Biblio

Physical layer (PHY) security in decode-and-forward (DF) relay systems is discussed. Based on the types of wiretap links, the secrecy performance of three typical secure DF relay models is analyzed. Different from conventional works in this field, rigorous derivations of the secrecy channel capacity are provided from an information-theoretic perspective. Meanwhile, closed-form expressions are derived to characterize the secrecy outage probability (SOP). For the sake of unveiling more system insights, asymptotic analyses are performed on the SOP for a sufficiently large signal-to-noise ratio (SNR). The analytical results are validated by computer simulations and are in excellent agreement.

Nowadays, 5G has been widely used in various fields. People are starting to turn their attention to 6G. Therefore, at the beginning, this paper describes in detail the principle and performance of 6G, and introduces the key technologies of 6G, Cavity technology and THz technology. Based on the high-performance indicators of 6G, we then study the possible application changes brought by 6G, for example, 6G technology will make remote surgery and remote control possible. 6G technology will make remote surgery and remote control possible. 6G will speed up the interconnection of everything, allowing closer and faster connection between cars. Next, virtual reality is discussed. 6G technology will enable better development of virtual reality technology and enhance people's immersive experience. Finally, we present the issues that need to be addressed with 6G technology, such as cybersecurity issues and energy requirements. As well as the higher challenges facing 6G technology, such as connectivity and communication on a larger social plane.

Machine learning (ML) models are increasingly being used in the development of Malware Detection Systems. Existing research in this area primarily focuses on developing new architectures and feature representation techniques to improve the accuracy of the model. However, recent studies have shown that existing state-of-the art techniques are vulnerable to adversarial machine learning (AML) attacks. Among those, data poisoning attacks have been identified as a top concern for ML practitioners. A recent study on clean-label poisoning attacks in which an adversary intentionally crafts training samples in order for the model to learn a backdoor watermark was shown to degrade the performance of state-of-the-art classifiers. Defenses against such poisoning attacks have been largely under-explored. We investigate a recently proposed clean-label poisoning attack and leverage an ensemble-based Nested Training technique to remove most of the poisoned samples from a poisoned training dataset. Our technique leverages the relatively large sensitivity of poisoned samples to feature noise that disproportionately affects the accuracy of a backdoored model. In particular, we show that for two state-of-the art architectures trained on the EMBER dataset affected by the clean-label attack, the Nested Training approach improves the accuracy of backdoor malware samples from 3.42% to 93.2%. We also show that samples produced by the clean-label attack often successfully evade malware classification even when the classifier is not poisoned during training. However, even in such scenarios, our Nested Training technique can mitigate the effect of such clean-label-based evasion attacks by recovering the model's accuracy of malware detection from 3.57% to 93.2%.

5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results.

This paper uses the test tool provided by the Internet Protocol Version 6 (IPv6) Forum to test the protocol conformance of IPv6 devices. The installation and testing process of IPv6 Ready Logo protocol conformance test suite developed by TAHI PROJECT team is described in detail. This section describes the test content and evaluation criteria of the suite, analyzes the problems encountered during the installation and use of the suite, describes the method of analyzing the test results of the suite, and describes the test content added to the latest version of the test suite. The test suite can realize automatic testing, the test cases accurately reflect the requirements of the IPv6 protocol specification, can be used to judge whether IPv6-based Internet of Things(IoT) devices meets the relevant protocol standards.

Existing solutions for scheduling arbitrarily complex distributed applications on networks of computational nodes are insufficient for scenarios where the network topology is changing rapidly. New Internet of Things (IoT) domains like the Internet of Robotic Things (IoRT) and the Internet of Battlefield Things (IoBT) demand solutions that are robust and efficient in environments that experience constant and/or rapid change. In this paper, we demonstrate how recent advancements in machine learning (in particular, in graph convolutional neural networks) can be leveraged to solve the task scheduling problem with decent performance and in much less time than traditional algorithms.

As a fundamental departure from the IP design which encodes source and destination addresses in each packet, Named Data Networking (NDN) directly uses application-defined data names for network layer communications. While bringing important data-centric benefits, the semantic richness of NDN names has also raised confidentiality and privacy concerns. In this paper, we first define the problem of name confidentiality, and then investigate the solution space through a comprehensive examination of all the proposed solutions up to date. Our work shows that the proposed solutions are simply different means to hide the actual data names via a layer of translation; they differ in where and how the translation takes place, which lead to different trade-offs in feasibility, efficiency, security, scalability, and different degrees of adherence to NDN's data-centric communications. Our investigation suggests the feasibility of a systematic design that can enable NDN to provide stronger name confidentiality and user privacy as compared to today's TCP/IP Internet.

This paper presents a new framework for SATCOM jamming resiliency in the presence of a smart adversary jammer that can prioritize specific channels to attack with a non-uniform probability of distribution. We first develop a model and a defense action strategy based on a Markov decision process (MDP). We propose a greedy algorithm for the MDP-based defense algorithm's policy to optimize the expected user's immediate and future discounted rewards. Next, we remove the assumption that the user has specific information about the attacker's pattern and model. We develop a Q-learning algorithm-a reinforcement learning (RL) approach-to optimize the user's policy. We show that the Q-learning method provides an attractive defense strategy solution without explicit knowledge of the jammer's strategy. Computer simulation results show that the MDP-based defense strategies are very efficient; they offer a significant data rate advantage over the simple random hopping approach. Also, the proposed Q-learning performance can achieve close to the MDP approach without explicit knowledge of the jammer's strategy or attacking model.

Physical layer security has a paramount importance in tactical wireless networks. Traditional approaches may not fulfill all requirements, demanding additional sophisticated techniques. Thus, Combined Interference and Communications (CIC) emerges as a strategy against message interception in Cognitive Radio Military Networks (CRMN). Since CIC adopts an interference approach under specific CRMN requirements and characteristics, it saves great energy and reduces the receiver detection factor when compared to previous proposals in the literature. However, previous CIC analyses were conducted under vaguely realistic channel models. Thus, the focus of this paper is two-fold. Firstly, we identify more realistic channel models to achieve tactical network scenario channel parameters. Additionally, we use such parameters to evaluate CIC suitability to increase CRMN physical layer security. Numerical experiments and emulations illustrate potential impairments on previous work due to the adoption of unrealistic channel models, concluding that CIC technique remains as an upper limit to increase physical layer security in CRMN.

Cyber deception plays an important role in both proactive and reactive defense systems. Internet of Battlefield things connecting smart devices of any military tactical network is of great importance. The goal of cyber deception is to provide false information regarding the network state, and topology to protect the IoBT's network devices. In this paper, we propose a novel deceptive approach based on game theory that takes into account the topological aspects of the network and the criticality of each device. To find the optimal deceptive strategy, we formulate a two-player game to study the interactions between the network defender and the adversary. The Nash equilibrium of the game model is characterized. Moreover, we propose a scalable game-solving algorithm to overcome the curse of dimensionality. This approach is based on solving a smaller in-size subgame per node. Our numerical results show that the proposed deception approach effectively reduced the impact and the reward of the attacker

Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements, edge devices remain resource-constrained, thus prohibiting complex deep neural networks from deploying and deriving actionable insights from various sensors. Furthermore, deploying sophisticated sensors in a distributed manner to improve decision-making also poses an extra challenge of coordinating and exchanging data between the nodes and server. We propose an architecture that abstracts away these thorny deployment considerations from an end-user (such as a commander or warfighter). Our architecture can automatically compile and deploy the inference model into a set of distributed nodes and server while taking into consideration of the resource availability, variation, and uncertainties.

Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.

Recently deep learning has demonstrated much success within the fields of image and natural language processing, facial recognition, and computer vision. The success is attributed to large, accessible databases and deep learning's ability to learn highly accurate models. Thus, deep learning is being investigated as a viable end-to-end approach to digital communications design. This work investigates the use of adversarial deep learning to ensure that a radio can communicate covertly, via Direct Sequence Spread Spectrum (DSSS), with another while a third (the adversary) is actively attempting to detect, intercept and exploit their communications. The adversary's ability to detect and exploit the DSSS signals is hindered by: (i) generating a set of spreading codes that are balanced and result in low side lobes as well as (ii) actively adapting the encoding scheme. Lastly, DSSS communications performance is assessed using energy constrained devices to accurately portray IoT and IoBT device limitations.

This document paper presents a critical and condensed analyze on series of devices that are intended for the military field, making an overview analysis of the technical solutions presented and that identifying those aspects that are really important for the military field or that offering a new approach. We currently have a wide range of medical devices that can be adapted for use in the military, but this adaptation must follow some well-defined aspects. A device that does not offer 100% reliability will be difficult to adopt in a military system, where mistakes are not allowed.

With the paradigm shift to cloud-based operations, reliable and secure access to and transfer of data between differing security domains has never been more essential. A Cross Domain Solution (CDS) is a guarded interface which serves to execute the secure access and/or transfer of data between isolated and/or differing security domains defined by an administrative security policy. Cross domain security requires trustworthiness at the confluence of the hardware and software components which implement a security policy. Security components must be relied upon to defend against widely encompassing threats – consider insider threats and nation state threat actors which can be both onsite and offsite threat actors – to information assurance. Current implementations of CDS systems use suboptimal Trusted Computing Bases (TCB) without any formal verification proofs, confirming the gap between blind trust and trustworthiness. Moreover, most CDSs are exclusively operated by Department of Defense agencies and are not readily available to the commercial sectors, nor are they available for independent security verification. Still, more CDSs are only usable in physically isolated environments such as Sensitive Compartmented Information Facilities and are inconsistent with the paradigm shift to cloud environments. Our purpose is to address the question of how trustworthiness can be implemented in a remotely deployable CDS that also supports availability and accessibility to all sectors. In this paper, we present a novel CDS system architecture which is the first to use a formally verified TCB. Additionally, our CDS model is the first of its kind to utilize a computation-isolation approach which allows our CDS to be remotely deployable for use in cloud-based solutions.

Among many application domains of machine learning in real-world settings, cyber security can benefit from more automated techniques to combat sophisticated adversaries. Modern network intrusion detection systems leverage machine learning models on network logs to proactively detect cyber attacks. However, the risk of adversarial attacks against machine learning used in these cyber settings is not fully explored. In this paper, we investigate poisoning attacks at training time against machine learning models in constrained cyber environments such as network intrusion detection; we also explore mitigations of such attacks based on training data sanitization. We consider the setting of poisoning availability attacks, in which an attacker can insert a set of poisoned samples at training time with the goal of degrading the accuracy of the deployed model. We design a white-box, realizable poisoning attack that reduced the original model accuracy from 95% to less than 50 % by generating mislabeled samples in close vicinity of a selected subset of training points. We also propose a novel Nested Training method as a defense against these attacks. Our defense includes a diversified ensemble of classifiers, each trained on a different subset of the training set. We use the disagreement of the classifiers' predictions as a data sanitization method, and show that an ensemble of 10 SVM classifiers is resilient to a large fraction of poisoning samples, up to 30% of the training data.

Deep learning (DL) models have been shown to be vulnerable to adversarial attacks. DL model security against adversarial attacks is critical to using DL-trained models in forward deployed systems, e.g. facial recognition, document characterization, or object detection. We provide results and lessons learned applying a moving target defense (MTD) strategy against iterative, gradient-based adversarial attacks. Our strategy involves (1) training a diverse ensemble of DL models, (2) applying randomized affine input transformations to inputs, and (3) randomizing output decisions. We report a primary lesson that this strategy is ineffective against a white-box adversary, which could completely circumvent output randomization using a deterministic surrogate. We reveal how our ensemble models lacked the diversity necessary for effective MTD. We also evaluate our MTD strategy against a black-box adversary employing an ensemble surrogate model. We conclude that an MTD strategy against black-box adversarial attacks crucially depends on lack of transferability between models.

Experimentation focused on assessing the value of complex visualisation approaches when compared with alternative methods for data analysis is challenging. The interaction between participant prior knowledge and experience, a diverse range of experimental or real-world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant experimentation results. This paper outlines a hybrid approach proposed for experimentation with complex interactive data analysis tools, specifically for computer network traffic analysis. The approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of software performance which is supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis.

Nowadays interest in IoT solutions is growing. A significant barrier to the use of these solutions in military applications is to ensure the security of data transmission and authentication of data sources and recipients of the data. Developing an efficient solution to these problems requires finding a compromise between the facts that the sensors often are mobile, use wireless communication, usually have the small processing power and have little energy resources. The article presents the security domain designated for cooperating mobile sensor nodes. The domain has the following features: the strong authentication of each domain member, cryptographic protection of data exchange in the data link layer and protection of data stored in the sensor node resources. The domain is also prepared to perform diagnostic procedures and to exchange sensory data with other domains securely. At each node, the Trusted Platform Module (TPM) is used to support these procedures.

Naval Battlefield Network communications rely on wireless network technologies to transmit data between different naval entities, such as ships and shore nodes. Existing naval battle networks heavily depend on the satellite communication system using single-path TCP for reliable, non-interactive data. While satisfactory for traditional use cases, this communication model may be inadequate for outlier cases, such as those arising from satellite failure and wireless signal outage. To promote network stability and assurance in such scenarios, the addition of unmanned aerial vehicles to function as relay points can complement network connectivity and alleviate potential strains in adverse conditions. The inherent mobility of aerial vehicles coupled with existing source node movements, however, leads to frequent network handovers with non-negligible overhead and communication interruption, particularly in the present single-path model. In this paper, we propose a solution based on multi-path TCP and software-defined networking, which, when applied to mobile wireless heterogeneous networks, reduces the network handover delay and improves the total throughput for transmissions among various naval entities at sea and littoral. In case of single link failure, the presence of a connectable relay point maintains TCP connectivity and reduces the risk of service interruption. To validate feasibility and to evaluate performance of our solution, we constructed a Mininet- WiFi emulation testbed. Compared against single-path TCP communication methods, execution of the testbed when configured to use multi-path TCP and UAV relays yields demonstrably more stable network handovers with relatively low overhead, greater reliability of network connectivity, and higher overall end-to-end throughput. Because the SDN global controller dynamically adjusts allocations per user, the solution effectively eliminates link congestion and promotes more efficient bandwidth utilization.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system, in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents, which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite-IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information compared with a baseline scheme, in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared with the baseline.

Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).

The recent trend of military is to combined Internet of Things (IoT) knowledge to their field for enhancing the impact in battlefield. That's why Internet of battlefield (IoBT) is our concern. This paper discusses how Fog Radio Access Network(F-RAN) can provide support for local computing in Industrial IoT and IoBT. F-RAN can play a vital role because of IoT devices are becoming popular and the fifth generation (5G) communication is also an emerging issue with ultra-low latency, energy consumption, bandwidth efficiency and wide range of coverage area. To overcome the disadvantages of cloud radio access networks (C-RAN) F-RAN can be introduced where a large number of F-RAN nodes can take part in joint distributed computing and content sharing scheme. The F-RAN in IoBT is effective for enhancing the computing ability with fog computing and edge computing at the network edge. Since the computing capability of the fog equipment are weak, to overcome the difficulties of fog computing in IoBT this paper illustrates some challenging issues and solutions to improve battlefield efficiency. Therefore, the distributed computing load balancing problem of the F-RAN is researched. The simulation result indicates that the load balancing strategy has better performance for F-RAN architecture in the battlefield.

FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.

Cyber-physical systems are an integral component of weapons, sensors and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.