Biblio

Physical layer security can ensure secure communication over noisy channels in the presence of an eavesdropper with unlimited computational power. We adopt an information theoretic variant of semantic-security (SS) (a cryptographic gold standard), as our secrecy metric and study the open problem of the type II wiretap channel (WTC II) with a noisy main channel is, whose secrecy-capacity is unknown even under looser metrics than SS. Herein the secrecy-capacity is derived and shown to be equal to its SS capacity. In this setting, the legitimate users communicate via a discrete-memory less (DM) channel in the presence of an eavesdropper that has perfect access to a subset of its choosing of the transmitted symbols, constrained to a fixed fraction of the block length. The secrecy criterion is achieved simultaneously for all possible eavesdropper subset choices. On top of that, SS requires negligible mutual information between the message and the eavesdropper's observations even when maximized over all message distributions. A key tool for the achievability proof is a novel and stronger version of Wyner's soft covering lemma. Specifically, the lemma shows that a random codebook achieves the soft-covering phenomenon with high probability. The probability of failure is doubly-exponentially small in the block length. Since the combined number of messages and subsets grows only exponentially with the block length, SS for the WTC II is established by using the union bound and invoking the stronger soft-covering lemma. The direct proof shows that rates up to the weak-secrecy capacity of the classic WTC with a DM erasure channel (EC) to the eavesdropper are achievable. The converse follows by establishing the capacity of this DM wiretap EC as an upper bound for the WTC II. From a broader perspective, the stronger soft-covering lemma constitutes a tool for showing the existence of codebooks that satisfy exponentially many constraints, a beneficial ability for many other applications in information theoretic security.

The usage of Information and Communication Technologies (ICTs) pervades everyday's life. If it is true that ICT contributed to improve the quality of our life, it is also true that new forms of (cyber)crime have emerged in this setting. The diversity and amount of information forensic investigators need to cope with, when tackling a cyber-crime case, call for tools and techniques where knowledge is the main actor. Current approaches leave to the investigator the chore of integrating the diverse sources of evidence relevant for a case thus hindering the automatic generation of reusable knowledge. This paper describes an architecture that lifts the classical phases of a digital forensic investigation to a knowledge-driven setting. We discuss how the usage of languages and technologies originating from the Semantic Web proposal can complement digital forensics tools so that knowledge becomes a first-class citizen. Our architecture enables to perform in an integrated way complex forensic investigations and, as a by-product, build a knowledge base that can be consulted to gain insights from previous cases. Our proposal has been inspired by real-world scenarios emerging in the context of an Italian research project about cyber security.

Mathematical formulae are essential in science, but face challenges of ambiguity, due to the use of a small number of identifiers to represent an immense number of concepts. Corresponding to word sense disambiguation in Natural Language Processing, we disambiguate mathematical identifiers. By regarding formulae and natural text as one monolithic information source, we are able to extract the semantics of identifiers in a process we term Mathematical Language Processing (MLP). As scientific communities tend to establish standard (identifier) notations, we use the document domain to infer the actual meaning of an identifier. Therefore, we adapt the software development concept of namespaces to mathematical notation. Thus, we learn namespace definitions by clustering the MLP results and mapping those clusters to subject classification schemata. In addition, this gives fundamental insights into the usage of mathematical notations in science, technology, engineering and mathematics. Our gold standard based evaluation shows that MLP extracts relevant identifier-definitions. Moreover, we discover that identifier namespaces improve the performance of automated identifier-definition extraction, and elevate it to a level that cannot be achieved within the document context alone.

Utility computing is being gradually realized as exemplified by cloud computing. Outsourcing computing and storage to global-scale cloud providers benefits from high accessibility, flexibility, scalability, and cost-effectiveness. However, users are uneasy outsourcing the storage of sensitive data due to security concerns. We address this problem by presenting SeMiNAS–-an efficient middleware system that allows files to be securely outsourced to providers and shared among geo-distributed offices. SeMiNAS achieves end-to-end data integrity and confidentiality with a highly efficient authenticated-encryption scheme. SeMiNAS leverages advanced NFSv4 features, including compound procedures and data-integrity extensions, to minimize extra network round trips caused by security meta-data. SeMiNAS also caches remote files locally to reduce accesses to providers over WANs. We designed, implemented, and evaluated SeMiNAS, which demonstrates a small performance penalty of less than 26% and an occasional performance boost of up to 19% for Filebench workloads.

NDN is a promising protocol that can help to reduce congestion at Internet scale by putting content at the center of communications instead of hosts, and by providing each node with a caching capability. NDN can also natively authenticate transmitted content with a mechanism similar to website certificates that allows clients to assess the original provider. But this security feature comes at a high cost, as it relies heavily on asymmetric cryptography which affects server performance when NDN Data are generated. This is particularly critical for many services dealing with real-time data (VOIP, live streaming, etc.), but current tools are not adapted for a realistic server-side performance evaluation of NDN traffic generation when digital signature is used. We propose a new tool, NDNperf, to perform this evaluation and show that creating NDN packets is a major bottleneck of application performances. On our testbed, 14 server cores only generate \textbackslashtextasciitilde400 Mbps of new NDN Data with default packet settings. We propose and evaluate practical solutions to improve the performance of server-side NDN Data generation leading to significant gains.

Preventive and reactive security measures can only partially mitigate the damage caused by modern ransomware attacks. Indeed, the remarkable amount of illicit profit and the cyber-criminals' increasing interest in ransomware schemes suggest that a fair number of users are actually paying the ransoms. Unfortunately, pure-detection approaches (e.g., based on analysis sandboxes or pipelines) are not sufficient nowadays, because often we do not have the luxury of being able to isolate a sample to analyze, and when this happens it is already too late for several users! We believe that a forward-looking solution is to equip modern operating systems with practical self-healing capabilities against this serious threat. Towards such a vision, we propose ShieldFS, an add-on driver that makes the Windows native filesystem immune to ransomware attacks. For each running process, ShieldFS dynamically toggles a protection layer that acts as a copy-on-write mechanism, according to the outcome of its detection component. Internally, ShieldFS monitors the low-level filesystem activity to update a set of adaptive models that profile the system activity over time. Whenever one or more processes violate these models, their operations are deemed malicious and the side effects on the filesystem are transparently rolled back. We designed ShieldFS after an analysis of billions of low-level, I/O filesystem requests generated by thousands of benign applications, which we collected from clean machines in use by real users for about one month. This is the first measurement on the filesystem activity of a large set of benign applications in real working conditions. We evaluated ShieldFS in real-world working conditions on real, personal machines, against samples from state of the art ransomware families. ShieldFS was able to detect the malicious activity at runtime and transparently recover all the original files. Although the models can be tuned to fit various filesystem usage profiles, our results show that our initial tuning yields high accuracy even on unseen samples and variants.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

We introduce a system-level Simulation and Analysis Engine (SAE) framework based on dynamic binary instrumentation for fine-grained and customizable instruction-level introspection of everything that executes on the processor. SAE can instrument the BIOS, kernel, drivers, and user processes. It can also instrument multiple systems simultaneously using a single instrumentation interface, which is essential for studying scale-out applications. SAE is an x86 instruction set simulator designed specifically to enable rapid prototyping, evaluation, and validation of architectural extensions and program analysis tools using its flexible APIs. It is fast enough to execute full platform workloads–-a modern operating system can boot in a few minutes–-thus enabling research, evaluation, and validation of complex functionalities related to multicore configurations, virtualization, security, and more. To reach high speeds, SAE couples tightly with a virtual platform and employs both a just-in-time (JIT) compiler that helps simulate simple instructions efficiently and a fast interpreter for simulating new or complex instructions. We describe SAE's architecture and instrumentation engine design and show the framework's usefulness for single- and multi-system architectural and program analysis studies.

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

Recently, wireless home routers increasingly become smart. While these smart routers provide rich functionalities to users, they also raise security concerns. Since a smart home router may process and store personal data for users, once compromised, these sensitive information will be exposed. Unfortunately, current operating systems on home routers are far from secure. As a consequence, users are facing a difficult tradeoff between functionality and privacy risks. This paper attacks this dilemma with a novel SEAL architecture for home routers. SEAL leverages the ARM TrustZone technology to divide a conventional router OS (i.e., Linux) in a non-secure/normal world. All sensitive user data are shielded from the normal world using encryption. Modules (called applets) that process the sensitive data are located in a secure world and confined in secure sandboxes provided by a tiny secure OS. We report the system design of SEAL and our preliminary implementation and evaluation results.

Small sized unmanned aerial vehicles (UAV) play major roles in variety of applications for aerial explorations and surveillance, transport, videography/photography and other areas. However, some other real life applications of UAV have also been studied. One of them is as a 'Disaster Response' component. In a post disaster situation, the UAVs can be used for search and rescue, damage assessment, rapid response and other emergency operations. However, in a disaster response situation it is very challenging to predict whether the climatic conditions are suitable to fly the UAV. Also it is necessary for an efficient dynamic path planning technique for effective damage assessment. In this paper, such dynamic path planning algorithms have been proposed for micro-jet, a small sized fixed wing UAV for data collection and dissemination in a post disaster situation. The proposed algorithms have been implemented on paparazziUAV simulator considering different environment simulators (wind speed, wind direction etc.) and calibration parameters of UAV like battery level, flight duration etc. The results have been obtained and compared with baseline algorithm used in paparazziUAV simulator for navigation. It has been observed that, the proposed navigation techniques work well in terms of different calibration parameters (flight duration, battery level) and can be effective not only for shelter point detection but also to reserve battery level, flight time for micro-jet in a post disaster scenario. The proposed techniques take approximately 20% less time and consume approximately 19% less battery power than baseline navigation technique. From analysis of produced results, it has been observed that the proposed work can be helpful for estimating the feasibility of flying UAV in a disaster response situation. Finally, the proposed path planning techniques have been carried out during field test using a micro-jet. It has been observed that, our proposed dynamic path planning algorithms give proximate results compare to simulation in terms of flight duration and battery level consumption.

The explosion in Internet-connected household devices, such as light-bulbs, smoke-alarms, power-switches, and webcams, is creating new vectors for attacking "smart-homes" at an unprecedented scale. Common perception is that smart-home IoT devices are protected from Internet attacks by the perimeter security offered by home routers. In this paper we demonstrate how an attacker can infiltrate the home network via a doctored smart-phone app. Unbeknownst to the user, this app scouts for vulnerable IoT devices within the home, reports them to an external entity, and modifies the firewall to allow the external entity to directly attack the IoT device. The ability to infiltrate smart-homes via doctored smart-phone apps demonstrates that home routers are poor protection against Internet attacks and highlights the need for increased security for IoT devices.

This paper presents general techniques for verifying virtually synchronous distributed control systems with interconnected physical environments. Such cyber-physical systems (CPSs) are notoriously hard to verify, due to their combination of nontrivial continuous dynamics, network delays, imprecise local clocks, asynchronous communication, etc. To simplify their analysis, we first extend the PALS methodology–-that allows to abstract from the timing of events, asynchronous communication, network delays, and imprecise clocks, as long as the infrastructure guarantees bounds on the network delays and clock skews–-from real-time to hybrid systems. We prove a bisimulation equivalence between Hybrid PALS synchronous and asynchronous models. We then show how various verification problems for synchronous Hybrid PALS models can be reduced to SMT solving over nonlinear theories of the real numbers. We illustrate the Hybrid PALS modeling and verification methodology on a number of CPSs, including a control system for turning an airplane.

We introduce a scalable observer architecture to estimate the states of a discrete-time linear-time-invariant (LTI) system whose sensors can be manipulated by an attacker. Given the maximum number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel multi-modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. We provide proofs of convergence for our algorithm and report simulation results to compare its runtime performance with alternative techniques. Our algorithm scales well for large systems (including up to 5000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our algorithm on the design of resilient power distribution systems.

Social Engineering is a kind of advance persistent threat (APT) that gains private and sensitive information through social networks or other types of communication. The attackers can use social engineering to obtain access into social network accounts and stays there undetected for a long period of time. The purpose of the attack is to steal sensitive data and spread false information rather than to cause direct damage. Such targets can include Facebook accounts of government agencies, corporations, schools or high-profile users. We propose to use IDS, Intrusion Detection System, to battle such attacks. What the social engineering does is try to gain easy access, so that the attacks can be repeated and ongoing. The focus of this study is to find out how this type of attacks are carried out so that they can properly detected by IDS in future research.

Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem. In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of SQLi attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from SQLi attack statements. We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real SQLi vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100% (i.e., missing no attacks) with a low false positive rate (0.6%).

Electrical substations are crucial for power grids. A number of international standards, such as IEC 60870 and 61850, have emerged to enable remote and automated control over substations. However, owing to insufficient security consideration in their design and implementation, the resulting systems could be vulnerable to cyber attacks. As a result, the modernization of a large number of substations dramatically increases the scale of potential damage successful attacks can cause on power grids. To counter such a risk, one promising direction is to design and deploy an additional layer of defense at the substations. However, it remains a challenge to evaluate various substation cybersecurity solutions in a realistic environment. In this paper, we present the design and implementation of SoftGrid, a software-based smart grid testbed for evaluating the effectiveness, performance, and interoperability of various security solutions implemented to protect the remote control interface of substations. We demonstrate the capability and usefulness of SoftGrid through a concrete case study. We plan to open-source SoftGrid to facilitate security research in related areas.

Recommender systems have become quite popular recently. However, such systems are vulnerable to several types of attacks that target user ratings. One such attack is the Sybil attack where an entity masquerades as several identities with the intention of diverting user ratings. In this work, we propose evolutionary game theory as a possible solution to the Sybil attack in recommender systems. After modeling the attack, we use replicator dynamics to solve for evolutionary stable strategies. Our results show that under certain conditions that are easily achievable by a system administrator, the probability of an attack strategy drops to zero implying degraded fitness for Sybil nodes that eventually die out.

Global Positioning System (GPS) is used ubiquitously in a wide variety of applications ranging from navigation and tracking to modern smart grids and communication networks. However, it has been demonstrated that modern GPS receivers are vulnerable to signal spoofing attacks. For example, today it is possible to change the course of a ship or force a drone to land in a hostile area by simply spoofing GPS signals. Several countermeasures have been proposed in the past to detect GPS spoofing attacks. These counter-measures offer protection only against naive attackers. They are incapable of detecting strong attackers such as those capable of seamlessly taking over a GPS receiver, which is currently receiving legitimate satellite signals, and spoofing them to an arbitrary location. Also, there is no hardware platform that can be used to compare and evaluate the effectiveness of existing countermeasures in real-world scenarios. In this work, we present SPREE, which is, to the best of our knowledge, the first GPS receiver capable of detecting all spoofing attacks described in the literature. Our novel spoofing detection technique called auxiliary peak tracking enables detection of even a strong attacker capable of executing the seamless takeover attack. We implement and evaluate our receiver against three different sets of GPS signal traces: (i) a public repository of spoofing traces, (ii) signals collected through our own wardriving effort and (iii) using commercial GPS signal generators. Our evaluations show that SPREE constraints even a strong attacker (capable of seamless takeover attack) from spoofing the receiver to a location not more than 1 km away from its true location. This is a significant improvement over modern GPS receivers that can be spoofed to any arbitrary location. Finally, we release our implementation and datasets to the community for further research and development.

We study the problem of estimating distinct elements in the data stream model, which has a central role in traffic monitoring, query optimization, data mining and data integration. Different from all previous work, we study the problem in the noisy data setting, where two different looking items in the stream may reference the same entity (determined by a distance function and a threshold value), and the goal is to estimate the number of distinct entities in the stream. In this paper, we formalize the problem of robust distinct elements, and develop space and time-efficient streaming algorithms for datasets in the Euclidean space, using a novel technique we call bucket sampling. We also extend our algorithmic framework to other metric spaces by establishing a connection between bucket sampling and the theory of locality sensitive hashing. Moreover, we formally prove that our algorithms are still effective under small distinct elements ambiguity. Our experiments demonstrate the practicality of our algorithms.