Biblio

Sensors of diverse capabilities and modalities, carried by us or deeply embedded in the physical world, have invaded our personal, social, work, and urban spaces. Our relationship with these sensors is a complicated one. On the one hand, these sensors collect rich data that are shared and disseminated, often initiated by us, with a broad array of service providers, interest groups, friends, and family. Embedded in this data is information that can be used to algorithmically construct a virtual biography of our activities, revealing intimate behaviors and lifestyle patterns. On the other hand, we and the services we use, increasingly depend directly and indirectly on information originating from these sensors for making a variety of decisions, both routine and critical, in our lives. The quality of these decisions and our confidence in them depend directly on the quality of the sensory information and our trust in the sources. Sophisticated adversaries, benefiting from the same technology advances as the sensing systems, can manipulate sensory sources and analyze data in subtle ways to extract sensitive knowledge, cause erroneous inferences, and subvert decisions. The consequences of these compromises will only amplify as our society increasingly complex human-cyber-physical systems with increased reliance on sensory information and real-time decision cycles.Drawing upon examples of this two-faceted relationship with sensors in applications such as mobile health and sustainable buildings, this talk will discuss the challenges inherent in designing a sensor information flow and processing architecture that is sensitive to the concerns of both producers and consumer. For the pervasive sensing infrastructure to be trusted by both, it must be robust to active adversaries who are deceptively extracting private information, manipulating beliefs and subverting decisions. While completely solving these challenges would require a new science of resilient, secure and trustworthy networked sensing and decision systems that would combine hitherto disciplines of distributed embedded systems, network science, control theory, security, behavioral science, and game theory, this talk will provide some initial ideas. These include an approach to enabling privacy-utility trade-offs that balance the tension between risk of information sharing to the producer and the value of information sharing to the consumer, and method to secure systems against physical manipulation of sensed information.
 

Tracking moving objects is a task of the utmost importance to the defence community. As this task requires high accuracy, rather than employing a single detector, it has become common to use multiple ones. In such cases, the tracks produced by these detectors need to be correlated (if they belong to the same sensing modality) or associated (if they were produced by different sensing modalities). In this work, we introduce Computational-Intelligence-based methods for correlating and associating various contacts and tracks pertaining to maritime vessels in an area of interest. Fuzzy k-Nearest Neighbours will be used to conduct track correlation and Fuzzy C-Means clustering will be applied for association. In that way, the uncertainty of the track correlation and association is handled through fuzzy logic. To better model the state of the moving target, the traditional Kalman Filter will be extended using an Echo State Network. Experimental results on five different types of sensing systems will be discussed to justify the choices made in the development of our approach. In particular, we will demonstrate the judiciousness of using Fuzzy k-Nearest Neighbours and Fuzzy C-Means on our tracking system and show how the extension of the traditional Kalman Filter by a recurrent neural network is superior to its extension by other methods.

Participatory sensing tries to create cost-effective, large-scale sensing systems by leveraging sensors embedded in mobile devices. One major challenge in these systems is to protect the users' privacy, since users will not contribute data if their privacy is jeopardized. Especially location data needs to be protected if it is likely to reveal information about the users' identities. A common solution is the blinding out approach that creates so-called ban zones in which location data is not published. Thereby, a user's important places, e.g., her home or workplace, can be concealed. However, ban zones of a fixed size are not able to guarantee any particular level of privacy. For instance, a ban zone that is large enough to conceal a user's home in a large city might be too small in a less populated area. For this reason, we propose an approach for dynamic map-based blinding out: The boundaries of our privacy zones, called Silent Zones, are determined in such way that at least k buildings are located within this zone. Thus, our approach adapts to the habitat density and we can guarantee k-anonymity in terms of surrounding buildings. In this paper, we present two new algorithms for creating Silent Zones and evaluate their performance. Our results show that especially in worst case scenarios, i.e., in sparsely populated areas, our approach outperforms standard ban zones and guarantees the specified privacy level.

In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. A typical WSN topology that applies to most applications allows sensors to act as data sources that forward their measurements to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN. An adversary may employ traffic analysis techniques such as evidence theory to identify the BS based on network traffic flow even when the WSN implements conventional security mechanisms. This motivates a need for WSN operators to achieve improved BS anonymity to protect the identity, role, and location of the BS. Many traffic analysis countermeasures have been proposed in literature, but are typically evaluated based on data traffic only, without considering the effects of network synchronization on anonymity performance. In this paper we use evidence theory analysis to examine the effects of WSN synchronization on BS anonymity by studying two commonly used protocols, Reference Broadcast Synchronization (RBS) and Timing-synch Protocol for Sensor Networks (TPSN).

In this paper we introduce PADAVAN, a novel anonymous data collection scheme for Vehicular Ad Hoc Networks (VANETs). PADAVAN allows users to submit data anonymously to a data consumer while preventing adversaries from submitting large amounts of bogus data. PADAVAN is comprised of an n-times anonymous authentication scheme, mix cascades and various principles to protect the privacy of the submitted data itself. Furthermore, we evaluate the effectiveness of limiting an adversary to a fixed amount of messages.

In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial automation and product tracking to intrusion detection at a hostile border. A typical WSN topology allows sensors to act as data sources that forward their measurements to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN. An adversary may employ traffic analysis techniques to identify the BS based on network traffic flow even when the WSN implements conventional security mechanisms. This motivates a need for WSN operators to achieve improved BS anonymity to protect the identity, role, and location of the BS. Although a variety of countermeasures have been proposed to improve BS anonymity, those techniques are typically evaluated based on a WSN that does not employ acknowledgements. In this paper we propose an enhanced evidence theory metric called Acknowledgement-Aware Evidence Theory (AAET) that more accurately characterizes BS anonymity in WSNs employing acknowledgements. We demonstrate AAET's improved robustness to a variety of configurations through simulation.

Random numbers represent one of the most sensible part of a cryptographic system, since the cryptographic keys must be entirely based on them. The security of a communication relies on the key that had been established between two users. If an attacker is able to deduce that key, the communication is compromised. This is why key generation must completely rely on random number generators, so that nobody can deduce the. This paper will describe a set of public and free Random Number Generators (RNG) within Android-based Smartphones by exploiting different sensors, along with the way of achieving this scope. Moreover, this paper will present some conclusive tests and results over them.
 

In the field of scene understanding, researchers have mainly focused on using video/images to extract different elements in a scene. The computational as well as monetary cost associated with such implementations is high. This paper proposes a low-cost system which uses sound-based techniques in order to jointly perform localization as well as fingerprinting of the sound sources. A network of embedded nodes is used to sense the sound inputs. Phase-based sound localization and Support-Vector Machine classification are used to locate and classify elements of the scene, respectively. The fusion of all this data presents a complete “picture” of the scene. The proposed concepts are applied to a vehicular-traffic case study. Experiments show that the system has a fingerprinting accuracy of up to 97.5%, localization error less than 4 degrees and scene prediction accuracy of 100%.

In this paper we address the problem of designing a fault tolerant control scheme for an HVAC control system where sensing and actuation data are exchanged with a centralized controller via a wireless sensors and actuators network where the communication nodes are subject to permanent failures and malicious intrusions.

Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model measures the potential impact of a threat on assets given its occurrence probability. The attacks prediction model was integrated with our autonomous cloud intrusion detection framework (ACIDF) to raise early warnings about attacks to the controller so it can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments on DARPA 2000 dataset, the proposed prediction model has successfully fired the early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the auto response controller ample time to take preventive measures.

Sensors of diverse capabilities and modalities, carried by us or deeply embedded in the physical world, have invaded our personal, social, work, and urban spaces. Our relationship with these sensors is a complicated one. On the one hand, these sensors collect rich data that are shared and disseminated, often initiated by us, with a broad array of service providers, interest groups, friends, and family. Embedded in this data is information that can be used to algorithmically construct a virtual biography of our activities, revealing intimate behaviors and lifestyle patterns. On the other hand, we and the services we use, increasingly depend directly and indirectly on information originating from these sensors for making a variety of decisions, both routine and critical, in our lives. The quality of these decisions and our confidence in them depend directly on the quality of the sensory information and our trust in the sources. Sophisticated adversaries, benefiting from the same technology advances as the sensing systems, can manipulate sensory sources and analyze data in subtle ways to extract sensitive knowledge, cause erroneous inferences, and subvert decisions. The consequences of these compromises will only amplify as our society increasingly complex human-cyber-physical systems with increased reliance on sensory information and real-time decision cycles.Drawing upon examples of this two-faceted relationship with sensors in applications such as mobile health and sustainable buildings, this talk will discuss the challenges inherent in designing a sensor information flow and processing architecture that is sensitive to the concerns of both producers and consumer. For the pervasive sensing infrastructure to be trusted by both, it must be robust to active adversaries who are deceptively extracting private information, manipulating beliefs and subverting decisions. While completely solving these challenges would require a new science of resilient, secure and trustworthy networked sensing and decision systems that would combine hitherto disciplines of distributed embedded systems, network science, control theory, security, behavioral science, and game theory, this talk will provide some initial ideas. These include an approach to enabling privacy-utility trade-offs that balance the tension between risk of information sharing to the producer and the value of information sharing to the consumer, and method to secure systems against physical manipulation of sensed information.

Modern military forces are enabled by networked command and control systems, which provide an important interface between the cyber environment, electronic sensors and decision makers. However these systems are vulnerable to cyber attack. A successful cyber attack could compromise data within the system, leading to incorrect information being utilized for decisions with potentially catastrophic results on the battlefield. Degrading the utility of a system or the trust a decision maker has in their virtual display may not be the most effective means of employing offensive cyber effects. The coordination of cyber and kinetic effects is proposed as the optimal strategy for neutralizing an adversary's C4ISR advantage. However, such an approach is an opportunity cost and resource intensive. The adversary's cyber dependence can be leveraged as a means of gaining tactical and operational advantage in combat, if a military force is sufficiently trained and prepared to attack the entire information network. This paper proposes a research approach intended to broaden the understanding of the relationship between command and control systems and the human decision maker, as an interface for both cyber and kinetic deception activity.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.

We consider the estimation of a scalar state based on m measurements that can be potentially manipulated by an adversary. The attacker is assumed to have full knowledge about the true value of the state to be estimated and about the value of all the measurements. However, the attacker has limited resources and can only manipulate up to l of the m measurements. The problem is formulated as a minimax optimization, where one seeks to construct an optimal estimator that minimizes the “worst-case” expected cost against all possible manipulations by the attacker. We show that if the attacker can manipulate at least half the measurements (l ≥ m/2), then the optimal worst-case estimator should ignore all measurements and be based solely on the a-priori information. We provide the explicit form of the optimal estimator when the attacker can manipulate less than half the measurements (l <; m/2), which is based on (m2l) local estimators. We further prove that such an estimator can be reduced into simpler forms for two special cases, i.e., either the estimator is symmetric and monotone or m = 2l + 1. Finally we apply the proposed methodology in the case of Gaussian measurements.

Distributed wireless sensor network technologies have become one of the major research areas in healthcare industries due to rapid maturity in improving the quality of life. Medical Wireless Sensor Network (MWSN) via continuous monitoring of vital health parameters over a long period of time can enable physicians to make more accurate diagnosis and provide better treatment. The MWSNs provide the options for flexibilities and cost saving to patients and healthcare industries. Medical data sensors on patients produce an increasingly large volume of increasingly diverse real-time data. The transmission of this data through hospital wireless networks becomes a crucial problem, because the health information of an individual is highly sensitive. It must be kept private and secure. In this paper, we propose a security model to protect the transfer of medical data in hospitals using MWSNs. We propose Compressed Sensing + Encryption as a strategy to achieve low-energy secure data transmission in sensor networks.

A distributed cyber control system comprises various types of assets, including sensors, intrusion detection systems, scanners, controllers, and actuators. The modeling and analysis of these components usually require multi-disciplinary approaches. This paper presents a modeling and dynamic analysis of a distributed cyber control system for situational awareness by taking advantage of control theory and time Petri net. Linear time-invariant systems are used to model the target system, attacks, assets influences, and an anomaly-based intrusion detection system. Time Petri nets are used to model the impact and timing relationships of attacks, vulnerability, and recovery at every node. To characterize those distributed control systems that are perfectly attackable, algebraic and topological attackability conditions are derived. Numerical evaluation is performed to determine the impact of attacks on distributed control system.

Nowadays, our surrounding environment is more and more scattered with various types of sensors. Due to their intrinsic properties and representation formats, they form small islands isolated from each other. In order to increase interoperability and release their full capabilities, we propose to represent devices descriptions including data and service invocation with a common model allowing to compose mashups of heterogeneous sensors. Pushing this paradigm further, we also propose to augment service descriptions with a discovery protocol easing automatic assimilation of knowledge. In this work, we describe the architecture supporting what can be called a Semantic Sensor Web-of-Things. As proof of concept, we apply our proposal to the domain of smart buildings, composing a novel ontology covering heterogeneous sensing, actuation and service invocation. Our architecture also emphasizes on the energetic aspect and is optimized for constrained environments.

In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.
 

The security of Smart Grid, being one of the very important aspects of the Smart Grid system, is studied in this paper. We first discuss different pitfalls in the security of the Smart Grid system considering the communication infrastructure among the sensors, actuators, and control systems. Following that, we derive a mathematical model of the system and propose a robust security framework for power grid. To effectively estimate the variables of a wide range of state processes in the model, we adopt Kalman Filter in the framework. The Kalman Filter estimates and system readings are then fed into the χ2-square detectors and the proposed Euclidean detectors, which can detect various attacks and faults in the power system including False Data Injection Attacks. The χ2-detector is a proven-effective exploratory method used with Kalman Filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks such as replay and DoS attacks. However, the study shows that the χ2-detector detectors are unable to detect statistically derived False Data Injection Attacks while the Euclidean distance metrics can identify such sophisticated injection attacks.
 

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.

We investigate the coverage efficiency of a sensor network consisting of sensors with circular sensing footprints of different radii. The objective is to completely cover a region in an efficient manner through a controlled (or deterministic) deployment of such sensors. In particular, it is shown that when sensing nodes of two different radii are used for complete coverage, the coverage density is increased, and the sensing cost is significantly reduced as compared to the homogeneous case, in which all nodes have the same sensing radius. Configurations of heterogeneous disks of multiple radii to achieve efficient circle coverings are presented and analyzed.