5G will provide broadband access everywhere, entertain higher user mobility, and enable connectivity of massive number of devices (e.g. Internet of Things (IoT)) in an ultrareliable and affordable way. The main technological enablers such as cloud computing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) are maturing towards their use in 5G. However, there are pressing security challenges in these technologies besides the growing concerns for user privacy. In this paper, we provide an overview of the security challenges in these technologies and the issues of privacy in 5G. Furthermore, we present security solutions to these challenges and future directions for secure 5G systems.

Host Identity Protocol (HIP), a novel internetworking technology proposes separation of the identity-location roles of the Internet Protocol (IP). HIP has been successful from the technological perspectives for network security and mobility, however, it has very limited deployment. In this paper we assess HIP to find the reasons behind its limited deployment and highlight the challenges faced by HIP for its commercial use. We propose technological development and outline deployment strategies for the wide use of HIP. Furthermore, this paper investigates the use of HIP in Software Defined Networks (SDN) to evaluate its performance in new disruptive networking technologies. In a nutshell, this paper presents revealing challenges for the deployment of innovative networking protocols and a way ahead for successful and large scale deployment.

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

The integration of satellite and terrestrial networks has become inevitable in the next generations of communications networks due to emerging needs of ubiquitous connectivity of remote locations. New and existing services and critical infrastructures in remote locations in sea, on land and in space will be seamlessly connected through a diverse set of terrestrial and non-terrestrial communication technologies. However, the integration of terrestrial and non-terrestrial systems will open up both systems to unique security challenges that can arise due to the migration of security challenges from one to another. Similarly, security challenges can also arise due to the incompatibility of distinct systems or incoherence of security policies. The resulting security implications, thus, can be highly consequential due to the criticality of the infrastructures such as space stations, autonomous ships, and airplanes, for instance. Therefore, in this article we study existing security challenges in satellite-terrestrial communication systems and discuss potential solutions for those challenges. Furthermore, we provide important research directions to encourage future research on existing security gaps.

The development of the Fifth Generation (5G) wireless networks is gaining momentum to connect almost all aspects of life through the network with much higher speed, very low latency and ubiquitous connectivity. Due to its crucial role in our lives, the network must secure its users, components, and services. The security threat landscape of 5G has grown enormously due to the unprecedented increase in types of services and in the number of devices. Therefore, security solutions if not developed yet must be envisioned already to cope with diverse threats on various services, novel technologies, and increased user information accessible by the network. This article outlines the 5G network threat landscape, the security vulnerabilities in the new technological concepts that will be adopted by 5G, and provides either solutions to those threats or future directions to cope with those security challenges. We also provide a brief outline of the post-5G cellular technologies and their security vulnerabilities which is referred to as Future Generations (XG) in this paper. In brief, this article highlights the present and future security challenges in wireless networks, mainly in 5G, and future directions to secure wireless networks beyond 5G.

In this paper, we analyze the performance of a wireless caching system with heterogeneous traffic and relaying capabilities satisfying secrecy constraints for one of two receiving users. In this setup, the second user has no secrecy requirements and receives cacheable content either from the relay helper or the core network through a wireless base station. The wireless relay helper can assist both users since it is equipped with finite storage that is split into cacheable and non-cacheable storage. Concurrently, a passive eavesdropper tries to overhear transmissions to the user with secrecy requirements. Consequently, we examine how this relay’s storage split and the eavesdropper affect the performance of the average throughput and delay of the system as the transmission powers, the relay’s transmission probability, and the relay’s cache size vary. © 2023 IEEE.

Digitalisation offers many benefits to Air TrafficManagement (ATM). Yet, with technological innovations comechallenges in managing new cyber security threats and risks.This paper presents a comprehensive review over challenges facedin ATM when protecting critical assets, and outlines how thenewly established exploratory research project SEC-AIRSPACEwill address these challenges.

Hackers hide behind compromised intermediate hosts and pose advanced persistent threats (APTs). The compromised hosts are used as stepping stones to launch real attacks, as is evident from an incident that shook the world in 2016 - Panama Papers Leak. The major attack would not go unnoticed if the compromised stepping stone, in this case an email server, could be identified in time. In this paper, we explore how todays programmable networks could be retrofitted with effective stepping stone detection mechanisms to correlate flows. We share initial results to prove that such a setup exists. Lastly, we analyze scalability issues associated with the setup and explore recent developments in network monitoring which have potential to address these issues.

In Air Traffic Management (ATM) training, simulations of real air traffic control (ATC) scenarios are a key part of practical teaching. On the internet one may find multiple different ATM simulators available to the public with open source code. Today most aircraft transmit data about position, altitude, and speed into the atmosphere that practically are unencrypted data points. This data is called automatic dependant surveillance broadcast (ADS-B) data. The lack of security means that potential attackers could project "fake" ADS-B data and spoof existing data to air traffic controllers (ATCO) if the right equipment is used. We see this as a security flaw and we want to prepare ATCO for cyberattacks by modifying an ATM simulator with cyberattacks. First, OpenScope was chosen as the ATM simulator to be modified. Subsequently, three types of attacks were chosen for the simulator to be equipped with, based on ADS-B weaknesses from existing literature: aircraft not responding to commands, aircraft with altering positional data, and aircraft with incorrect speed and altitude data. The recorded parameters were the written command lines and corresponding aircraft type it was applied to. Using this modified simulator, ATCO can now be evaluated against cyberattacks.

Virtual Private LAN Service (VPLS) is a VPN technology that connects remote client sites with provider networks in a transparent manner. Session key-based HIPLS (S-HIPLS) is a VPLS architecture based on the Host Identity Protocol (HIP) that provides a secure VPLS architecture using a Key Distribution Center (KDC) to implement security mechanisms such as authentication, encryption etc. It exhibits limited scalability though. Using multiple distributed KDCs would offer numerous advantages including reduced workload per KDC, distributed key storage, and improved scalability, while simultaneously eliminating the single point of failure of S-HIPLS. It would also come with the need for optimally placing KDCs in the provider network. In this work, we formulate the KDC placement (KDCP) problem for a secure VPLS network as an Integer Linear Programming (ILP) problem. The latter is NP-hard, thereby suggesting a high computational cost for obtaining exact solutions especially for large deployments. Therefore, we motivate the use of a primal-dual algorithm to efficiently produce near-optimal solutions. Extensive evaluations on large-scale network topologies, such as the random Internet graph, demonstrate our method’s time-efficiency as well as its improved scalability and usefulness compared to both HIPLS and S-HIPLS.

The Industrial Internet brings the promise of increased efficiency through on-demand manufacturing and maintenance, combining sensors data from engines and industrial devices with big data analysis in the cloud. In this chapter, we survey the main challenges that the Industrial Internet faces from a networking viewpoint. We especially focus on security, as critical industrial components could be exposed over the Internet, affecting resilience. We describe two approaches, Identity-Defined Networking and Software-Defined Virtual Private LAN Services as potential network architectures for the Industrial Internet.

Industrial device scanners allow anyone to scan devices on private networks and the Internet. They were intended as network security tools, but they are commonly exploited as attack tools, as scanning can reveal vulnerable devices. However, from a defensive perspective, this vulnerability disclosure could be used to secure devices if characteristics such as type, model, manufacturer, and firmware could be identified. Automated scanning reports can help to apply security measures before an attacker finds a vulnerability. A complete device recognition procedure can then be seen as the basis for auditing networks and identifying vulnerabilities to mitigate cyber-attacks, especially among Industrial Internet of Things (IIoT) devices that are part of critical systems. In this survey, considering SCADA (Supervisory Control and Data Acquisition) systems as monitoring and control components of essential infrastructure, we focus on analyzing the architectures, specifications, and constraints of several industrial device scanners. In addition, we examine the information revealed by the scanners to identify the threats posed by them on industrial systems and networks. We analyze monthly and yearly statistics of cyber-attack incidents to investigate the role of these scanners in accelerating attacks. By presenting the findings of an experimentation, we highlight how easily anyone could identify hundreds of Internet-connected industrial devices in Sweden, which could lead to a major service interruption in industrial environments designed for minimal human involvement. We also discuss several methods to avoid scanners or reduce their identifying capabilities to conceal industrial devices from unauthorized access.

The paper presents the results of the design and implementation of detection system against DDoS attacks for OpenStack cloud computing platform. Proposed system uses data mining techniques to detect malicious traffic. Formal models of detecting components are described. To train data mining models real legitimate traffic was combined with modelled malicious one. Paper presents results of detecting the origin of DDoS attacks on cloud instances.

This document describes an architecture for protocols and services to support Unmanned Aircraft System Remote Identification and tracking (UAS RID), plus UAS-RID-related communications.  The architecture takes into account both current (including proposed) regulations and non-IETF technical standards. The architecture adheres to the requirements listed in the DRIP  Requirements document [RFC9153] and illustrates how all of them can be met, except for GEN-7 QoS, which is left for future work. The requirements document provides an extended introduction to the   problem space and use cases. Further, this architecture document frames the DRIP Entity Tag (DET) [RFC9374] within the architecture.

Maritime security is an ever-growing problem, and SATCOM systems available on the open Internet provide an attack vector for bad actors. This research paper presents an analysis of the availability of SATCOM terminals on the open Internet using the Shodan search engine. The study focuses on two popular SATCOM terminal models/interfaces, Sailor and Intellian. The terminals are available in old and new models/interfaces. The general results are that the older models have shown a decrease in availability while the newer ones showed an increase or at least maintained the number of available systems. The study concludes that openly available maritime SATCOM systems still are a prevalent problem and that the maritime industry has not made significant progress in addressing this issue.

Cybersecurity attacks that target software have become profitable and popular targets for cybercriminals who consciously take advantage of web-based vulnerabilities and execute attacks that might jeopardize essential industry 5.0 features. Several machine learning-based techniques have been developed in the literature to identify these types of assaults. In contrast to single classifiers, ensemble methods have not been evaluated empirically. To the best of our knowledge, this work is the first empirical evaluation of both homogeneous and heterogeneous ensemble approaches compared to single classifiers for web -based attack detection in industry 5.0, utilizing two of the most realistic public web-based attack data -sets. The authors divided the experiment into three main phases: In the first phase, they evaluated the performance of five well-established supervised machine learning (ML) classifiers. In the second phase, they constructed a heterogeneous ensemble of the three best-performing ML algorithms using max vot-ing and stacking methods. In the third phase, they used four well-known homogeneous ensembles to evaluate the performance of the bagging and boosting method. The results based on the ECML/PKDD 2007 and CSIC HTTP 2010 datasets revealed that bagging, particularly Random Forest, outperformed sin-gle classifiers in terms of accuracy, precision, F-value, FPR, and area of the ROC curve with values of 99.597%, 98.274%, 99.129%, 0.523%, 100 and 99.867%, 99.867%, 99.867%, 0.267%, 100, respectively. In con-trast, single classifiers performed better than boosting and stacking. However, in terms of FPR, the boost-ing exceeded single classifiers. Max voting is appropriate when accuracy, precision, and FPR are the primary concerns, whereas single classifiers can be employed when recall, FNR, training, and prediction times are critical elements. In terms of training time, ensemble approaches are more likely to be affected by data volume than single classifiers. The papers findings will help security researchers and practition-ers identify the most efficient learning techniques for securing web applications. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Fast reroute and other forms of immediate failover have long been used to recover from certain classes of failures without invoking the network control plane. While the set of such techniques is growing, the level of resiliency to failures that this approach can provide is not adequately understood. In this paper, we embarked upon a systematic algorithmic study of the resiliency of forwarding tables in a variety of models (i.e., deterministic/probabilistic routing, with packet-headerrewriting, with packet-duplication). Our results show that the resiliency of a routing scheme depends on the "connectivity" k of a network, i.e., the minimum number of link deletions that partition a network. We complement our theoretical result with extensive simulations. We show that resiliency to four simultaneous link failures, with limited path stretch, can be achieved without any packet modification/duplication or randomization. Furthermore, our routing schemes provide resiliency against k - 1 failures, with limited path stretch, by storing log(k) bits in the packet header, with limited packet duplication, or with randomized forwarding technique.

Several studies have shown insufficient security in air traffic communication. Controller-Pilot Datalink Communications (CPDLC) is used to communicate in text over the VHF data link, and Automatic Dependent Surveillance Broadcast (ADS-B) determines the position of an aircraft. The vulnerability of air data communication was confirmed by successful experiments using Software-Defined Radio, where both CPDLC and ADS-B messages were transmitted in a safe environment. Neither ADS-B messages nor CPDLC messages are encrypted during transmission. The encoding of FANS-1/A messages was demonstrated, and the experiments showed that it is possible to send such messages with relatively inexpensive technology.

Network communications and the Internet pervade our daily activities so deeply that we strongly depend on the availability and quality of the services they provide. For this reason, natural and technological disasters, by affecting network and service availability, have a potentially huge impact on our daily lives. Ensuring adequate levels of resiliency is hence a key issue that future network paradigms, such as 5G, need to address. This paper provides an overview of the main avenues of research on this topic within the context of the RECODIS COST Action.

As mobile and multi-homed devices are becoming ubiquitous, the need for a dynamic, yet secure communication protocol is unavoidable. The Host Identity Protocol (HIP) was constructed to meet this requirement; to provide significantly more secure mobility and multi-homing capabilities. HIP opportunistic mode, which is to be used when other, more trusted mechanisms are lacking, is based on a leap of faith (LoF) paradigm. In this paper, we analyze different Man in the middle (MiTM) attacks which might occur under this LoF, and propose a set of tweaks for hardening opportunistic HIP (HOH) that strengthen opportunistic modes security.

Key sharing has always been a complex issue. It became even more challenging for the Internet of Things (IoT), where a trusted third party for global management rarely exists. With authentication and confidentiality lacking, things resort to a leap of faith (LoF) paradigm where it is assumed that no attacker is present during the initial configuration. In this paper we focus on the Host Identity Protocol (HIP), specifically designed to provide mobility and multihoming capabilities. Although HIP is normally based on many strict security mechanisms (e.g., DNSSEC), it also provides a better than nothing opportunistic mode, based on the LoF paradigm, which is to be used when other more trusted mechanisms are not available. In this paper, we analyze different MiTM attacks which might occur under this opportunistic mode. Taking advantage of HIPs multihoming capabilities, we propose two key spraying techniques which strengthen the opportunistic modes security. The first technique spreads the four key-exchange messages among different networks, while the second spreads fractions of one of those messages. Evaluation of these techniques is provided, demonstrating the major benefit of our proposal.

Virtual Private LAN services (VPLS) is a Layer 2 Virtual Private Network (L2VPN) service that has gained immense popularity due to a number of its features, such as protocol independence, multipoint-to-multipoint mesh connectivity, robust security, low operational cost (in terms of optimal resource utilization), and high scalability. In addition to the traditional VPLS architectures, novel VPLS solutions have been designed leveraging new emerging paradigms, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to keep up with the increasing demand. These emerging solutions help in enhancing scalability, strengthening security, and optimizing resource utilization. This paper aims to conduct an in-depth survey of various VPLS architectures and highlight different characteristics through insightful comparisons. Moreover, the article discusses numerous technical aspects such as security, scalability, compatibility, tunnel management, operational issues, and complexity, along with the lessons learned. Finally, the paper outlines future research directions related to VPLS. To the best of our knowledge, this paper is the first to furnish a detailed survey of VPLS.

Peer-to-peer (P2P) sharing systems use incentives for resource exchange to encourage cooperation and ensure fairness. In bilateral strategies, such as BitTorrent Tit-for-Tat or deficit-based FairTorrent, individual decisions of peers utilize direct observations. It may result in low performance and unfair treatment. In this paper, we study a novel exchange strategy that applies Cyclic Ranking (CR). In addition to direct observations, a peer utilizes provision cycles-a shared history of effective exchanges. The PageRank algorithm runs for the locally collected cycles and computes the numerical ranks to estimate the reputation. The CR strategy incrementally augments known incentive-aware strategies. For evaluation we implement CR-BitTorrent and CR-FairTorrent variants. Our simulation model captures the dependence on network bandwidth and the number of seeders as well as selfishness and stability of the participants. The initial experiments show improved fairness and download times, compared to the original BitTorrent and FairTorrent. The performance of selfish and unstable peers decreases by as much as 50%. The CR strategy suits well in environments where direct reciprocity has shown little effect. Contrasted to existing solutions, the CR strategy rewards longevity and stability of peers.

The next industrial revolution is foreseen to happen with upcoming Industrial Internet that combines massive data collected by industrial sensors with data analysis for improving the efficiency of operations. Collecting, pre-processing, storing and analyzing such real-time data is a complex task with stringent demands on communication intelligence, QoS and security. In this paper we outline some challenges facing the Industrial Internet, namely integration with 5G wireless networks, Software Defined Machines, ownership and smart processing of digital sensor data. We propose a secure communication architecture for the Industrial Internet based on Smart Spaces and Virtual Private LAN Services. It is a position paper, describing state-of-the-art and a roadmap for future research on the Industrial Internet.

The increased utilization of the new types of cockpit communications, including controller pilot data link communications (CPDLC), puts the airplane at higher risk of hacking or interference than ever before. We review the technological characteristics and properties of the CPDLC and construct the corresponding threat model. Based on the limitations imposed by the system parameters, we propose several solutions for the improved security of the data messaging communication used in air traffic management (ATM). We discuss the applicability of elliptical curve cryptography (ECC), protected aircraft communications addressing and reporting systems (PACARs) and the Host Identity Protocol (HIP) as possible countermeasures to the identified security threats. In addition, we consider identity-defined networking (IDN) as an example of a genuine security solution which implies global changes in the whole air traffic communication system.

The search engine Shodan crawls the Internet to collect banners from Internet connected devices. When making this information publicly available, anyone can search and find these devices. Results from Shodan show that it is not only web or mail servers that are connected, but also industrial Control Systems (ICS) and Internet of Things (IoT) devices. Some of these devices use protocols that were invented more than 20 years ago. These protocols are not designed to be exposed on the Internet and since they lack security mechanisms, they are vulnerable to attacks. With help from Shodan we have searched for vulnerable devices using search queries corresponding to ICS and IoT protocols. To find the security flaws in protocols, we utilized the vulnerability and exploit database Rapid7. Our results indicate that there are several hundreds of online devices that are vulnerable in Sweden.

In this article, we propose an energy -aware load monitoring model, called e-Mon, for enabling energy aware load balancing in Peer-to-Peer (P2P) systems. P2P is a scalable and self-organizing technology for utilizing computational resources of the end-user devices for the benefit of a computing system. In P2P systems, the need for fair balance of load is crucial since the end-users need to be incentivized to participate in the system. The short battery life, caused by additional strain on the computational resources of the end-user devices, is a significant negative incentive factor for mobile end-users of current P2P systems. The e-Mon model, proposed in this article, enables moving load from energy-critical to less energy-critical nodes in P2P systems. This is done by including the energy status of a peer node as one of the factors defining a nodes load. The model helps saving the energy of mobile P2P nodes, particularly in cases when the remaining battery capacity is low. The article provides a thorough energy efficiency evaluation demonstrating that e-Mon can significantly improve the battery life of mobile nodes by improving the quality and fairness of load balance between heterogeneous nodes. With a proper selection of a load balancing model for the application scenario, e-Mon is shown to achieve up to 470% battery life extension compared to the case with traditional load balancing with no battery monitoring. (C) 2016 Elsevier Inc. All rights reserved.

Dual Connectivity (DC) is an important lower-layer feature accelerating the transition from 4G to 5G that also is expected to play an important role in standalone 5G radio networks. However, even though the packet reordering introduced by DC can significantly impact the performance of upper-layer protocols, no prior work has studied the impact of DC on QUIC. In this paper, we present the first such performance study. Using a series of throughput and fairness experiments, we show how QUIC is affected by different DC parameters, network conditions, and whether the DC implementation aims to improve throughput or reliability. Results for two QUIC implementations (aioquic, ngtcp2) and two congestion control algorithms (NewReno, CUBIC) are presented under both static and highly time-varying network conditions Our findings provide network operators with insights and understanding into the impacts of splitting QUIC traffic in a DC environment. With reasonably selected DC parameters and increased UDP receive buffers, QUIC over DC performs similarly to TCP over DC and achieves optimal fairness under symmetric link conditions when DC is not used for packet duplication. The insights can help network operators provide modern users with better end-to-end service when deploying DC.

Dual Connectivity (DC) is an important lower-layer feature accelerating the transition from 4G to 5G that also is expected to play an important role in standalone 5G. However, even though the packet reordering introduced by DC can significantly impact the performance of upper-layer protocols, no prior work has studied the impact of DC on QUIC. In this paper, we present the first such performance study. Using a series of throughput and fairness experiments, we show how QUIC is affected by different DC parameters, network conditions, and whether the DC implementation aims to improve throughput or reliability. Our findings provide insights into the impacts of splitting QUIC traffic in a DC environment. With reasonably selected DC parameters and increased UDP receive buffers, QUIC over DC performs similarly to TCP over DC and achieves optimal fairness under symmetric link conditions when DC is not used for packet duplication. 

The search engine Shodan crawls the Internet for, among other things, Industrial Control Systems (ICS). ICS are devices used to operate and automate industrial processes. Due to the increasing popularity of the Internet, these devices are getting more and more connected to the Internet. These devices will, if not hidden, be shown on Shodan. This study uses Shodan, together with data found by other researches to plot the trends of these ICS devices. The studied trends focus on the country percentage distribution and the usage of ICS protocols. The results show that all studied countries, except the United States, have decreased their percentage of world total ICS devices. We suspect that this does not represent the real story, as companies are getting better at hiding their devices from online crawlers. Our results also show that the usage of old ICS protocols is increasing. One of the explanations is that industrial devices, running old communication protocols, are increasingly getting connected to the Internet. In addition to the trend study, we evaluate Shodan by studying the time it takes for Shodan to index one of our devices on several networks. We also study ways of avoiding detection by Shodan and show that, by using a method called port knocking, it is relatively easy for a device to hide from Shodan, but remain accessible for legitimate users.

Information-Centric Networking (ICN) introduces a paradigm shift from a host centric communication model for Future Internet architectures. It supports the retrieval of a particular content regardless of the physical location of the content. Emergency network in a disaster scenario or disruptive network presents a significant challenge to the ICN deployment. In this paper, we present a Content dIstribution and retrieval framework in disaster netwOrks for public pRotection (CIDOR) which exploits the design principle of the native CCN architecture in the native Delay Tolerant Networking (DTN) architecture. We prove the feasibility and investigate the performance of our proposed solution using extensive simulation with different classes of the DTN routing strategies in different mobility scenarios. The simulation result shows that CIDOR can reduce the content retrieval time up to 50% while the response ratio is close to 100%.

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol which is intended to be used for constrained networks and devices. CoAP and its extensions (e.g., CoAP observe and group communication) provide the potential for developing novel applications in the Internet-of-Things (IoT). However, a full-fledged CoAP-based application may require significant computing capability, power, and storage capacity in IoT devices. To address these challenges, we present the design, implementation, and experimentation with the CoAP handler which provides transparent CoAP services through the ICN core network. In addition, we demonstrate how the CoAP traffic over an ICN network can unleash the full potential of the CoAP, shifting both overhead and complexity from the (constrained) endpoints to the ICN network. The experiments prove that the CoAP Handler helps to decrease the required computation complexity, communication overhead, and state management of the CoAP server.

Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security.

This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal smartphone. For the driver behavior monitoring, the smartphones cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following drivers online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.

The paper presents an approach and case study of a distributed driver monitoring system. The system utilizes smartphone sensors for detecting dangerous states for a driver in a vehicle. We use a mounted smartphone on a vehicle windshield directed towards the drivers face tracked by the front-facing camera. Using information from camera video frames as well as other sensors, we determine drowsiness, distraction, aggressive driving, and high pulse rate dangerous states that can lead to road accidents. We propose a cloud system architecture to capture statistics from vehicle drivers, analyze it and personalize the smartphone application for the driver. The cloud service provides reports on driver trips as well as statistics to developers. This allows to monitor and improve the system by developing modules for personification and taking into account context situation. We identified statistically that the driver eye closeness is related to the light brightness and drowsiness recognition should be adjusted accordingly.

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

Communication systems in aviation tend to focus on safety rather than security. Protocols such as ADS-B are known to use plain-text, unauthenticated messages and thus are open to various attacks. Controller-Pilot Data Communication Link is no exception and was shown vulnerable also in practice. In this paper, we propose a cryptographic mechanism to provide secure mobility for CPDLC that can enable data encryption and authentication. The protocol is formally verified with the Proverif tool. We also estimate the byte overhead in CPDLC use.

Smart grid systems enhanced the capability of traditional power networks while being vulnerable to different types of cyber-attacks. These vulnerabilities could cause attackers to crash into the network breaching the integrity and confidentiality of the smart grid systems. Therefore, an intrusion detection system (IDS) becomes an important way to provide a secure and reliable services in a smart grid environment. This article proposes a feature-based IDS for smart grid systems. The proposed system performance is evaluated in terms of accuracy, intrusion detection rate (DR), and false alarm rate (FAR). The obtained results show that the random forest and neural network classifiers have outperformed other classifiers. We have achieved a 0.5% FAR on KDD99 dataset and a 0.08% FAR on the NSLKDD dataset. The DR and the testing accuracy on average are 99% for both datasets.

L-band Digital Aeronautical Communication System (LDACS) is a newly proposed modern state-of-the-art system that will enable communication, navigation, and surveillance in the future aviation network. The current LDACS system does not prevent and detect intrusion within the LDACS domain. Therefore, it may suffer from various cyber-attacks, including spoofing, injection and many more attacks. To the best of our knowledge, this paper proposes the first federated learning-based attack detection model, called FL-Guard, for LDACS. Our proposed model exploits a federated learning environment and uses a deep neural network (DNN) to detect possible attacks on LDACS-based Air-Ground communication. FL-Guardis was simulated on a network of four aeroplanes, and the preliminary results show that the proposed model can detect attacks with 89 % accuracy.

The worldwide implementation of Remote Identification (RID) regulations mandates unmanned aircraft systems (UAS), or drones, to openly transmit their identity and real-time location as plain text on the wireless channel. This mandate serves the purpose of accounting for and monitoring drone operations effectively. However, the current RID standard's plain-text transmission exposes it to cyberattacks, including eavesdropping, injection, and impersonation. The Drone Remote Identification Protocol (DRIP) has been proposed to enhance the security of RID. The DRIP ensures information secrecy and confidentiality by using unique session keys while guaranteeing the authenticity of messages and entities through digital signatures. These security features of DRIP make it a preferable alternative to the existing RID standard. However, the lack of verification regarding its security claims raises concerns about its performance in hostile conditions. This paper comprehensively analyzes the DRIP protocol's security features using Tamarin Prover, a formal security verification tool. With its automated reasoning capabilities, Tamarin Prover accurately identifies potential security vulnerabilities within the DRIP protocol while thoroughly verifying its conformance to security properties. Our investigation demonstrates that the DRIP protocol is susceptible to replay attacks. We strongly recommend the inclusion of message freshness components, reducing the lifespan of DET broadcasts, and incorporating a not-after timestamp that is set only a few minutes ahead of the current time. These measures enhance the protocol's defence against replay attacks and ensure message authenticity and Integrity.

The safety of the passengers and goods in airplanes depends upon a number of combined factors. An airplane's condition and the pilot's experience are pivotal, but another very crucial element is the synchronization among the pilots and the air traffic controller (ATC). The communication link between the two carries many uncertain aspects.  The aviation sector often tends to give more priority to safety rather than cybersecurity.  Although the controller-pilot data communication link (CPDLC) system has been proposed for consistent and reliable communication recently, it has some serious drawbacks. In this paper, we highlight the shortcomings of the CPDLC system from a cyber security perspective. We propose a federated learning-based privacy-preserving intrusion detection system (IDS) to protect the CPDLC from uplink and downlink cyber attacks. To ensure a realistic and viable solution, we created our own training dataset by eavesdropping on the air-ground communication at a site near Arlanda airport, Sweden. The anomaly detection model constructed through federated learning has achieved higher accuracy, precision, recall and F1 score as compared to the centrally and locally trained models, enabling higher security. Due to the lower training loss and time, the proposed approach is highly suitable for the sensitive aviation communications.

The L-band Digital Aeronautical Communications System (LDACS) is a key advancement for next-generation aviation networks, enhancing Communication, Navigation, and Surveillance (CNS) capabilities. It operates with VHF Datalink mode 2 (VDLm2) and features a seamless handover mechanism to maintain uninterrupted communication between aircraft and ground stations (GSs), improving safety and efficiency in air traffic management (ATM). However, LDACS’ handover process encounters significant security risks due to inadequate authentication and key agreement between aircraft and ground station controllers (GSCs) during handovers. This vulnerability threatens communications’ confidentiality, integrity, and authenticity, posing risks to flight safety and sensitive data. Therefore, developing and implementing a robust security framework to protect aviation communications is essential. In response, we have proposed a security solution specifically designed to protect LDACS handovers. Our solution uses a mutual authentication and key agreement mechanism tailored for LDACS handovers, ensuring robust security for all types of handovers, including Intra GSC - Intra Aeronautical Telecommunication Network (ATN), Inter GSC - Intra ATN, and Inter GSC - Inter ATN. Our approach utilizes post-quantum cryptography to protect aviation communication systems against potential post-quantum threats, such as unauthorized access to flight data, interception of communication, and spoofing of aircraft identity. Furthermore, our proposed solution has undergone a thorough informal security analysis to ensure its effectiveness in addressing handover challenges and offering robust protection against various threats. It seamlessly integrates with the LDACS framework, delivering low Bit Error Rate (BER) and latency levels, making it a highly reliable approach in practice.

The aviation industry faces significant challenges due to rising global air travel demand. Frequency saturation in Air Traffic Management (ATM) leads to communication problems, necessitating the enhancement of traditional systems. The Single European Sky ATM Research (SESAR) initiative, backed by the European Commission, aims to digitize ATM, with the L-band Digital Aeronautical Communications System (LDACS) as a key component. LDACS aims to improve communication, enhance surveillance, and optimize airspace usage for safer, more efficient ATM. Although LDACS is protected against most cyberattacks, a critical security objective, anonymity, is currently overlooked. To strengthen LDACS's security, robust authentication mechanisms, Post-Quantum security, and measures to ensure aircraft anonymity are crucial. Therefore, we propose a comprehensive security framework to enhance LDACS's cybersecurity, focusing on mutual authentication and key agreement. The protocol uses Physical Unclonable Function (PUF) for robust mutual authentication and Bit-flipping Key Encapsulation (BIKE) for secure session key establishment utilizing Post-Quantum Cryptography (PQC). This framework ensures anonymity and secure communication between aircraft and ground stations while minimizing message exchange, latency, and data overhead. An informal security analysis confirms our proposed framework's potential to augment the efficiency and security of ATM operations.

Communication systems in aviation tend to focus on safety rather than security. Protocols such as Automatic Dependent Surveillance-Broadcast (ADS-B) use plain-text, unauthenticated messages and, therefore, open to various attacks. The open and shared nature of the ADS-B protocol makes its messages extremely vulnerable to various security threats, such as jamming, flooding, false information, and false Squawk attacks. To handle this security issue in the ADS-B system, a state-of-theart dataset is required to train the ADS-B system against these attacks using machine learning algorithms. Therefore, we generated the dataset with four new attacks: name jumping attack, false information attack, false heading attack, and false squawk attack. After the dataset generation, we performed some data pre-processing steps, including removing missing values, removing outliers from data, and data transformation. After pre-processing, we applied three machine learning algorithms. Logistic regression, Naive Bayes, and K-Nearest Neighbor (KNN) are used in this paper. We used accuracy, precision, recall, F1-Score, and false alarm rate (FAR) to evaluate the performance of machine learning algorithms. KNN outperformed Naive Bayes and logistic regression algorithms in terms of the results. We achieved 0% FAR for anomaly messages, and for normal ADS-B messages, we achieved 0.10% FAR, respectively. On average more than 99.90% accuracy, precision, recall, and F1-score are achieved using KNN for both normal and anomaly ADS-B messages.

Communication systems in aviation tend to focus on safety rather than security. Protocols such as Automatic Dependent Surveillance-Broadcast (ADS-B) use plain-text, unauthenticated messages and, therefore, open to various attacks. The open and shared nature of the ADS-B protocol makes its messages extremely vulnerable to various security threats, such as jamming, flooding, false information, and false Squawk attacks. To handle this security issue in the ADS-B system, a state-of-the-art dataset is required to train the ADS-B system against these attacks using machine learning algorithms. 

Controller Pilot Data Link Communications (CPDLC) enhances air traffic communication by replacing traditional voice transmissions with digital messages over Very High Frequency (VHF) radio systems. This transition improves communication resilience by providing clear, text-based instructions that reduce misunderstandings and increase bandwidth efficiency by enabling more data to be transmitted simultaneously. It benefits congested airspace by reducing radio frequency congestion and minimizing communication errors. However, due to the plain-text nature of its messages, CPDLC faces significant security challenges, making it vulnerable to cyber-attacks such as eavesdropping, modification, injection, and man-in-the-middle (MITM) attacks. This vulnerability allows motivated attackers to intercept CPDLC messages using inexpensive devices like Software-Defined Radio (SDR), HACKRF-one, and an antenna. Such breaches can lead to fatal safety incidents, severely impacting passengers and the aviation industry. To address this, we proposed a robust security framework for securing CPDLC communication by implementing critical measures, including mutual authentication, secure key establishment, and handover. The proposed framework has been tested on hardware to verify its effectiveness in practical scenarios, ensuring it aligns with existing CPDLC standards and integrates seamlessly into current systems without impacting operational efficiency. Our findings indicate that the proposed security framework enhances CPDLC's defenses against potential cyber threats while maintaining system performance, making it feasible to protect global air traffic communications.

Multipath routing gains clear network performance advantages for data streaming in networked systems with high path diversity. The level of packet reordering, however, becomes higher: distant packets are reordered, the application performance is reduced due to head-of-line blocking at the destination, and a large resequencing buffer is needed for sorting incoming packets. In this paper, we study by simulation the stochastic compensation effect to reduce packet reordering. If a source randomizes packet scheduling into multiple paths of random transmission delays, then these two sides of randomness “quench” each other. We perform comparison experiments to test this hypothesis in various multipath configurations, focusing on deterministic vs. randomized strategies of packet scheduling. The experiments confirm the existence of the stochastic compensation effect and its considerable influence on the application performance. © 2016, UK Simulation Society. All rights reserved.