Thanassis Giannetsos

Intrusion detection of sinkhole attacks in wireless sensor networks

In this paper, we present an Intrusion Detection System designed for wireless sensor networks and show how it can be configured to detect Sinkhole attacks. A Sinkhole attack forms a serious threat to sensor networks. We study in depth this attack by presenting how it can be launched in realistic networks that use the MintRoute protocol of TinyOS. MintRoute is the most widely used routing protocol in sensor network deployments, using the link quality metric to build the corresponding routing tree. Having implemented this attack in TinyOS, we embed the appropriate rules in our IDS system that will enable it to detect successfully the intruder node. We demonstrate this in our own sensor network deployment and we also present simulation results to confirm the effectiveness and accuracy of the algorithm in the general case of random topologies.

LIDeA: a distributed lightweight intrusion detection architecture for sensor networks

Wireless sensor networks are vulnerable to adversaries as they are frequently deployed in open and unattended environments. Preventive mechanisms can be applied to protect them from an assortment of attacks. However, more sophisticated methods, like intrusion detection systems, are needed to achieve a more autonomic and complete defense mechanism, even against attacks that have not been anticipated in advance. In this paper, we present a lightweight intrusion detection system, called LIDeA, designed for wireless sensor networks. LIDeA is based on a distributed architecture, in which nodes overhear their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We show how such a system can be implemented in TinyOS, which components and interfaces are needed, and what is the resulting overhead imposed.

Cooperative Intrusion Detection in Wireless Sensor Networks

We consider the problem of cooperative intrusion detection in wireless sensor networks where the nodes are equipped with local detector modules and have to identify the intruder in a distributed fashion. The detector modules issue suspicions about an intrusion in the sensors neighborhood. We formally define the problem of intrusion detection and identify necessary and sufficient conditions for its solvability. Based on these conditions we develop a generic algorithm for intrusion detection and present simulations and experiments which show the effectiveness of our approach.

SPPEAR: security & privacy-preserving architecture for participatory-sensing applications

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of participatory sensing (PS). The openness of such systems and the richness of user data they entail raise significant concerns for their security, privacy and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a comprehensive solution. That is, a secure and accountable PS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after a PS system that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving PS entities (servers). We address these seemingly contradicting requirements with our SPPEAR architecture. Our full blown implementation and experimental evaluation demonstrate that SPPEAR is efficient, practical, and scalable. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system is a comprehensive solution that significantly extends the state-of-the-art and can catalyze the deployment of PS applications.

People-centric sensing in assistive healthcare: Privacy challenges and directions

As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static-topology and an application-aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor-based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people-centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people-centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people-centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people-centric sensing applications and spawn further research in this area. Copyright

Trustworthy People-Centric Sensing: Privacy, security and user incentives road-map

The broad capabilities of widespread mobile devices have paved the way for People-Centric Sensing (PCS). This emerging paradigm enables direct user involvement in possibly large-scale and diverse data collection and sharing. Unavoidably, this raises significant privacy concerns, as participants may inadvertently reveal a great deal of sensitive information. However, ensuring user privacy, e.g., by anonymizing data they contribute, may cloak faulty (possibly malicious) actions. Thus, PCS systems must not only be privacy-preserving but also accountable and reliable. As an increasing number of applications (e.g., assistive healthcare and public safety systems) can significantly benefit from people-centric sensing, it becomes imperative to meet these seemingly contradicting requirements. In this work, we discuss security, user privacy and incentivization for this sensing paradigm, exploring how to address all aspects of this multifaceted problem. We critically survey the security and privacy properties of state-of-the-art research efforts in the area. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security and privacy do not hinder the deployment of PCS systems.

SEROSA: SERvice oriented security architecture for Vehicular Communications

Modern vehicles are no longer mere mechanical devices; they comprise dozens of digital computing platforms, coordinated by an in-vehicle network, and have the potential to significantly enhance the digital life of individuals on the road. While this transformation has driven major advancements in road safety and transportation efficiency, significant work remains to be done to support the security and privacy requirements of the envisioned ecosystem of commercial services and applications (i.e., Internet access, video streaming, etc.). In the era when “service is everything and everything is a service”, Vehicular Communication (VC) systems cannot escape from this ongoing trend towards multi-service environments accessible from anywhere. To meet the diverse requirements of vehicle operators and Service Providers (SPs), we present SEROSA, a service-oriented security and privacy-preserving architecture for VC. By synthesizing existing VC standards and Web Services (WS), our architecture provides comprehensive identity and service management while ensuring interoperability with existing SPs. We fully implement our system and extensively assess its efficiency, practicality, and dependability. Overall, SEROSA significantly extends the state of the art and serves as a catalyst for the integration of vehicles into the vast domain of Internet-based services.

Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our systems efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

Spy-Sense: spyware tool for executing stealthy exploits against sensor networks

As the domains of pervasive computing and sensor networking are expanding, a new era is emerging, enabling the design and proliferation of intelligent sensor-based applications. At the same time, it is important to maintain a high degree of confidentiality, integrity and availability of both the data and network resources. However, a common threat that is often overlooked in the design of secure sensor network applications is the existence of spyware programs. This work demonstrates Spy-Sense, a spyware tool that allows the injection of stealthy exploits in the nodes of a sensor network. Spy-Sense is hard to recognize and get rid of, and once activated, it runs discretely in the background without interfering or disrupting normal network operation. To the best of our knowledge, this is the first instance of a spyware program that can be used to attack the confidentiality and functionality of a sensor network.

Arbitrary Code Injection through Self-propagating Worms in Von Neumann Architecture Devices

Malicious code (or malware) is defined as a software designed to execute attacks on software systems and fulfill the harmful intents of an attacker. As lightweight embedded devices become more ubiquitous and increasingly networked, they present a new and very disturbing target for malware developers. In this paper, we demonstrate how to execute malware on wireless sensor nodes that are based on the Von Neumann architecture. We achieve this by exploiting a buffer overflow vulnerability to smash the call stack and intrude a remote node over the radio channel. By breaking the malware into multiple packets, the attacker can inject arbitrarily long malicious code to the node and completely take control of it. Then we proceed to show how the malware can be crafted to become a self-replicating worm that broadcasts itself and infects the network in a hop-by-hop manner. To our knowledge, this is the first instance of a self-propagating worm that provides a detailed analysis along with instructions in order to execute arbitrary malicious code. We also provide a complete implementation of our attack, measure its effectiveness in terms of time taken for the worm to propagate to the entire sensor network and, finally, suggest possible countermeasures.