Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

Cybersecurity: mapping the ethical terrain

This edited collection examines the ethical trade-offs involved in cybersecurity: between security and privacy; individual rights and the good of a society; and between the types of burdens placed on particular groups in order to protect others. Foreword Governments and society are increasingly reliant on cyber systems. Yet the more reliant we are upon cyber systems, the more vulnerable we are to serious harm should these systems be attacked or used in an attack. This problem of reliance and vulnerability is driving a concern with securing cyberspace. For example, a ‘cybersecurity’ team now forms part of the US Secret Service. Its job is to respond to cyber-attacks in specific environments such as elevators in a building that hosts politically vulnerable individuals, for example, state representatives. Cybersecurity aims to protect cyberinfrastructure from cyber-attacks; the concerning aspect of the threat from cyber-attack is the potential for serious harm that damage to cyber-infrastructure presents to resources and people. These types of threats to cybersecurity might simply target information and communication systems: a distributed denial of service (DDoS) attack on a government website does not harm a website in any direct way, but prevents its normal use by stifling the ability of users to connect to the site. Alternatively, cyber-attacks might disrupt physical devices or resources, such as the Stuxnet virus, which caused the malfunction and destruction of Iranian nuclear centrifuges. Cyber-attacks might also enhance activities that are enabled through cyberspace, such as the use of online media by extremists to recruit members and promote radicalisation. Cyber-attacks are diverse: as a result, cybersecurity requires a comparable diversity of approaches. Cyber-attacks can have powerful impacts on people’s lives, and so—in liberal democratic societies at least—governments have a duty to ensure cybersecurity in order to protect the inhabitants within their own jurisdiction and, arguably, the people of other nations. But, as recent events following the revelations of Edward Snowden have demonstrated, there is a risk that the governmental pursuit of cybersecurity might overstep the mark and subvert fundamental privacy rights. Popular comment on these episodes advocates transparency of government processes, yet given that cybersecurity risks represent major challenges to national security, it is unlikely that simple transparency will suffice. Managing the risks of cybersecurity involves trade-offs: between security and privacy; individual rights and the good of a society; and types of burdens placed on particular groups in order to protect others. These trade-offs are often ethical trade-offs, involving questions of how we act, what values we should aim to promote, and what means of anticipating and responding to the risks are reasonably—and publicly—justifiable. This Occasional Paper (prepared for the National Security College) provides a brief conceptual analysis of cybersecurity, demonstrates the relevance of ethics to cybersecurity and outlines various ways in which to approach ethical decision-making when responding to cyber-attacks

Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

A Comprehensive Cybersecurity Defense Framework for Large Organizations

There is a growing need to understand and identify overarching organizational requirements for cybersecurity defense in large organizations. Applying proper cybersecurity defense will ensure that the right capabilities are fielded at the right locations to safeguard critical assets while minimizing duplication of effort and taking advantage of efficiencies. Exercising cybersecurity defense without an understanding of comprehensive foundational requirements instills an ad hoc and in many cases conservative approach to network security. Organizations must be synchronized across federal and civil agencies to achieve adequate cybersecurity defense. Understanding what constitutes comprehensive cybersecurity defense will ensure organizations are better protected and more efficient. This work, represented through design science research, developed a model to understand comprehensive cybersecurity defense, addressing the lack of standard requirements in large organizations. A systemic literature review and content analysis were conducted to form seven criteria statements for understanding comprehensive cybersecurity defense. The seven criteria statements were then validated by a panel of expert cyber defenders utilizing the Delphi consensus process. Based on the approved criteria, the team of cyber defenders facilitated the development of a Comprehensive Cybersecurity Defense Framework prototype for understanding cybersecurity defense. Through the Delphi process, the team of cyber defense experts ensured the framework matched the seven criteria statements. An additional and separate panel of stakeholders conducted the Delphi consensus process to ensure a non-biased evaluation of the framework. The comprehensive cybersecurity defense framework is developed through the data collected from two distinct and separate Delphi panels. The framework maps risk management, behavioral, and defense in depth frameworks with cyber defense roles to offer a comprehensive approach to cyber defense in large companies, agencies, or organizations. By defining the cyber defense tasks, what those tasks are trying to achieve and where best to accomplish those tasks on the network, a comprehensive approach is reached

A survey on vulnerability of federated learning: A learning algorithm perspective

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

A survey on vulnerability of federated learning: A learning algorithm perspective

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions