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.

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/).

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patients health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness systems integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

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 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.

Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.

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.

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.

This document defines terminology and requirements for solutions produced by the Drone Remote Identification Protocol (DRIP) Working Group. These solutions will support Unmanned Aircraft System Remote Identification and tracking (UAS RID) for security, safety, and other purposes (e.g., initiation of identity-based network sessions supporting UAS applications). DRIP will facilitate the use of existing Internet resources to support RID and to enable enhanced related services, and it will enable online and offline verification that RID information is trustworthy.

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 conceptualisation of the sixth generation of mobile wireless networks (6G) has already started with some potential disruptive technologies resonating as enablers for driving the emergence of a number of innovative applications. Particularly, 6G will be a prominent supporter for the evolution towards a truly Intelligent Transportation System and the realization of the Smart City concept by fulfilling the limitations of 5G, once vehicular networks are becoming highly dynamic and complex with stringent requirements on ultra-low latency, high reliability, and massive connections. More importantly, providing security and privacy to such critical systems should be a top priority as vulnerabilities can be catastrophic, thus there are huge concerns regarding data collected from sensors, people and their habits. In this paper, we provide a timely deliberation of the role that promissory 6G enabling technologies such as artificial intelligence, network softwarisation, network slicing, blockchain, edge computing, intelligent reflecting surfaces, backscatter communications, terahertz links, visible light communications, physical layer authentication, and cell-free massive multiple-input multiple-output (MIMO) will play on providing the expected level of security and privacy for the Internet of Vehicles.

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.

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.

Controller-Pilot Data Link Communication, a technology that has been introduced to help offload the congested VHF voice communication in larger airports, is being questioned on its sufficiency in security. As the traffic load in air traffic communication keeps demanding more reliable and secure systems, we in this paper look at how widely CPDLC is actually used in practice in Europe. By using the newly introduced technology in software defined radios, we show that it is possible to capture and decode CPDLC messages to readable plain text. Furthermore, we discuss which type of attacks could be possible with information retrieved from CPDLC communication.

Todays vehicles are examples of Cyber-Physical Systems (CPS) controlled by a large number of electronic control units (ECUs), which manage everything from heating to steering and braking. Due to the increasing complexity and inter-dependency of these units, it has become essential for an ECU to be able to ensure the integrity of the firmware running on other ECUs to guarantee its own correct operation. Existing solutions for firmware attestation use a centralized approach, which means a single point of failure. In this article, we propose and investigate a decentralized firmware attestation scheme for the automotive domain. The basic idea of this scheme is that each ECU can attest to the state of those ECUs on which it depends. Two flavors of ECU attestation, i.e., parallel and serial solution, were designed, implemented, and evaluated. The two variants were compared in terms of both detection performance (i.e., the ability to identify unauthorized firmware modifications) and timing performance. Our results show that the proposed scheme is feasible to implement and that the parallel solution showed a significant improvement in timing performance over the serial solution.

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.

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.

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.

Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned 6G wireless systems, possible challenges with different 6G technologies, and the potential solutions. We provide our vision on 6G security and security key performance indicators (KPIs) with the tentative threat landscape based on the foreseen 6G network architecture. Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications. We also give the reader some insights into the standardization efforts and research-level projects relevant to 6G security. In particular, we discuss the security considerations with 6G enabling technologies such as distributed ledger technology (DLT), physical layer security, distributed AI/ML, visible light communication (VLC), THz, and quantum computing. All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

The goal of this paper is to develop, deploy, test, and evaluatea a lightweight portable intrusion detection system (LPIDS) over wireless networks by adopting two different string matching algorithms: Aho-Corasick algorithm and Knuth-Morris-Pratt algorithm (KMP). Thus, this research contributes in three ways. First, an efficient and lightweight IDS (LPIDS) is proposed. Second, the LPIDS was developed, implemented, tested, and evaluated using Aho-Corasick and KMP on two different hardware platforms: Wi-Fi Pineapple and Raspberry Pi. Third, a comparative analysis of proposed LPIDS is done in terms of network metrics such as throughput, power consumption, and response time with regard to their counterparts. Additionally, the proposed LPIDS is suggested for consultants while performing security audits. The experimental results reveal that Aho-Corasick performs better than KMP throughout the majority of the process, but KMP is typically faster in the beginning with fewer rules. Similarly, Raspberry Pi shows remarkably higher performance than Wi-Fi Pineapple in all of the measurements. Moreover, we compared the throughput between LPIDS and Snort, it is observed and analyzed that former has significantly higher throughput than later when most of the rules do not include content parameters. This paper concludes that due to computational complexity and slow hardware processing capabilities of Wi-Fi Pineapple, it could not become suitable IDS in the presence of different pattern matching strategies. Finally, we propose modification of Snort to increase the throughput of the system.

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.

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.

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.

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.

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 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.

The popularity of new cyber-physical systems such as Industrial Internetor Industrial Internet of Things (IIoT) in new applications is creating new requirementssuch as high security, enhanced scalability, and optimal utilization of networkresources, efficient energy management and low operational cost. Specifically, theincreasing number of connected devices and new services will result in the increasingcapacity requirements for the cyber-physical systems. Thus, accommodating thesecure connectivity for this expected traffic growth is an imminent requirement offuture cyber-physical systems. Although the existing secure communication architecturesare able to provide a sufcient level of security, they are suffering from limitationssuch as limited scalability, over utilization of network resources and highoperational cost, mainly due to the complex and static security management procedures.On these grounds, SDN and NFV are promising technologies which areexpected to solve the limitations in current communication networks. The pursuit ofa cohesive cyber security strategy will minimize the risks and enable society to take advantage of the opportunities associated with the IIoT. In this chapter, we presentsthe possible secure connectivity solutions for IIoT/Industrial Internet.

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.

The pervasive and smart healthcare is important for elderly patients which has revolutionized the medical world and caught the attention from industry and academia with the help of portable sensor-enabled devices. Tiny size and resource-constrained nature restricts them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the key challenges to be tackled carefully for ubiquitous healthcare. Energy efficiency, reliability and longer battery cycle are the vital ingredients for wearable devices to empower cost-effective and pervasive medical environment. Thus,this research work has three key contributions. First, a novel transmission power control driven energy efficient algorithm (EEA) is proposed to enhance energy, battery lifetime and reliability while monitoring the health status of elderly patients. Proposed EEA and conventional constant transmission power control (TPC) are evaluated by adopting real-time datasets of static (i.e., wheelchair sitting) and dynamic (i.e., wheelchair moving) body postures of elderly patients. Second, smart healthcare framework is proposed. Third, performance metrics such as, energy drain, battery lifetime and reliability are introduced and calculated by considering average and threshold RSSI and TPC values. Finally, it is observed through experimental analysis that the proposed EEA enhances energy efficiency with acceptable PLR than the constant TPC during data transmission.

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, Body Sensor Networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio-signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (PTPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus this paper first, proposes a novel joint transmission power control (TPC) and duty-cycle adaptation based framework for pervasive healthcare. Second, adaptive energy-efficient transmission power control (AETPC) algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a Feedback Control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio (PLR) by adjusting both transmission power and duty-cycle unlike the conventional constant TPC and PTPC methods.

Today's vehicles are equipped with a large number of Electronic Control Units (ECUs), which control everything from heating to steering and braking. Due to the increasing complexity and inter-dependency of these units, it has become essential for an ECU to be able to ensure the integrity of the firmware running on other ECU's to guarantee its own correct operation. Existing solutions for firmware attestation uses a centralized approach which means a single point of failure. In this article, we propose and investigate a decentralized firmware attestation scheme for the automotive domain. The basic idea of this scheme is that each ECU can attest the state of those ECU's on which it depends. Two flavors of ECU attestation i.e. parallel and serial solution were designed, implemented and evaluated. The two variants were compared in terms of both detection performance (i.e., the ability to identify unauthorized firmware modifications) and timing performance. Our results show that the proposed scheme is feasible to implement and that the parallel solution showed a significant improvement in timing performance over the serial solution.

The proliferation of the Internet of Things (IoT) technologies have strengthen the self-monitoring and autonomous characteristics of the sensor networks deployed in numerous application areas. The recent developments of the edge computing paradigms have also enabled on-site processing and managing capabilities of sensor networks. In this paper, we introduce a system model that enables end-to-end secure connectivity between low-power IoT devices and UAVs, that helps to manage data processing tasks of a heterogeneous wireless sensor networks. The performance of proposed solution is analyzed by using simulation results. Moreover, in order to demonstrate the practical usability of the proposed solution, the prototype implementation is presented using commercial off-the-shelf devices.

Smart meters are considered as foundational part of the smart metering infrastructure (SMI) in smart energy networks. Smart meter is a digital device that makes use of twoway communication between consumer and utility to exchange, manage and control energy consumptions within a home. However, despite all the features, a smart meter raises several securityrelated concerns. For instance, how to exchange data between the legal entities (e.g., smart meter and utility server) while maintaining privacy of the consumer. To address these concerns, authentication and key agreement in SMI can provide important security properties that not only to maintain a trust between the legitimate entities but also to satisfy other security services. This work presents a lightweight authentication and key agreement (LAKA) that enables trust, anonymity, integrity and adequate security in the domain of smart energy network. The proposed scheme employs hybrid cryptography to facilitate mutual trust (authentication), dynamic session key, integrity, and anonymity. We justify the feasibility of the proposed scheme with a testbed using 802.15.4 based device (i.e., smart meter). Moreover, through the security and performance analysis, we show that the proposed scheme is more effective and energy efficient compared to the previous schemes.

In this paper, we propose a game-theoretic model of the mobile network market. The market is presented by three sides: primary mobile network operators (MNO), mobile virtual network operators (MVNO) and consumers of the services. MVNO are mobile operators without their own infrastructure. They buy resources from MNO and compete with other MVNO for the consumers selling a service in the mobile network market. We construct a two-stage game. In the first stage, MVNO (players) select the MNO, one or several, and then announce the price for their service for the consumers in this MNO. After the profile of prices is determined, the consumers are distributed among MVNOs following the logistic function. The equilibrium in this two-stage game is constructed. For identical consumers, the analytic formulas for the solution are derived.

The widespread Internet of Things (IoT) ecosystems empower the deployment of various Bluetooth Low Energy (BLE) sensor nodes in many ambient assisted living (AAL) type applications. Regardless of their limitations, these low-power IoT sensor nodes need pervasive and secure connections to transfer the aggregated data to the central servers located in remote clouds which will perform further processing and storing functions. The common practice is to use one or multiple dedicated gateways to assist the communication between the sensor and the cloud. This paper presents a mobile-based relay assistance solution for establishing secure end-to-end (E2E) connectivity between low power IoT sensors and cloud servers without using a dedicated gateway. za The prototype implementation and the described security features verify the technical readiness of the proposed solution.

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.

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.

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.

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.

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.

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.

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.

Internet of Things (IoT) devices need pervasive and secure connections to transfer the aggregated data to the central servers located in remote clouds where the collected data further processed and stored. However, most low-power IoT devices cannot transmit the collected the data directly to such servers due the limited transmission power and range. Thus, third-party devices such as smart mobile phones are used as a relay to establish the communication link between IoT devices and the cloud server. This paper demonstrates a mobile-based relay assistance solution for secure end-to-end connectivity between low-power IoT sensors and cloud servers by using Bluetooth Low Energy (BLE) technology. The prototype implementation verifies the technical readiness of the proposed solution.

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.