Abstract A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.
Smart cyber-physical systems (SCPS) have become an important part of the IT landscape. Often these systems include IoT devices that allow effective and easy acquisition of data in areas such as healthcare, smart cities, smart vehicles, and smart homes @cite_66 . Data mining and analysis are among the primary goals of collecting data from SCPS. The infrastructural extensions of SCPSs have contributed to the exponential growth in the number of IoT sensors, but security is often overlooked, and the devices become a source of privacy leak. The security and privacy concerns of big data and data streams are not entirely new, but require constant attention due to technological advancements of the environments and the devices used @cite_40 . Confidentiality, authentication, and authorization are just a few of the concerns @cite_44 @cite_83 @cite_32 . Many studies have raised the importance of privacy and security of SCPS due to their heavy use of personally identifiable information (PII) @cite_84 . Controlling access via authentication @cite_67 , attribute-based encryption @cite_74 , temporal and location-based access control @cite_67 and employing constraint-based protocols @cite_61 are some examples of improving privacy of SCPS.
@cite_61 @cite_67 @cite_32 @cite_84 @cite_44 @cite_40 @cite_83 @cite_74 @cite_66
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Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI’s, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard. Smart Cyber--Physical Systems (sCPS) are modern CPS systems that are engineered to seamlessly integrate a large number of computation and physical components; they need to control entities in their environment in a smart and collective way to achieve a high degree of effectiveness and efficiency. At the same time, these systems are supposed to be safe and secure, deal with environment dynamicity and uncertainty, cope with external threats, and optimize their behavior to achieve the best possible outcome. This "smartness" typically stems from highly cooperative behavior, self--awareness, self--adaptation, and selfoptimization. Most of the "smartness" is implemented in software, which makes the software one of the most complex and most critical constituents of sCPS. As the specifics of sCPS render traditional software engineering approaches not directly applicable, new and innovative approaches to software engineering of sCPS need to be sought. This paper reports on the results of the Second International Workshop on Software Engineering for Smart Cyber--Physical Systems (SEsCPS 2016), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS. This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy. Abstract In a cyber-physical system, the control component plays an essential role to make the cyber and physical components work harmoniously together. When information collected from the physical space contains private or sensitive data that cannot be passed onto the cyber space, properly controlling the cyber-physical system becomes a very challenging task. For instance, the smart grid systems, a replacement for the traditional power grid systems, have been widely used in the industries. To prevent power shortage, threshold-based power usage control (PUC) in a smart grid considers a situation where the utility company sets a threshold to control the total power usage or supply of a neighborhood. If the total power usage exceeds the threshold, either certain households need to reduce their power consumption or the utility company needs to buy additional power supplies to meet the increasing demand. In these scenarios, the utility company needs to frequently collect power usage data from smart meters. It has been well documented that these power usage data can reveal a person's daily activity and violate personal privacy. To mitigate the privacy concerns, the goal of this paper is to develop efficient and privacy-preserving power usage control protocols that allow a utility company to balance supply and demand in a smart grid without violating personal privacy of its customers. We will provide extensive empirical study to show the practicality of our proposed protocols. Abstract The safety of medical data and equipment plays a vital role in today’s world of Medical Internet of Things (MIoT). These IoT devices have many constraints (e.g., memory size, processing capacity, and power consumption) that make it challenging to use cost-effective and energy-efficient security solutions. Recently, researchers have proposed a few Radio-Frequency Identification (RFID) based security solutions for MIoT. The use of RFID technology in securing IoT systems is rapidly increasing because it provides secure and lightweight safety mechanisms for these systems. More recently, authors have proposed a lightweight RFID mutual authentication (LRMI) protocol. The authors argue that LRMI meets the necessary security requirements for RFID systems, and the same applies to MIoT applications as well. In this paper, our contribution has two-folds, firstly we analyze the LRMI protocol’s security to demonstrate that it is vulnerable to various attacks such as secret disclosure, reader impersonation, and tag traceability. Also, it is not able to preserve the anonymity of the tag and the reader. Secondly, we propose a new secure and lightweight mutual RFID authentication (SecLAP) protocol, which provides secure communication and preserves privacy in MIoT systems. Our security analysis shows that the SecLAP protocol is robust against de-synchronization, replay, reader tag impersonation, and traceability attacks, and it ensures forward and backward data communication security. We use Burrows-Abadi-Needham (BAN) logic to validate the security features of SecLAP. Moreover, we compare SecLAP with the state-of-the-art and validate its performance through a Field Programmable Gate Array (FPGA) implementation, which shows that it is lightweight, consumes fewer resources on tags concerning computation functions, and requires less number of flows. Cyber-Physical Systems (CPS) are large interconnected softwareintensive systems that influence, by sensing and actuating, the physical world. Examples are traffic management and power grids. One of the trends we observe is the need to endow such systems with the "smart" capabilities, typically in the form of selfawareness and self-adaptation, along with the traditional qualities of safety and dependability. These requirements combined with specifics of the domain of smart CPS -- such as large scale, the role of end-users, uncertainty, and open-endedness -- render traditional software engineering (SE) techniques not directly applicable; making systematic SE of smart CPS a challenging task. This paper reports on the results of the First International Workshop on Software Engineering of Smart Cyber-Physical Systems (SEsCPS 2015), where participants discussed characteristics, challenges and opportunities of SE for smart CPS, with the aim to outline an agenda for future research in this important area. ABSTRACTSmart grid (SG) allows for two-way communication between the utility and its consumers and hence they are considered as an inevitable future of the traditional grid. Since consumers are the key component of SGs, providing security and privacy to their personal data is a critical problem. In this paper, a security protocol, namely TPS3, is based on Temporal Perturbation and Shamir’s Secret Sharing (SSS) schemes that are proposed to ensure the privacy of SG consumer’s data. Temporal perturbation is employed to provide temporal privacy, while the SSS scheme is used to ensure data confidentiality. Temporal perturbation adds random delays to the data collected by smart meters, whereas the SSS scheme fragments these data before transmitting them to the data collection server. Joint employment of both schemes makes it hard for attackers to obtain consumer data collected in the SG. The proposed protocol TPS3 is evaluated in terms of privacy, reliability, and communication cost using two different SG topol... With the increasing of mobile devices, Device-to-Device(D2D) communication is considered as a promising technology for achieving direct communication of devices. But the security issues still remain to be solved. In this paper, We utilize D2D communication between UEs in proximity to share epidemic media contents. We propose a Secure Content Sharing Protocol(SCSP) that combine with the credibility between users. The credibility involves user's interest, interaction and geographical location. By means of encryption, signature and authentication in SCSP, the data confidentiality, non-repudiation and mutual authentication can be guaranteed. Furthermore, the user's privacy can be protected based on Pallier Homomorphic Encryption. At last, it is shown that the proposed SCSP can meet the security requirements and have lower computation cost in comparison with other related works through security analysis and quantitative evaluation. Distributed sensor networks are becoming a robust solution that allows users to directly access data generated by individual sensors. In many practical scenarios, fine-grained access control is a pivotal security requirement to enhance usability and protect sensitive sensor information from unauthorized access. Recently, there have been proposed many schemes to adapt public key cryptosystems into sensor systems consisting of high-end sensor nodes in order to enforce security policy efficiently. However, the drawback of these approaches is that the complexity of computation increases linear to the expressiveness of the access policy. Key-policy attribute-based encryption is a promising cryptographic solution to enforce fine-grained access policies on the sensor data. However, the problem of applying it to distributed sensor networks introduces several challenges with regard to the attribute and user revocation. In this paper, we propose an access control scheme using KP-ABE with efficient attribute and user revocation capability for distributed sensor networks that are composed of high-end sensor devices. They can be achieved by the proxy encryption mechanism which takes advantage of attribute-based encryption and selective group key distribution. The analysis results indicate that the proposed scheme achieves efficient user access control while requiring the same computation overhead at each sensor as the previous schemes. Abstract In large-scale Internet of Things (IoT) systems, huge volumes of data are collected from anywhere at any time, which may invade people’s privacy, especially when the systems are used in medical or daily living environments. Preserving privacy is an important issue, and higher privacy demands usually tend to require weaker identity. However, previous research has indicated that strong security tends to demand strong identity, especially in authentication processes. Thus, defining a good tradeoff between privacy and security remains a challenging problem. This motivates us to develop a privacy-preserving and accountable authentication protocol for IoT end-devices with weaker identity, which integrates an adapted construction of short group signatures and Shamir’s secret sharing scheme. We analyze the security properties of our protocol in the context of six typical attacks and verify the formal security using the Proverif tool. Experiments using our implementation in MacBook Pro and Intel Edison development platforms show that our authentication protocol is feasible in practice. GPS-enabled devices are now ubiquitous, from airplanes and cars to smartphones and wearable technology. This has resulted in a wealth of data about the movements of individuals and populations, which can be analyzed for useful information to aid in city and traffic planning, disaster preparedness and so on. However, the places that people go can disclose extremely sensitive information about them, and thus their use needs to be filtered through privacy preserving mechanisms. This turns out to be a highly challenging task: raw trajectories are highly detailed, and typically no pair is alike. Previous attempts fail either to provide adequate privacy protection, or to remain sufficiently faithful to the original behavior. This paper presents DPT, a system to synthesize mobility data based on raw GPS trajectories of individuals while ensuring strong privacy protection in the form of e-differential privacy. DPT makes a number of novel modeling and algorithmic contributions including (i) discretization of raw trajectories using hierarchical reference systems (at multiple resolutions) to capture individual movements at differing speeds, (ii) adaptive mechanisms to select a small set of reference systems and construct prefix tree counts privately, and (iii) use of direction-weighted sampling for improved utility. While there have been prior attempts to solve the subproblems required to generate synthetic trajectories, to the best of our knowledge, ours is the first system that provides an end-to-end solution. We show the efficacy of our synthetic trajectory generation system using an extensive empirical evaluation. The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80 accuracy with only one try and more than 90 accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information. Pervasive healthcare systems, smart grids, and unmanned aircraft systems are examples of Cyber-Physical Systems (CPSs) that have become highly integrated in the modern world. As this integration deepens, the importance of securing these systems increases. In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. We summarize advantages and drawbacks of each dimension’s options. We also summarize the most and least studied CPS IDS techniques in the literature and provide insight on the effectiveness of IDS techniques as they apply to CPSs. Finally, we identify gaps in CPS IDS research and suggest future research areas. Distributed sensor data storage and retrieval have gained increasing popularity in recent years for supporting various applications. While distributed architecture enjoys a more robust and fault-tolerant wireless sensor network (WSN), such architecture also poses a number of security challenges especially when applied in mission-critical applications such as battlefield and e-healthcare. First, as sensor data are stored and maintained by individual sensors and unattended sensors are easily subject to strong attacks such as physical compromise, it is significantly harder to ensure data security. Second, in many mission-critical applications, fine-grained data access control is a must as illegal access to the sensitive data may cause disastrous results and or be prohibited by the law. Last but not least, sensor nodes usually are resource-constrained, which limits the direct adoption of expensive cryptographic primitives. To address the above challenges, we propose, in this paper, a distributed data access control scheme that is able to enforce fine-grained access control over sensor data and is resilient against strong attacks such as sensor compromise and user colluding. The proposed scheme exploits a novel cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it for WSNs with respect to both performance and security requirements. The feasibility of the scheme is demonstrated by experiments on real sensor platforms. To our best knowledge, this paper is the first to realize distributed fine-grained data access control for WSNs. Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85 of them from the data logs generated at runtime. Side-channel attacks (SCAs), such as differential power analysis or differential electromagnetic analysis, pose a serious threat to the security of embedded systems. In the literature, few articles address the problem of securing general purpose processors (GPPs) with resourceful countermeasures. However, in many low-cost applications, where security is not critical, cryptographic algorithms are typically implemented in software. Since it has been proved that GPPs are vulnerable to SCAs, it is desirable to develop efficient mechanisms to ensure a certain level of security. In this paper, we extend side-channel countermeasures to the register transfer level description. The challenge is to create a new class of processor that executes embedded software applications, which are intrinsically protected against SCAs. For that purpose, we first investigate how to integrate into the datapath two countermeasures based on masking and hiding approaches. Through an FPGA-based processor, we then evaluate the overhead and the effectiveness of the proposed solutions against time-domain first-order attacks. We finally show that a suitable combination of countermeasures significantly increases the side-channel resistance in a cost-effective way. The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large scale IoT based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain enabled decentralized capability based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identity based capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine grained AC solution to IoT systems. The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications. The use of radio for communication with medical devices provides important convenience and safety features. However, devices that respond to unauthorized queries may inadvertently disclose their presence. It is reasonable for users to expect that their devices' detectability by third parties to be limited, and thus even eavesdroppers to an authorized conversation (e.g. a command sent to an insulin pump) should observe an access code of minimal utility for triggering future transmissions. Access rights should be revocable in a manner that limits that harm that can be caused by authorized devices in the possession of unauthorized users. In order to detect inappropriate use, it may be desirable for access history to be available for audit. We describe a family of replay-attack resistant protocols suitable for embedding into query messages that can authorize users with a range of differentiated access rights. Implanted devices utilizing these techniques can evade detection by suppressing responses to queries that do not contain appropriate authorization. Techniques are described that can support revocation of authorization and audits of access history. Finally, we observe that the processing of unauthorized queries consumes power and thus can be used to mount a potentially devastating denial-of-service attack against the batteries of both conventional devices and devices that utilize the cryptographic protocols we describe. Since field-powered communication systems (e.g. HF-RFID) contain embedded general-purpose processors that operate using only power extracted from externally applied fields, they are substantially more robust against such forms of attack. Within the last decade, Security became a major focus in the traditional IT-Industry, mainly through the interconnection of systems and especially through the connection to the Internet. This opened up a huge new attack surface, which resulted in major takedowns of legitimate services and new forms of crime and destruction. This led to the development of a multitude of new defense mechanisms and strategies, as well as the establishing of Security procedures on both, organizational and technical level. Production systems have mostly remained in isolation during these past years, with security typically focused on the perimeter. Now, with the introduction of new paradigms like Industry 4.0, this isolation is questioned heavily with Physical Production Systems (PPSs) now connected to an IT-world resulting in cyber-physical systems sharing the attack surface of traditional web based interfaces while featuring completely different goals, parameters like lifetime and safety, as well as construction. In this work, we present an outline on the major security challenges faced by cyber-physical production systems. While many of these challenges harken back to issues also present in traditional web based IT, we will thoroughly analyze the differences. Still, many new attack vectors appeared in the past, either in practical attacks like Stuxnet, or in theoretical work. These attack vectors use specific features or design elements of cyber-physical systems to their advantage and are unparalleled in traditional IT. Furthermore, many mitigation strategies prevalent in traditional IT systems are not applicable in the industrial world, e.g., patching, thus rendering traditional strategies in IT-Security unfeasible. A thorough discussion of the major challenges in CPPS-Security is thus required in order to focus research on the most important targets.