system. This setup not only reflects a more realistic usage scenario but also allows for a comprehensive analysis of network traffic and interactions between different devices, thereby potentially identifying new, complex security vulnerabilities. The second part of the thesis undertakes a comparative study of cyber range infrastructures and architectures, an area relatively unexplored in existing literature. This study aims to provide nuanced insights and practical recommendations for developing robust, scalable cyber range infrastructures at a national level. By examining different frameworks, this research contributes to the foundational knowledge necessary for advancing national cybersecurity defenses. Overall, the findings from this research aim to contribute to improving IoT security and guiding the development of robust national cyber range frameworks. / Denna avhandling tar upp de säkerhetsbrister som finns inom det ekosystem som omfattar Internet of Things (IoT) enheter, med särskilt fokus på vardagliga apparater som kylskåp, dammsugare och kameror. Den stora spridningen av IoT-enheter inom olika sektorer har väckt många säkerhetsfrågor, vilka betonar behovet av effektivare metoder för penetrationstestning för att förhindra möjliga cyberattacker. För att möta detta behov presenterar den första delen av avhandlingen en metod för att skapa en penetrationstestningsmiljö särskilt anpassad för IoT-enheter. Till skillnad från tidigare studier, vilka främst fokuserar på enskilda eller specifika enhetstestningar, kombinerar detta arbete olika hushållsapparater i en enda testbädd, vilket möjliggör testningen av ett komplext system. Detta upplägg speglar inte bara en mer realistisk användningssituation, utan tillåter också en mer omfattande analys av nätverkstrafik och interaktioner mellan olika enheter, vilket potentiellt kan identifiera nya, komplexa säkerhetsbrister. Den andra delen av avhandlingen genomför en jämförande studie av cyberanläggningars infrastrukturer och arkitekturer, ett område som är relativt outforskat i befintlig litteratur. Denna studie syftar till att ge insikter och praktiska rekommendationer för att utveckla robusta, skalbara infrastrukturer för cyberanläggningar på nationell nivå. Genom att undersöka olika ramverk bidrar denna forskning till den grundläggande kunskap som behövs för att förbättra nationella cybersäkerhetsförsvar. Sammanfattningsvis syftar resultaten från denna forskning till att förbättra IoT-säkerheten och vägleda utvecklingen av robusta nationella ramverk för cyberanläggningar.

In this time of increasing cyber risks, robust intrusion detection systems (IDS) arefundamentally necessary for protecting network systems. This master thesis compares twoprimary network intrusion detection resources to clarify their effectiveness, advantages, andboundaries. The investigation follows a thorough approach, including reviewing existingliterature, practical experimentation, and assessing their performance. The primary goal revolves around a deeper comprehension of the operational procedures, threatdetection capacity, and scalability of the chosen IDS solutions. Through carefulexperimentation and scrutiny, this study investigates various elements such as detection accuracy, false favorable rates, the usage of resources, and resilience in varied networksituations. Real-life data sets and contrived attack situations are harnessed to measure the proficiency of these tools in identifying both identified and fresh intrusion efforts. Finally, our experimentation did not identify a single optimal tool due to certain imperfections in both evaluated tools. However, these findings were instrumental in concluding the properties that would constitute an ideal tool. In the end, this study propels the forward arena of networksecurity, offering a detailed insight into the capabilities and limitations of day-to-day intrusion detection tools. This study aims to strengthen cybersecurity defenses and nurture improved decision-making capabilities. These efforts mitigate the constantly changing threats caused byharmful entities in our digital world.

Along with playing an ever-increasing role in the integration of other communication networks and expanding in application diversities, the current Internet suffers from serious overuse and congestion bottlenecks. Efficient congestion control is fundamental to ensure the Internet reliability, satisfy the specified Quality-of-Service (QoS) constraints and achieve desirable performance in response to varying application scenarios. Active Queue Management (AQM) is a promising scheme to support end-to-end Transmission Control Protocol (TCP) congestion control because it enables the sender to react appropriately to the real network situation. Analytical performance models are powerful tools which can be adopted to investigate optimal setting of AQM parameters. Among the existing research efforts in this field, however, there is a current lack of analytical models that can be viewed as a cost-effective performance evaluation tool for AQM in the presence of heterogeneous traffic, generated by various network applications. This thesis aims to provide a generic and extensible analytical framework for analyzing AQM congestion control for various traffic types, such as non-bursty Poisson and bursty Markov-Modulated Poisson Process (MMPP) traffic. Specifically, the Markov analytical models are developed for AQM congestion control scheme coupled with queue thresholds and then are adopted to derive expressions for important QoS metrics. The main contributions of this thesis are listed as follows: • Study the queueing systems for modeling AQM scheme subject to single-class and multiple-classes Poisson traffic, respectively. Analyze the effects of the varying threshold, mean traffic arrival rate, service rate and buffer capacity on the key performance metrics. • Propose an analytical model for AQM scheme with single class bursty traffic and investigate how burstiness and correlations affect the performance metrics. The analytical results reveal that high burstiness and correlation can result in significant degradation of AQM performance, such as increased queueing delay and packet loss probability, and reduced throughput and utlization. • Develop an analytical model for a single server queueing system with AQM in the presence of heterogeneous traffic and evaluate the aggregate and marginal performance subject to different threshold values, burstiness degree and correlation. • Conduct stochastic analysis of a single-server system with single-queue and multiple-queues, respectively, for AQM scheme in the presence of multiple priority traffic classes scheduled by the Priority Resume (PR) policy. • Carry out the performance comparison of AQM with PR and First-In First-Out (FIFO) scheme and compare the performance of AQM with single PR priority queue and multiple priority queues, respectively.

Made available in DSpace on 2014-07-29T15:08:17Z (GMT). No. of bitstreams: 1 Dissertacao_mestrado_josemar.pdf: 1807801 bytes, checksum: b47a17ed3732deb7f34a3e310ec05477 (MD5) Previous issue date: 2011-09-29 / The multifractal modeling is more appropriate in describing some features finding in traffic flows in real networks than other models. This work investigates the behavior of the traffic modeling based policing algorithms found in the literature (Leaky Bucket, Fractal Leaky Bucket, Gaussian Multifractal Leaky Bucket) regarding the buffer utilization, the efficiency for the use of buffer efficiency in describing the accumulated traffic (envelope process), packet dropping and data loss (bytes). First, we compare the envelope process of the considered policing algorithms and the proposed algorithm MAPM (Multifractal Arrival Policing Mechanism), with traffic without policing. Next, it was calculated the system loss rate for a finity buffer system with and without policing algoritm. Using the deterministic Network Calculus, it is also estimated the average queue length (backlog) and delay of bytes by applying the concept of the Min-Plus algebra that presents some differences against the conventional algebra. The proposed policing algorithm was applied to a transmission system based on OFDM (Orthogonal Frequency - Division Multiplexing) / TDMA (Time Division Multiplexing Access) system, where it was evaluated the network performance of the considered policing algorithms. Simulations were carried out with real wireless network trace (Wi-Fi) and wired network trace in order to demonstrate the efficiency of the algorithm proposed in relation to policing algorithms in the literature. The simulations shown the efficiency of the MAPM policing algorithm with traces from wired and wireless networks. We also propose the use of deterministic Network Calculus associated to multifractal envelope process to analyze the traffic behavior in terms of delay and backlog before policing algorithms application in the network. Finally we present the results of buffer utilization, link utilization, delay and backlog to a WiMAX system where the policing algorithm MAPM in general shown delay and backlog increasing in the traffic compared with anothers algorithms and low link utilization / A modelagem multifractal tem se mostrado mais apropriada para descrever algumas características encontradas nos fluxos de tráfego de redes reais que outros modelos, tais como o modelo de Poisson e Markov. Este trabalho investiga o comportamento dos algoritmos de policiamento baseados em modelagem de tráfego (Balde Furado, Balde Furado Fractal, Balde Furado Multifractal Gaussiano) em relação à utilização do buffer, eficiência em descrever o tráfego acumulado (processo envelope) e descarte de pacotes e perda de dados (bytes). Primeiramente, compara-se o processo envelope dos algoritmos de policiamento considerados e do algoritmo proposto neste trabalho, o MAPM (Multifractal Arrival Policing Mechanism), com o tráfego sem policiamento. Em seguida, a taxa de perda em um enlace simples com buffer finito com e sem algoritmo de policiamento é calculada. Utilizando o Cálculo de Rede Determinístico, estima-se o tamanho da fila (backlog) e retardo (delay) de bytes, conforme o conceito da álgebra Min-Plus. O algoritmo de policiamento proposto foi aplicado em um sistema de transmissão OFDM (Orthogonal Frequency - Division Multiplexing) / TDMA (Time Division Multiplexing Access) baseado no sistema WiMAX simplificado, onde se avaliou o desempenho da rede com os algoritmos de policiamento considerados. Realizam-se simulações com séries reais de tráfego de redes sem fio (Wi-Fi) e de redes com fio a fim de demonstrar a eficiência do algoritmo proposto em relação aos algoritmos de policiamento encontrados na literatura. As simulações realizadas evidenciam a eficiência do algoritmo MAPM em policiar séries reais de tráfego de redes com fio e redes sem fio. Propôs-se também, a utilização do Cálculo de Rede Determinístico associado ao processo envelope multifractal para se analisar o comportamento do tráfego em termos de retardo e backlog após a aplicação dos algoritmos de policiamento de rede. Por fim, são apresentados os resultados de retardo, backlog, utilização média do buffer e utilização do enlace para um sistema WiMAX onde o algoritmo de policiamento MAPM que apresentou em geral, acréscimo no retardo e backlog do tráfego em relação aos outros algoritmos e baixa utilização do enlace.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-12-16T13:58:28Z No. of bitstreams: 2 Tese - Flávio Geraldo Coelho Rocha - 2016.pdf: 4809831 bytes, checksum: e575d503488bc0e0cb8f1a1b3478d982 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2016-12-16T16:59:35Z (GMT) No. of bitstreams: 2 Tese - Flávio Geraldo Coelho Rocha - 2016.pdf: 4809831 bytes, checksum: e575d503488bc0e0cb8f1a1b3478d982 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-12-16T16:59:35Z (GMT). No. of bitstreams: 2 Tese - Flávio Geraldo Coelho Rocha - 2016.pdf: 4809831 bytes, checksum: e575d503488bc0e0cb8f1a1b3478d982 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-11-22 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / In this work, in order to describe network traffic characteristics, such as long-range dependence among samples, self-similarity and multiscale behavior, we propose a Multifractal Adaptive Model based on a multiscale cascade in the Wavelet Domain. We compare the proposed model performance with those of other models presented in the literature. It is also proposed an envelope process for the network traffic that takes into account parameters of the Multifractal Adaptive Model. Furthermore, we derive an equation in order to estimate the buffer overflow probability for both a simplified communication system with a single server, single queue and finite buffer, and to a wireless network multiuser scenario based on OFDM technology. To this end, we consider the service curve of the round-robin scheduling algorithm of the OFDM network. Taking into account the envelope process and the service curve we obtain, through the Network Calculus theory, the maximum delay experienced by users of the OFDM network. Moreover, assuming a similar network scenario to an LTE network, we propose a joint channel-aware and queue-aware resource scheduling algorithm. Based on the presented scheduler, we propose a minimum service curve for the LTE user and through this we propose an approach to accomplish maximum delay guarantee. / Neste trabalho, com o objetivo de descrever características do tráfego de redes, tais como longa-dependência entre amostras, autossimilaridade e comportamento multiescala, propõe-se um Modelo Multifractal Adaptativo baseado em uma cascata multiescala no domínio Wavelet. O desempenho do modelo proposto é comparado a outros modelos presentes na literatura. Também é proposto um processo envelope para o tráfego de redes que leva em consideração parâmetros do Modelo Multifractal Adaptativo proposto. Além disso, deduz-se uma equação para o cálculo da probabilidade de transbordo do buffer, tanto para um sistema de comunicação simplificado com servidor único, fila única e buffer finito, quanto para um ambiente multiusuário de rede sem fio baseado na tecnologia OFDM. Para tanto, utiliza-se a curva de serviço do escalonador round-robin da rede OFDM. Utilizando-se do processo envelope e da curva de serviço, obtém-se por meio do Cálculo de Rede a estimativa para o retardo máximo experimentado pelos usuários da rede OFDM. Em seguida, assume-se um ambiente de rede similar ao de uma rede LTE e propõe-se para essa rede um escalonador de recursos sensível às condições do canal de comunicação e à probabilidade de transbordo do buffer. Com base no escalonador apresentado, propõe-se uma curva de serviço mínima para o usuário da rede LTE e por meio dessa, propõe-se uma abordagem para garantia de retardo.