Biblio

Drones are increasingly being used as mobile data collectors for various monitoring services. However, since they may move around in unattended hostile areas with valuable data, they can be the targets of malicious physical/cyber attacks. These attacks may aim at stealing privacy-sensitive data, including secret keys, and eavesdropping on communications between the drones and the ground station. To detect tampered drones, a code attestation technique is required. However, since attestation itself does not guarantee that the data in the drones' memory are not leaked, data collected by the drones must be protected and secret keys for secure communications must not be leaked. In this paper, we present a solution integrating techniques for software-based attestation, data encryption and secret key protection. We propose an attestation technique that fills up free memory spaces with data repositories. Data repositories consist of pseudo-random numbers that are also used to encrypt collected data. We also propose a group attestation scheme to efficiently verify the software integrity of multiple drones. Finally, to prevent secret keys from being leaked, we utilize a technique that converts short secret keys into large look-up tables. This technique prevents attackers from abusing free space in the data memory by filling up the space with the look-up tables. To evaluate the integrated solution, we implemented it on AR.Drone and Raspberry Pi.

Generative policies enable devices to generate their own policies that are validated, consistent and conflict free. This autonomy is required for security policy generation to deal with the large number of smart devices per person that will soon become reality. In this paper, we discuss the research issues that have to be addressed in order for devices involved in security enforcement to automatically generate their security policies - enabling policy-based autonomous security management. We discuss the challenges involved in the task of automatic security policy generation, and outline some approaches based om machine learning that may potentially provide a solution to the same.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (textless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- textbartextbar

Policies govern choices in the behavior of systems. They are applied to human behavior as well as to the behavior of autonomous systems but are defined differently in each case. Generally humans have the ability to interpret the intent behind the policies, to bring about their desired effects, even occasionally violating them when the need arises. In contrast, policies for automated systems fully define the prescribed behavior without ambiguity, conflicts or omissions. The increasing use of AI techniques and machine learning in autonomous systems such as drones promises to blur these boundaries and allows us to conceive in a similar way more flexible policies for the spectrum of human-autonomous systems collaborations. In coalition environments this spectrum extends across the boundaries of authority in pursuit of a common coalition goal and covers collaborations between human and autonomous systems alike. In social sciences, social exchange theory has been applied successfully to explain human behavior in a variety of contexts. It provides a framework linking the expected rewards, costs, satisfaction and commitment to explain and anticipate the choices that individuals make when confronted with various options. We discuss here how it can be used within coalition environments to explain joint decision making and to help formulate policies re-framing the concepts where appropriate. Social exchange theory is particularly attractive within this context as it provides a theory with “measurable” components that can be readily integrated in machine reasoning processes.

Assured Mission Delivery Network (AMDN) is a collaborative network to support data-intensive scientific collaborations in a multi-cloud environment. Each scientific collaboration group, called a mission, specifies a set of rules to handle computing and network resources. Security is an integral part of the AMDN design since the rules must be set by authorized users and the data generated by each mission may be privacy-sensitive. In this paper, we propose a CertificateLess cryptography-based Rule-management Protocol (CL-RP) for AMDN, which supports authenticated rule registrations and updates with non-repudiation. We evaluate CL-RP through test-bed experiments and compare it with other standard protocols.

We present a formal method for computing the best security provisioning for Internet of Things (IoT) scenarios characterized by a high degree of mobility. The security infrastructure is intended as a security resource allocation plan, computed as the solution of an optimization problem that minimizes the risk of having IoT devices not monitored by any resource. We employ the shortfall as a risk measure, a concept mostly used in the economics, and adapt it to our scenario. We show how to compute and evaluate an allocation plan, and how such security solutions address the continuous topology changes that affect an IoT environment.

The mitigation of insider threats against databases is a challenging problem as insiders often have legitimate access privileges to sensitive data. Therefore, conventional security mechanisms, such as authentication and access control, may be insufficient for the protection of databases against insider threats and need to be complemented with techniques that support real-time detection of access anomalies. The existing real-time anomaly detection techniques consider anomalies in references to the database entities and the amounts of accessed data. However, they are unable to track the access frequencies. According to recent security reports, an increase in the access frequency by an insider is an indicator of a potential data misuse and may be the result of malicious intents for stealing or corrupting the data. In this paper, we propose techniques for tracking users' access frequencies and detecting anomalous related activities in real-time. We present detailed algorithms for constructing accurate profiles that describe the access patterns of the database users and for matching subsequent accesses by these users to the profiles. Our methods report and log mismatches as anomalies that may need further investigation. We evaluated our techniques on the OLTP-Benchmark. The results of the evaluation indicate that our techniques are very effective in the detection of anomalies.

This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe that the three aspects addressed in this paper will form the basis for studying the Science of Cyber Security.

This paper introduces a research agenda focusing on cybersecurity in the context of product lifecycle management. The paper discusses research directions on critical protection techniques, including protection techniques from insider threat, access control systems, secure supply chains and remote 3D printing, compliance techniques, and secure collaboration techniques. The paper then presents an overview of DBSAFE, a system for protecting data from insider threat.

Detection of high risk network flows and high risk hosts is becoming ever more important and more challenging. In order to selectively apply deep packet inspection (DPI) one has to isolate in real time high risk network activities within a huge number of monitored network flows. To help address this problem, we propose an iterative methodology for a simultaneous assessment of risk scores for both hosts and network flows. The proposed approach measures the risk scores of hosts and flows in an interdependent manner; thus, the risk score of a flow influences the risk score of its source and destination hosts, and also the risk score of a host is evaluated by taking into account the risk scores of flows initiated by or terminated at the host. Our experimental results show that such an approach not only effective in detecting high risk hosts and flows but, when deployed in high throughput networks, is also more efficient than PageRank based algorithms.

In large-scale systems, user authentication usually needs the assistance from a remote central authentication server via networks. The authentication service however could be slow or unavailable due to natural disasters or various cyber attacks on communication channels. This has raised serious concerns in systems which need robust authentication in emergency situations. The contribution of this paper is two-fold. In a slow connection situation, we present a secure generic multi-factor authentication protocol to speed up the whole authentication process. Compared with another generic protocol in the literature, the new proposal provides the same function with significant improvements in computation and communication. Another authentication mechanism, which we name stand-alone authentication, can authenticate users when the connection to the central server is down. We investigate several issues in stand-alone authentication and show how to add it on multi-factor authentication protocols in an efficient and generic way.

Data is one of the most valuable assets for organization. It can facilitate users or organizations to meet their diverse goals, ranging from scientific advances to business intelligence. Due to the tremendous growth of data, the notion of big data has certainly gained momentum in recent years. Cloud computing is a key technology for storing, managing and analyzing big data. However, such large, complex, and growing data, typically collected from various data sources, such as sensors and social media, can often contain personally identifiable information (PII) and thus the organizations collecting the big data may want to protect their outsourced data from the cloud. In this paper, we survey our research towards development of efficient and effective privacy-enhancing (PE) techniques for management and analysis of big data in cloud computing.We propose our initial approaches to address two important PE applications: (i) privacy-preserving data management and (ii) privacy-preserving data analysis under the cloud environment. Additionally, we point out research issues that still need to be addressed to develop comprehensive solutions to the problem of effective and efficient privacy-preserving use of data.