Biblio

Cyber-Physical Systems (CPS) consist of embedded computers with sensing and actuation capability, and are integrated into and tightly coupled with a physical system. Because the physical and cyber components of the system are tightly coupled, cyber-security is important for ensuring the system functions properly and safely. However, the effects of a cyberattack on the whole system may be difficult to determine, analyze, and therefore detect and mitigate. This work presents a model based software development framework integrated with a hardware-in-the-loop (HIL) testbed for rapidly deploying CPS attack experiments. The framework provides the ability to emulate low level attacks and obtain platform specific performance measurements that are difficult to obtain in a traditional simulation environment. The framework improves the cybersecurity design process which can become more informed and customized to the production environment of a CPS. The developed framework is illustrated with a case study of a railway transportation system.

As the malware threat landscape is constantly evolving and over one million new malware strains are being generated every day [1], early automatic detection of threats constitutes a top priority of cybersecurity research, and amplifies the need for more advanced detection and classification methods that are effective and efficient. In this paper, we present the application of machine learning algorithms to predict the length of time malware should be executed in a sandbox to reveal its malicious intent. We also introduce a novel hybrid approach to malware classification based on static binary analysis and dynamic analysis of malware. Static analysis extracts information from a binary file without executing it, and dynamic analysis captures the behavior of malware in a sandbox environment. Our experimental results show that by turning the aforementioned problems into machine learning problems, it is possible to get an accuracy of up to 90% on the prediction of the malware analysis run time and up to 92% on the classification of malware families.

Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life - often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

In this paper, inspired by Gatys's recent work, we propose a novel approach that transforms photos to comics using deep convolutional neural networks (CNNs). While Gatys's method that uses a pre-trained VGG network generally works well for transferring artistic styles such as painting from a style image to a content image, for more minimalist styles such as comics, the method often fails to produce satisfactory results. To address this, we further introduce a dedicated comic style CNN, which is trained for classifying comic images and photos. This new network is effective in capturing various comic styles and thus helps to produce better comic stylization results. Even with a grayscale style image, Gatys's method can still produce colored output, which is not desirable for comics. We develop a modified optimization framework such that a grayscale image is guaranteed to be synthesized. To avoid converging to poor local minima, we further initialize the output image using grayscale version of the content image. Various examples show that our method synthesizes better comic images than the state-of-the-art method.

Given a model with multiple input parameters, and multiple possible sources for collecting data for those parameters, a data collection strategy is a way of deciding from which sources to sample data, in order to reduce the variance on the output of the model. Cain and Van Moorsel have previously formulated the problem of optimal data collection strategy, when each arameter can be associated with a prior normal distribution, and when sampling is associated with a cost. In this paper, we present ADaCS, a new tool built as an extension of PRISM, which automatically analyses all possible data collection strategies for a model, and selects the optimal one. We illustrate ADaCS on attack trees, which are a structured approach to analyse the impact and the likelihood of success of attacks and defenses on computer and socio-technical systems. Furthermore, we introduce a new strategy exploration heuristic that significantly improves on a brute force approach.

The risk posed by insider threats has usually been approached by analyzing the behavior of users solely in the cyber domain. In this paper, we show the viability of using physical movement logs, collected via a building access control system, together with an understanding of the layout of the building housing the system’s assets, to detect malicious insider behavior that manifests itself in the physical domain. In particular, we propose a systematic framework that uses contextual knowledge about the system and its users, learned from historical data gathered from a building access control system, to select suitable models for representing movement behavior. We then explore the online usage of the learned models, together with knowledge about the layout of the building being monitored, to detect malicious insider behavior. Finally, we show the effectiveness of the developed framework using real-life data traces of user movement in railway transit stations.

Though the GNSS receiver baseband signal processing realizes more precise estimation by using Kalman Filter, traditional KF-based tracking loops estimate code phase and carrier frequency simultaneously by a single filter. In this case, the error of code phase estimate can affect the carrier frequency tracking loop, which is vulnerable than code tracking loop. This paper presents a tracking architecture based on dual filter. Filters can performing code locking and carrier tracking respectively, hence, the whole tracking loop ultimately avoid carrier tracking being subjected to code tracking errors. The control system is derived according to the mathematical expression of the Kalman system. Based on this model, the transfer function and equivalent noise bandwidth are derived in detail. As a result, the relationship between equivalent noise bandwidth and Kalman gain is presented. Owing to this relationship, the equivalent noise bandwidth for a well-designed tracking loop can adjust automatically with the change of environments. Finally, simulation and performance analysis for this novel architecture are presented. The simulation results show that dual Kalman filters can restrain phase noise more effectively than the loop filter of the classical GNSS tracking channel, therefore this whole system seems more suitable to working in harsh environments.

In this paper we explore how to embed status information of IoT-enabled devices in the acoustic atmosphere using melodic ambient sounds while limiting obtrusiveness for the user. The user can use arbitrary sound samples to represent the devices he wants to monitor. Our system combines these sound samples into a melodic ambient rhythm that contains information on all the processes or variables that user is monitoring. We focus on continuous rather than binary information (e.g. "monitoring progress status" rather then "new message received"). We evaluate our system in a machine monitoring scenario focusing on 5 distinct machines/processes to monitor with 6 priority levels for each. 9 participants use our system to monitor these processes with an up to 92.44% detection rate, if several levels are combined. Participants had no previous experience with this or similar systems and had only 5-10 minute training session before the tests.

In the ASPIRE research project, a software protection tool flow was designed and prototyped that targets native ARM Android code. This tool flow supports the deployment of a number of protections against man-at-the-end attacks. In this tutorial, an overview of the tool flow will be presented and attendants will participate to a hands-on demonstration. In addition, we will present an overview of the decision support systems developed in the project to facilitate the use of the protection tool flow.

Advances in Device-to-Device (D2D) ecosystems have brought on mobile applications that utilise nearby mobile devices in order to improve users' quality of experience (QoE). The interactions between the mobile devices have to be transparent to the end users and can be of many services – opportunistic networking, traffic offloading, computation offloading, cooperative streaming and P2P based k-anonymity location privacy service, to name a few. Whenever mobile users are willing to "ask for help" from their neighbours, they need to make non trivial decisions in order to maximise their utility. Current motivation approaches for mobile users that participate in such environments are of two types: (i) credit-based and (ii) reputation-based. These approaches rely either on centralised authorities or require prohibitively many messages or require tamper resistant security modules. In this paper we propose a trust-based approach that does not require synchronisation between the mobile users. Moreover, we present the three-way tradeoff between, consistency, message exchange and awareness and we conclude that our approach can provide first-rate data to neighbour selection mechanisms for D2D ecosystems with much less overhead.

Three-dimensional typography (3D typography) refers to the arrangement of text in three-dimensional space. It injects vitality into the letters, thereby giving the viewer a strong impression that is hard to forget. These days, 3D typography plays an important role in daily life beyond the artistic design. It is easy to observe the 3D typography used in the 3D virtual space such as movie or games. Also it is used frequently in signboard or furniture design. Despite its noticeable strength, most of the 3D typography is generated by just a simple extrusion of flat 2D typography. Comparing with 2D typography, 3D typography is more difficult to generate in short time due to its high complexity.

Digital Signage (DS) is one of the popular IoT technologies deployed in the urban space. DS can provide wayfinding and urban information to city dwellers and convey targeted messaging and advertising to people approaching the DS. With the rise of the online-to-offline (O2O) mobile commerce, DS also become an important marketing tool in urban retailing. However, most digital signage systems today lack interactive feature and context-aware recommendation engine. Few interactive digital signage systems available today are also insufficient in engaging anonymous viewers and also not considering temporal interaction between viewer and DS system. To overcome the above challenges, this paper proposes a context-aware recommender system framework with novel temporal interaction scheme for IoT based interactive digital signage deployed in urban space to engage anonymous viewer. The results of experiments indicate that the proposed framework improves the advertising effectiveness for DS system deployed in public in urban space.

This demo shows how two objects that each live within their own world, i.e., the are not in each others transitive closure of object references, can get to know each other in a well-defined manner using a new language construct. The basic problem is that if two object are in different worlds, there is no way they can communicate. Our proposed language construct, added to the Emerald programming language, allows objects in close proximity to get to know each other in a well-defined, language based manner.

Distributed detection with data fusion has gained great attention in recent years. Collaborative detection improves the performance, and the optimal sensor deployment may change with time. It has been shown that with data fusion less sensors are needed to get the same detection ability when abundant sensors are deployed randomly. However, because of limitations on equipment number and deployment methods, fixed sensor locations may be preferred underwater. In this paper, we try to establish a theoretical framework for finding sensor positions to maximize the detection probability with a distributed sensor network. With joint data processing, detection performance is related to all the sensor locations; as sensor number grows, the optimization problem would become more difficult. To simplify the demonstration, we choose a 1-dimensional line deployment model and present the relevant numerical results.

To improve the information assurance of mission execution over modern IT infrastructure, new cyber defenses need to not only provide security benefits, but also perform within a given cost regime. Current approaches for validating and integrating cyber defenses heavily rely on manual trial-and-error, without a clear and systematic understanding of security versus cost tradeoffs. Recent work on model-based analysis of cyber defenses has led to quantitative measures of the attack surface of a distributed system hosting mission critical applications. These metrics show great promise, but the cost of manually creating the underlying models is an impediment to their wider adoption. This paper describes an experimentation framework for automating multiple activities associated with model construction and validation, including creating ontological system models from real systems, measuring and recording distributions of resource impact and end-to-end performance overhead values, executing real attacks to validate theoretic attack vectors found through analytic reasoning, and creating and managing multi-variable experiments.

This paper describes the hardware acceleration of various feature calculation functions used in activity recognition. In this work we have used a large scale sensing matrix which recognizes and counts gym exercises. Human activity is played on pressure matrix and the sensor data is sent to computer using a wired protocol for further processing. The recorded data from matrix is huge making it impractical to process on a smart phone. We propose a FPGA (Field Programmable Gate Array) based processing methodology which not only accelerates sensing data processing but also reduces the size of 2D sensor data matrix to 10 features. The resultant feature set can be transferred using wireless medium to a smart phone or other processing unit where the classification can be done. Our system takes a matrix of arbitrary size and output a 'features' set for each matrix frame. We used HLS (High Level Synthesis), an approach to write algorithm for FPGA using SystemC/C/C++ instead of traditional VHDL/Verilog. Results show promising improvement in processing time as compared to Matlab. Since the size of data is reduced, wireless medium can be use to transmit data. Additionally, the development time for FPGA designs is greatly reduced due to the usage of an abstracted high level synthesis approach. This system is currently developed for pressure sensing system but this strategy can be applied to other sensing application like temperature sensor grid.

It is standard practice in enterprises to analyze large amounts of logs to detect software failures and malicious behaviors. Mobile applications pose a major challenge to centralized monitoring as network and storage limitations prevent fine-grained logs to be stored and transferred for off-line analysis. In this paper we introduce EMMA, a framework for automatic anomaly detection that enables security analysis as well as in-the-field quality assurance for enterprise mobile applications, and incurs minimal overhead for data exchange with a back-end monitoring platform. EMMA instruments binary applications with a lightweight anomaly-detection layer that reveals failures and security threats directly on mobile devices, thus enabling corrective measures to be taken promptly even when the device is disconnected. In our empirical evaluation, EMMA detected failures in unmodified Android mobile applications.

The increasing amount of structured and semi-structured information available on the Web and in distributed information systems, as well as the Web's diversification into different segments such as the Social Web, the Deep Web, or the Dark Web, requires new methods for horizontal search. FuhSen is a federated, RDF-based, hybrid search platform that searches, integrates and summarizes information about entities from distributed heterogeneous information sources using Linked Data. As a use case, we present scenarios where law enforcement institutions search and integrate data spread across these different Web segments to identify cases of organized crime. We present the architecture and implementation of FuhSen and explain the queries that can be addressed with this new approach.

The local minima has been deemed as a challenging issue when designing routing protocols for 3D underwater sensor networks. Recently, harmonic potential field method has been used to tackle the issue of local minima which was also a major bottleneck in path planning and obstacle avoidance of robotics community. Inspired by this, this paper proposes a harmonic potential field based routing protocol for 3D underwater sensor networks with local minima. More specifically, the harmonic potential field is calculated using harmonic functions and Dirichlet boundary conditions are used for the local minima, sink(or seabuoy) and sending node. Numerical results show the effectiveness of the proposed routing protocol.

Underwater sensing is envisioned using inexpensive underwater sensor nodes distributed over a wide area, deployed close to the bottom, and networked through underwater acoustic communications. In this paper, an underwater acoustic sensor node to perform the underwater sensing is designed and implemented. Specifically, we describe the design criteria, architecture and functional modules of underwater acoustic sensor node. Moreover, we give the experiment results of ocean current field estimation using the designed underwater acoustic sensor nodes at the sea area of Liuheng, Zhoushan, China.

In this paper, a multi-channel medium access control (MAC) protocol is proposed to overcome the Large Interference Range Collision (LIRC) problem in underwater acoustic sensor networks (UWASNs), which has been known to occur when a handshaking based MAC protocol is jointly used with a power control. The proposed scheme divides the frequency band into two separate channels each used for control and data packets transmission. Considering the acoustic signal attenuation characteristics, higher frequency is used for data and lower frequency is used for control. And then, the data transmission power is controlled to escape the LIRC problem and simultaneously to save as much as possible. Furthermore with the separated channels, we can also reduce control-data packet collisions.

In this study, we evaluate a multipath diversity reception in underwater CDMA system by performing a lake experiment. First, we design CDMA transmitter and receiver equipped with a multipath diversity with equal gain combining (EGC) and maximal ratio combining (MRC). Then, an experiment is performed at Lake Kyungcheon, South Korea to show that the diversity combining successfully corrects bit errors caused by multipath fading.

The harsh environment in the water has imposed challenges for underwater sensor networks (USNs), which collect the sensed data from the underwater sensors to the sink on land. The time-varying underwater acoustic channel has low band-width and high bit error rate, which leads to low data collection efficiency. Furthermore, the heterogeneous model of USNs that uses acoustic communications under the water and wireless communication above the water makes it difficult in efficient routing and forwarding for data collection. To this end, we propose a novel on-surface wireless-assisted opportunistic routing (SurOpp) for USNs. SurOpp deploys multiple buoy nodes on surface and includes all of them in the forwarding candidates to form a receive diversity. The opportunities of reception and forwarding in buoy nodes are exploited to improve the end-to-end transmissions. SurOpp also adopts rateless codes in the source to achieve opportunistic reception in the sink. The cooperation of both opportunistic reception in the buoys and the sink further decreases the messages of control overhead. The wireless interface in the buoy undertakes all the message exchanges in forwarding coordination to compensate the bandwidth limit of the acoustic channel. Simulations in NS3 show that SurOpp outperforms the traditional routing and existing opportunistic routing in terms of packet delivery ratio, end-to-end delay and energy consumption.

In this work, we develop an underwater relay network model for an unmanned cruise system. By introducing the underwater cruise, we analyze end-to-end outage performance for collecting data from a sensor node. Based on theoretical derivation of the outage probability, we further analyze the optimized location and data rate for relaying.

Several duty-cycling and energy-efficient communication protocols have been presented to solve power constraints of sensor nodes. The battery power of sensor nodes can be also supplied by surrounding energy resources using energy harvesting techniques. However, communication protocols only offer limited power for sensor nodes and energy harvesting may encounter a challenge that sensor nodes are unable to draw power from surrounding energy resources in certain environments. Thus, an emerging technology, wireless rechargeable sensor networks (WRSNs), is proposed to enhance the proposed communication protocols and energy harvesting techniques [1]. With a WRSN, a mobile vehicle is used to supply power to sensor nodes by wireless energy transfer. One of the most significant issue in WRSNs is path planning of the mobile vehicle. The mobile vehicle based on its movement trajectory visits each sensor nodes to recharge them so that the sensor nodes can obtain sufficient energy to execute continuous missions. However, all of the existing mobile vehicles charging methods [2, 3] for WRSNs require the locations of the sensor nodes based on the assumption that the location of each sensor node is known in advance by one of the sensor network localization mechanisms. Therefore, the proposed system integrates both the localization and wireless charging mechanisms for WRSNs to decrease the system initialization time and cost.