Biblio

Embedded systems must address a multitude of potentially conflicting design constraints such as resiliency, energy, heat, cost, performance, security, etc., all in the face of highly dynamic operational behaviors and environmental conditions. By incorporating elements of intelligence, the hope is that the resulting “smart” embedded systems will function correctly and within desired constraints in spite of highly dynamic changes in the applications and the environment, as well as in the underlying software/hardware platforms. Since terms related to “smartness” (e.g., self-awareness, self-adaptivity, and autonomy) have been used loosely in many software and hardware computing contexts, we first present a taxonomy of “self-x” terms and use this taxonomy to relate major “smart” software and hardware computing efforts. A major attribute for smart embedded systems is the notion of self-awareness that enables an embedded system to monitor its own state and behavior, as well as the external environment, so as to adapt intelligently. Toward this end, we use a System-on-Chip perspective to show how the CyberPhysical System-on-Chip (CPSoC) exemplar platform achieves self-awareness through a combination of cross-layer sensing, actuation, self-aware adaptations, and online learning. We conclude with some thoughts on open challenges and research directions.

Connectivity is at the heart of the future Internet-of-Things (IoT) infrastructure, which can control and communicate with remote sensors and actuators for the beacons, data collections, and forwarding nodes. Existing sensor network solutions cannot solve the bottleneck problems near the sink node; the tree-based Internet architecture has the single point of failure. To solve current deficiencies in multi-hop mesh network and cross-domain network design, we propose a mesh inside a mesh IoT network architecture. Our designed "edge router" incorporates these two mesh networks together and performs seamlessly transmission of multi-standard packets. The proposed IoT testbed interoperates with existing multi-standards (Wi-Fi, 6LoWPAN) and segments of networks, and provides both high-throughput Internet and resilient sensor coverage throughout the community.

This paper proposes a taxonomy of autonomous vehicle handover situations with a particular emphasis on situational awareness. It focuses on a number of research challenges such as: legal responsibility, the situational awareness level of the driver and the vehicle, the knowledge the vehicle must have of the driver's driving skills as well as the in-vehicle context. The taxonomy acts as a starting point for researchers and practitioners to frame the discussion on this complex problem.

In the near future, billions of new smart devices will connect the big network of the Internet of Things, playing an important key role in our daily life. Allowing IPv6 on the low-power resource constrained devices will lead research to focus on novel approaches that aim to improve the efficiency, security and performance of the 6LoWPAN adaptation layer. This work in progress paper proposes a hardware-based Network Packet Filtering (NPF) and an IPv6 Link-local address calculator which is able to filter the received IPv6 packets, offering nearly 18% overhead reduction. The goal is to obtain a System-on-Chip implementation that can be deployed in future IEEE 802.15.4 radio modules.

Agile methodologies are becoming widespread in modern software development. However, due to a lack of safety assurance activities, agile methods are criticized for being inadequate for the development of safe software. Safety analysis and safety verification are complementary methods for safety assurance. Yet, both usually rely on traditional, waterfall-like processes. Therefore, it is strongly needed to integrate an appropriate safety analysis approach into agile software development processes driving architecture design and verify the safe design at the code level. This paper presents a novel agile process model "S-Scrum" based on the existing development process "Safe Scrum" and extended by a safety analysis method and a safety verification approach based on STPA (System-Theoretic Process Analysis). The proposed agile development process S-Scrum can be separated into three parts: (1) performing safety-guided design by STPA inside each sprint. (2) Verifying safety requirements at the code level by using model checking. (3) Replacing traditional RAMS (Reliability, Availability, Maintainability, Safety) validation on the final product by STPA safety analysis. We adopt other aspects from the original Safe Scrum. Finally, the feasibility of S-Scrum is illustrated with the example of an airbag system.

There are currently no requirements (technical or otherwise) that routing paths must be contained within national boundaries. Indeed, some paths experience international detours, i.e., originate in one country, cross international boundaries and return to the same country. In most cases these are sensible traffic engineering or peering decisions at ISPs that serve multiple countries. In some cases such detours may be suspicious. Characterizing international detours is useful to a number of players: (a) network engineers trying to diagnose persistent problems, (b) policy makers aiming at adhering to certain national communication policies, (c) entrepreneurs looking for opportunities to deploy new networks, or (d) privacy-conscious states trying to minimize the amount of internal communication traversing different jurisdictions. In this paper we characterize international detours in the Internet during the month of January 2016. To detect detours we sample BGP RIBs every 8 hours from 461 RouteViews and RIPE RIS peers spanning 30 countries. We use geolocation of ASes which geolocates each BGP prefix announced by each AS, mapping its presence at IXPs and geolocation infrastructure IPs. Finally, we analyze each global BGP RIB entry looking for detours. Our analysis shows more than 5K unique BGP prefixes experienced a detour. 132 prefixes experienced more than 50% of the detours. We observe about 544K detours. Detours either last for a few days or persist the entire month. Out of all the detours, more than 90% were transient detours that lasted for 72 hours or less. We also show different countries experience different characteristics of detours.

Traditionally, network and system configurations are static. Attackers have plenty of time to exploit the system's vulnerabilities and thus they are able to choose when to launch attacks wisely to maximize the damage. An unpredictable system configuration can significantly lift the bar for attackers to conduct successful attacks. Recent years, moving target defense (MTD) has been advocated for this purpose. An MTD mechanism aims to introduce dynamics to the system through changing its configuration continuously over time, which we call adaptations. Though promising, the dynamic system reconfiguration introduces overhead to the applications currently running in the system. It is critical to determine the right time to conduct adaptations and to balance the overhead afforded and the security levels guaranteed. This problem is known as the MTD timing problem. Little prior work has been done to investigate the right time in making adaptations. In this paper, we take the first step to both theoretically and experimentally study the timing problem in moving target defenses. For a broad family of attacks including DDoS attacks and cloud covert channel attacks, we model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to make adaptations with the objective of minimizing the long-term cost rate. In our experiments, both DDoS attacks and cloud covert channel attacks are studied. Simulations based on real network traffic traces are conducted and we demonstrate that our proposed algorithm outperforms known adaptation schemes.

The safety-critical aspects of cyber-physical systems motivate the need for rigorous analysis of these systems. In the literature this work is often done using idealized models of systems where the analysis can be carried out using high-level reasoning techniques such as Lyapunov functions and model checking. In this paper we present VERIDRONE, a foundational framework for reasoning about cyber-physical systems at all levels from high-level models to C code that implements the system. VERIDRONE is a library within the Coq proof assistant enabling us to build on its foundational implementation, its interactive development environments, and its wealth of libraries capturing interesting theories ranging from real numbers and differential equations to verified compilers and floating point numbers. These features make proof assistants in general, and Coq in particular, a powerful platform for unifying foundational results about safety-critical systems and ensuring interesting properties at all levels of the stack.

The safety-critical aspects of cyber-physical systems motivate the need for rigorous analysis of these systems. In the literature this work is often done using idealized models of systems where the analysis can be carried out using high-level reasoning techniques such as Lyapunov functions and model checking. In this paper we present VERIDRONE, a foundational framework for reasoning about cyber-physical systems at all levels from high-level models to C code that implements the system. VERIDRONE is a library within the Coq proof assistant enabling us to build on its foundational implementation, its interactive development environments, and its wealth of libraries capturing interesting theories ranging from real numbers and differential equations to verified compilers and floating point numbers. These features make proof assistants in general, and Coq in particular, a powerful platform for unifying foundational results about safety-critical systems and ensuring interesting properties at all levels of the stack.
 

Opportunistic Situation Identification (OSI) is new paradigms for situation-aware systems, in which contexts for situation identification are sensed through sensors that happen to be available rather than pre-deployed and application-specific ones. OSI extends the application usage scale and reduces system costs. However, designing and implementing OSI module of situation-aware systems encounters several challenges, including the uncertainty of context availability, vulnerable network connectivity and privacy threat. This paper proposes a novel middleware framework to tackle such challenges, and its intuition is that it facilitates performing the situation reasoning locally on a smartphone without needing to rely on the cloud, thus reducing the dependency on the network and being more privacy-preserving. To realize such intuitions, we propose a hybrid learning approach to maximize the reasoning accuracy using limited phone's storage space, with the combination of two the-state-the-art techniques. Specifically, this paper provides a genetic algorithm based optimization approach to determine which pre-computed models will be selected for storage under the storage constraints. Validation of the approach based on an open dataset indicates that the proposed approach achieves higher accuracy with comparatively small storage cost. Further, the proposed utility function for model selection performs better than three baseline utility functions.

In this work, we address the problem of designing and implementing honeypots for Industrial Control Systems (ICS). Honeypots are vulnerable systems that are set up with the intent to be probed and compromised by attackers. Analysis of those attacks then allows the defender to learn about novel attacks and general strategy of the attacker. Honeypots for ICS systems need to satisfy both traditional ICT requirements, such as cost and maintainability, and more specific ICS requirements, such as time and determinism. We propose the design of a virtual, high-interaction and server-based ICS honeypot to satisfy the requirements, and the deployment of a realistic, cost-effective, and maintainable ICS honeypot. An attacker model is introduced to complete the problem statement and requirements. Based on our design and the MiniCPS framework, we implemented a honeypot mimicking a water treatment testbed. To the best of our knowledge, the presented honeypot implementation is the first academic work targeting Ethernet/IP based ICS honeypots, the first ICS virtual honeypot that is high-interactive without the use of full virtualization technologies (such as a network of virtual machines), and the first ICS honeypot that can be managed with a Software-Defined Network (SDN) controller.

Defect-prediction techniques can enhance the quality assurance activities for software systems. For instance, they can be used to predict bugs in source files or functions. In the context of a software product line, such techniques could ideally be used for predicting defects in features or combinations of features, which would allow developers to focus quality assurance on the error-prone ones. In this preliminary case study, we investigate how defect prediction models can be used to identify defective features using machine-learning techniques. We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective. Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.

The incorporation of security mechanisms to protect spacecraft's TT&c; payload links is becoming a constant requirement in many space missions. More advanced mission concepts will allow spacecrafts to have higher levels of autonomy, which includes performing key management operations independently of control centers. This is especially beneficial to support missions operating distantly from Earth. In order to support such levels of autonomy, key agreement is one approach that allows spacecrafts to establish new cryptographic keys as they deem necessary. This work introduces an approach based on a trusted platform module that allows for key agreement to be performed with minimal computational efforts and protocol iterations. Besides, it allows for opportunistic control center reporting while avoiding man-in-the-middle and replay attacks.

A yet-to-be-solved but very vital problem in forensics analysis is accurate memory dump data type reverse engineering where the target process is not a priori specified and could be any of the running processes within the system. We present ReViver, a lightweight system-wide solution that extracts data type information from the memory dump without its past execution traces. ReViver constructs the dump's accurate data structure layout through collection of statistical information about possible past traces, forensics inspection of the present memory dump, and speculative investigation of potential future executions of the suspended process. First, ReViver analyzes a heavily instrumented set of execution paths of the same executable that end in the same state of the memory dump (the eip and call stack), and collects statistical information the potential data structure instances on the captured dump. Second, ReViver uses the statistical information and performs a word-byword data type forensics inspection of the captured memory dump. Finally, ReViver revives the dump's execution and explores its potential future execution paths symbolically. ReViver traces the executions including library/system calls for their known argument/return data types, and performs backward taint analysis to mark the dump bytes with relevant data type information. ReViver's experimental results on real-world applications are very promising (98.1%), and show that ReViver improves the accuracy of the past trace-free memory forensics solutions significantly while maintaining a negligible runtime performance overhead (1.8%).

Behavior-based tracking is an unobtrusive technique that allows observers to monitor user activities on the Internet over long periods of time – in spite of changing IP addresses. Previous work has employed supervised classifiers in order to link the sessions of individual users. However, classifiers need labeled training sessions, which are difficult to obtain for observers. In this paper we show how this limitation can be overcome with an unsupervised learning technique. We present a modified k-means algorithm and evaluate it on a realistic dataset that contains the Domain Name System (DNS) queries of 3,862 users. For this purpose, we simulate an observer that tries to track all users, and an Internet Service Provider that assigns a different IP address to every user on every day. The highest tracking accuracy is achieved within the subgroup of highly active users. Almost all sessions of 73% of the users in this subgroup can be linked over a period of 56 days. 19% of the highly active users can be traced completely, i.e., all their sessions are assigned to a single cluster. This fraction increases to 40% for shorter periods of seven days. As service providers may engage in behavior-based tracking to complement their existing profiling efforts, it constitutes a severe privacy threat for users of online services. Users can defend against behavior-based tracking by changing their IP address frequently, but this is cumbersome at the moment.

The specification and implementation of network protocols are difficult tasks. This is particularly the case for WSN nodes which are typically implemented on low power microcontrollers with limited processing capabilities. Those platforms do not have the resources to run a full-fledged operating system but instead are programmed using a Real Time Operating System (RTOS) specialized in low-power wireless communications. The most popular are Contiki and TinyOS. Those RTOS support a fully-compliant IPv6 stack including the 6LoWPAN adaptation layer [1], several radio duty-cycling MAC protocols [2], [3] and multiple routing protocols [4].

The IPv6 Routing Protocol for Low-Power and Lossy Networks was recently introduced as the new routing standard for the Internet of Things. Although RPL defines basic security modes, it remains vulnerable to topological attacks which facilitate blackholing, interception, and resource exhaustion. We are concerned with analyzing the corresponding threats and protecting future RPL deployments from such attacks. Our contributions are twofold. First, we analyze the state of the art, in particular the protective scheme VeRA and present two new rank order attacks as well as extensions to mitigate them. Second, we derive and evaluate TRAIL, a generic scheme for topology authentication in RPL. TRAIL solely relies on the basic assumptions of RPL that (1) the root node serves as a trust anchor and (2) each node interconnects to the root in a straight hierarchy. Using proper reachability tests, TRAIL scalably and reliably identifies any topological attacker without strong cryptographic efforts.

In presence of known and unknown vulnerabilities in code and flow control of programs, virtual machine alike isolation and sandboxing to confine maliciousness of process, by monitoring and controlling the behaviour of untrusted application, is an effective strategy. A confined malicious application cannot effect system resources and other applications running on same operating system. But present techniques used for sandboxing have some drawbacks ranging from scope to methodology. Some of proposed techniques restrict specific aspect of execution e.g. system calls and file system access. In the same way techniques that truly isolate the application by providing separate execution environment either require modification in kernel or full blown operating system. Moreover these do not provide isolation from top to bottom but only virtualize operating system services. In this paper, we propose a design to confine native Linux process in virtual machine equivalent isolation by using hardware virtualization extensions with nominal initialization and acceptable execution overheads. We implemented our prototype called Process Virtual Machine that transition a native process into virtual machine, provides minimal possible execution environment, intercept and virtualize system calls to execute it on host kernel. Experimental results show effectiveness of our proposed technique.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Signed social networks have become increasingly important in recent years because of the ability to model trust-based relationships in review sites like Slashdot, Epinions, and Wikipedia. As a result, many traditional network mining problems have been re-visited in the context of networks in which signs are associated with the links. Examples of such problems include community detection, link prediction, and low rank approximation. In this paper, we will examine the problem of ranking nodes in signed networks. In particular, we will design a ranking model, which has a clear physical interpretation in terms of the sign of the edges in the network. Specifically, we propose the Troll-Trust model that models the probability of trustworthiness of individual data sources as an interpretation for the underlying ranking values. We will show the advantages of this approach over a variety of baselines.

Malicious I/O devices might compromise the OS using DMAs. The OS therefore utilizes the IOMMU to map and unmap every target buffer right before and after its DMA is processed, thereby restricting DMAs to their designated locations. This usage model, however, is not truly secure for two reasons: (1) it provides protection at page granularity only, whereas DMA buffers can reside on the same page as other data; and (2) it delays DMA buffer unmaps due to performance considerations, creating a vulnerability window in which devices can access in-use memory. We propose that OSes utilize the IOMMU differently, in a manner that eliminates these two flaws. Our new usage model restricts device access to a set of shadow DMA buffers that are never unmapped, and it copies DMAed data to/from these buffers, thus providing sub-page protection while eliminating the aforementioned vulnerability window. Our key insight is that the cost of interacting with, and synchronizing access to the slow IOMMU hardware–-required for zero-copy protection against devices–-make copying preferable to zero-copying. We implement our model in Linux and evaluate it with standard networking benchmarks utilizing a 40,Gb/s NIC. We demonstrate that despite being more secure than the safest preexisting usage model, our approach provides up to 5x higher throughput. Additionally, whereas it is inherently less scalable than an IOMMU-less (unprotected) system, our approach incurs only 0%–25% performance degradation in comparison.

Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system's output serves as one of the inputs for the users' decision making processes. In this work, we study the dynamics of explicit and implicit user trust in a simulated automated quality monitoring system, as a function of the system accuracy. We establish that users correctly perceive the accuracy of the system and adjust their trust accordingly.

Trust is an important facilitator for successful business relationships and an important technology adoption determinant. However, thus far trust has received little attention in the context of cloud computing, resulting in a lack of understanding of the dimensions of trust in cloud services and trust-building antecedents. Although the literature provides various conceptual models of trust for contexts related to cloud computing that may serve as a reference, in particular trust in IT outsourcing providers and trust in IT artifacts, idiosyncrasies of trust in cloud computing require a novel conceptual model of trust. First, a cloud service has a dual nature of being an IT artifact and a service provided by an organization. Second, cloud services are offered in impersonal cloud marketplaces and build upon a nested network of cloud services within the cloud ecosystem. In this article, we first analyze the concept of trust in cloud contexts. Next, we develop a conceptual model that describes trust in cloud services. The conceptual model incorporates the duality of trust in a cloud provider organization and trust in an IT artifact, as well as trust types for the impersonal environment and the cloud computing ecosystem. Using the conceptual model as a lens we then review 43 empirical studies on trust in IT outsourcing and trust in IT artifacts that were identified by a structured literature search. The resulting conceptual model provides a conceptual typology of constructs for trust in cloud services, defines trust-building antecedents, and develops 19 propositions describing the relationships between trust constructs and between trust constructs and trust-building antecedents. The conceptual model contributes to research by creating grounds for future theory-building on trust in cloud contexts, integrating two previously disjoint strands in the trust literature, and identifying knowledge gaps. Based on the conceptual model, we furthermore provide practical advice for managers from service providers, platform providers, customers, and institutional authorities.

Ethernet technology dominates enterprise and home network installations and is present in datacenters as well as parts of the backbone of the Internet. Due to its wireline nature, Ethernet networks are often assumed to intrinsically protect the exchanged data against attacks carried out by eavesdroppers and malicious attackers that do not have physical access to network devices, patch panels and network outlets. In this work, we practically evaluate the possibility of wireless attacks against wired Ethernet installations with respect to resistance against eavesdropping by using off-the-shelf software-defined radio platforms. Our results clearly indicate that twisted-pair network cables radiate enough electromagnetic waves to reconstruct transmitted frames with negligible bit error rates, even when the cables are not damaged at all. Since this allows an attacker to stay undetected, it urges the need for link layer encryption or physical layer security to protect confidentiality.

A well known technique for embedding high dimensional objects in two or three dimensional space is the t-distributed stochastic neighbour embedding (t-SNE). The t-SNE minimizes the Kullback-Liebler (KL) divergence between two probability distributions, one induced on points in the high dimensional space and the other induced on points in the low dimensional embedding space. In this work, we consider a more general framework of using Rényi divergence which is parametrized by the order α, the KL-divergence is a special case when α → 1.We study how various Rényi divergences perform when compared to the KL-divergence. We show that in terms of the metrics of trustworthiness and neighbourhood preservation, the embedding becomes better as Rényi divergence approaches the KL-divergence.