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6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into micro VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated.

The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing DT approaches, we propose RAN optimization using the Digital Twin (DTRAN) framework that follows a holistic approach from core to edge networks. The proposed DTRAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the DTRAN and describe the details of our specific use case, which is RAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario.

The development of Digital Twins (DTs) is hindered by a lack of specialized, open-source solutions that can meet the demands of dynamic applications. This has caused state-of-the-art DT applications to be validated using offline data. However, this approach falls short of integrating real-time data, which is one of the most important characteristics of DTs. This can limit the validating effectiveness of DT applications in cases such as aeronautical ad-hoc networks (AANETs). Considering this, we develop a Real-Time Digital Twin Platform and implement core network selection in AANETs as a case study. In this, we implement microservice-based architecture and design a robust data pipeline. Additionally, we develop an interactive user interface using open-source tools. Using this, the platform supports real-time decision-making in the presence of data retrieval failures.

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires significant retraining when the UAV is confronted with a new environment, resulting in wasted resources and time. Therefore, it is essential to develop techniques that can reduce the overhead of retraining DRL models, enabling them to adapt to constantly changing environments. This paper presents a novel method to reduce the need for extensive retraining using a double deep Q network (DDQN) model as a pretrained base, which is subsequently adapted to different urban environments through Continuous Transfer Learning (CTL). Our method involves transferring the learned model weights and adapting the learning parameters, including the learning and exploration rates, to suit each new environment specific characteristics. The effectiveness of our approach is validated in three scenarios, each with different levels of similarity. CTL significantly improves learning speed and success rates compared to DDQN models initiated from scratch. For similar environments, Transfer Learning (TL) improved stability, accelerated convergence by 65%, and facilitated 35% faster adaptation in dissimilar settings., Comment: 6 pages

This study explores implementing a digital twin network (DTN) for efficient 6G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust "What-if Analysis" module, utilizing conditional tabular generative adversarial network (CTGAN) for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker step and the impact of twinning intervals on network performance., Comment: 6 pages, 3 figures, 1 table conference

Ransomware has been predominantly a threat to Windows systems. But, Linux systems became interesting for cybercriminals and this trend is expected to continue. This endangers IoT ecosystems, whereas many IoT systems are based on Linux (e.g. cloud infrastructure and gateways). This paper researches how currently employed forensic techniques can be applied to Linux ransomware and evaluates the maturity as well as the impact on the system. While Windows-based ransomware predominantly uses RSA and AES for key management, a variety of approaches was identified for Linux. Cybercriminals appear to be deliberately moving away from RSA and AES to make Live forensic investigations more difficult. Linux ransomware is developed for a predefined goal and does not exploit the full potential of damage. It appears in an early stage and is expected to reach a similar potential to Windows-based malware. The results generated provided an excellent basic understanding to discuss and assess implications on the IoT industry at an early stage of development.

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions., Comment: 6 pages, 8 figures, 2tables, conference paper

Synchronization is fundamental for mirroring real-world entities in real-time and supporting effective operations of Digital Twins (DTs). Such synchronization is enabled by the communication between the physical and virtual realms, and it is mostly assumed to occur in real-time. However, this is not the case, as real-life scenarios witness performance degradation that may lead to synchronization problems. Hence, as such a problem has yet to be thoroughly analyzed in the literature, this work attempts to uncover potential challenges by emulating and analyzing the DT traffic flows in networks of different scales, for different communication protocols, and with various flow configurations. We propose a Twin Alignment Ratio metric to evaluate the synchronization performance to achieve this goal. Consequently, the findings reveal the interplay of network infrastructure, protocol selection, and twinning rate on synchronization and performance.

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.

Despite the crucial importance of addressing Black Hole failures in Internet backbone networks, effective detection strategies in backbone networks are lacking. This is largely because previous research has been centered on Mobile Ad-hoc Networks (MANETs), which operate under entirely different dynamics, protocols, and topologies, making their findings not directly transferable to backbone networks. Furthermore, detecting Black Hole failures in backbone networks is particularly challenging. It requires a comprehensive range of network data due to the wide variety of conditions that need to be considered, making data collection and analysis far from straightforward. Addressing this gap, our study introduces a novel approach for Black Hole detection in backbone networks using specialized Yet Another Next Generation (YANG) data models with Black Hole-sensitive Metric Matrix (BHMM) analysis. This paper details our method of selecting and analyzing four YANG models relevant to Black Hole detection in ISP networks, focusing on routing protocols and ISP-specific configurations. Our BHMM approach derived from these models demonstrates a 10% improvement in detection accuracy and a 13% increase in packet delivery rate, highlighting the efficiency of our approach. Additionally, we evaluate the Machine Learning approach leveraged with BHMM analysis in two different network settings, a commercial ISP network, and a scientific research-only network topology. This evaluation also demonstrates the practical applicability of our method, yielding significantly improved prediction outcomes in both environments.

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data capturing in traditional approaches, resulting in inaccurate modelling and high resource and energy consumption challenges. To fill this gap, we explore spatiotemporal graphs and propose the Reinforcement Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN). By doing so, our study contributes to advancing Green Cities and showcases tangible benefits in accuracy, synchronisation, resource optimization, and energy efficiency. As a result, we note the spatiotemporal graphs are able to offer a consistent accuracy and 55% higher querying performance when implemented using graph databases. In addition, our model demonstrates right-time data capturing with 20% lower overhead and 25% lower energy consumption.

The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS), significantly enhancing road safety and vehicular communication. However, the intricate and dynamic nature of VANETs presents formidable challenges, particularly in vehicle-to-infrastructure (V2I) communications. Roadside Units (RSUs), integral components of VANETs, are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks. These vulnerabilities pose grave risks to road safety, potentially leading to traffic congestion and vehicle malfunctions. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. Our study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs and address this gap. This framework enables real-time monitoring and efficient threat detection while also improving computational efficiency and reducing data transmission delay for increased energy efficiency and hardware durability. Our framework outperforms existing solutions in resource management and attack detection. It reduces RSU load and data transmission delay while achieving an optimal balance between resource consumption and high attack detection effectiveness. This highlights our commitment to secure and sustainable vehicular communication systems for smart cities., Comment: 6 pages, 5 figures, IEEE International Conference on Communications (ICC) 2024

The dramatic increase in the connectivity demand results in an excessive amount of Internet of Things (IoT) sensors. To meet the management needs of these large-scale networks, such as accurate monitoring and learning capabilities, Digital Twin (DT) is the key enabler. However, current attempts regarding DT implementations remain insufficient due to the perpetual connectivity requirements of IoT networks. Furthermore, the sensor data streaming in IoT networks cause higher processing time than traditional methods. In addition to these, the current intelligent mechanisms cannot perform well due to the spatiotemporal changes in the implemented IoT network scenario. To handle these challenges, we propose a DT-native AI-driven service architecture in support of the concept of IoT networks. Within the proposed DT-native architecture, we implement a TCP-based data flow pipeline and a Reinforcement Learning (RL)-based learner model. We apply the proposed architecture to one of the broad concepts of IoT networks, the Internet of Vehicles (IoV). We measure the efficiency of our proposed architecture and note ~30% processing time-saving thanks to the TCP-based data flow pipeline. Moreover, we test the performance of the learner model by applying several learning rate combinations for actor and critic networks and highlight the most successive model.

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are three-fold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently, improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts.

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.

The idea of next-generation ports has become more apparent in the last ten years in response to the challenge posed by the rising demand for efficiency and the ever-increasing volume of goods. In this new era of intelligent infrastructure and facilities, it is evident that cyber-security has recently received the most significant attention from the seaport and maritime authorities, and it is a primary concern on the agenda of most ports. Traditional security solutions can be applied to safeguard IoT and Cyber-Physical Systems (CPS) from harmful entities. Nevertheless, security researchers can only watch, examine, and learn about the behaviors of attackers if these solutions operate more transparently. Herein, honeypots are potential solutions since they offer valuable information about the attackers. It can be virtual or physical. Virtual honeypots must be more realistic to entice attackers, necessitating better high-fidelity. To this end, Digital Twin (DT) technology can be employed to increase the complexity and simulation fidelity of the honeypots. Seaports can be attacked from both their existing devices and external devices at the same time. Existing mechanisms are insufficient to detect external attacks; therefore, the current systems cannot handle attacks at the desired level. DT and honeypot technologies can be used together to tackle them. Consequently, we suggest a DT-assisted honeypot, called TwinPot, for external attacks in smart seaports. Moreover, we propose an intelligent attack detection mechanism to handle different attack types using DT for internal attacks. Finally, we build an extensive smart seaport dataset for internal and external attacks using the MANSIM tool and two existing datasets to test the performance of our system. We show that under simultaneous internal and external attacks on the system, our solution successfully detects internal and external attacks., Comment: Accepted on WS01 IEEE ICC 2023 Workshop on The Evolution of Digital Twin Paradigm in Wireless Communications

As global Internet of Things (IoT) devices connectivity surges, a significant portion gravitates towards the Edge of Things (EoT) network. This shift prompts businesses to deploy infrastructure closer to end-users, enhancing accessibility. However, the growing EoT network expands the attack surface, necessitating robust and proactive security measures. Traditional solutions fall short against dynamic EoT threats, highlighting the need for proactive and intelligent systems. We introduce a digital twin-empowered smart attack detection system for 6G EoT networks. Leveraging digital twin and edge computing, it monitors and simulates physical assets in real time, enhancing security. An online learning module in the proposed system optimizes the network performance. Our system excels in proactive threat detection, ensuring 6G EoT network security. The performance evaluations demonstrate its effectiveness, robustness, and adaptability using real datasets.

With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focuses on this crucial area. Several attempts already exist for air pollution monitoring. However, none of them are improving the performance of IoT data collection at the desired level. Inspired by the genuine Yet Another Next Generation (YANG) data model, we propose a YAng-based DAta model (YA-DA) to improve the performance of IIoT data collection. Moreover, by taking advantage of digital twin (DT) technology, we propose a DT-enabled fine-grained IIoT air quality monitoring system using YA-DA. As a result, DT synchronization becomes fine-grained. In turn, we improve the performance of IIoT data collection resulting in lower round-trip time (RTT), higher DT synchronization, and lower DT latency., Comment: This paper has been accepted at the 4th Workshop on Future of Wireless Access and Sensing for Industrial IoT (FUTUREIIOT) in IEEE Global Communications Conference (IEEE GLOBECOM) 2022

The Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) have become essential for our daily lives in contexts such as our homes, buildings, cities, health, transportation, manufacturing, infrastructure, and agriculture. However, they have become popular targets of attacks, due to their inherent limitations which create vulnerabilities. Honeypots and honeynets can prove essential to understand and defend against attacks on IoT, IIoT, and CPS environments by attracting attackers and deceiving them into thinking that they have gained access to the real systems. Honeypots and honeynets can complement other security solutions (i.e., firewalls, Intrusion Detection Systems - IDS) to form a strong defense against malicious entities. This paper provides a comprehensive survey of the research that has been carried out on honeypots and honeynets for IoT, IIoT, and CPS. It provides a taxonomy and extensive analysis of the existing honeypots and honeynets, states key design factors for the state-of-the-art honeypot/honeynet research and outlines open issues for future honeypots and honeynets for IoT, IIoT, and CPS environments.

Content Delivery Network (CDN) has been emerged as a compelling technology to provide efficient and scalable web services even under high client request. However, this leads to a dilemma between minimum deployment cost and robust service under heavy loads. To solve this problem, we propose the Workload Automation Engine (WAE) which enables dynamic resource management, automated scaling and rapid service deployment with least cost for CDN providers. Our modular design uses an algorithm to calculate the optimal assignment of virtual CDN functions such as streaming, progressive delivering and load balancer. In particular, we study on real CDN data which belongs to Medianova CDN Company in Turkey. Also we use Docker containerization as an underlying system. The results reveal that our containerized design reduces the latency and deployment cost by 45% and by 66%, respectively. Moreover, we obtain roughly 20% more CPU efficiency and 35% more utilized network., Comment: 5 pages, 6 figures, 3 tables Conference Publication Keywords: Content delivery networks, containerization, network functions virtualization This paper is published in 2018 IEEE Second International Balkan Conference on Communications and Networking, Podgorica, Montenegro, June 6-8,2018