Towards a Low-Cost Remote Memory Attestation for the Smart Grid

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes

A survey on cyber security for smart grid communications

A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications. © 2012 IEEE

Distributed IoT Attestation via Blockchain (Extended Version)

The growing number and nature of Internet of Things (IoT) devices makes these resource-constrained appliances particularly vulnerable and increasingly impactful in their exploitation. Current estimates for the number of connected things commonly reach the tens of billions. The low-cost and limited computational strength of these devices can preclude security features. Additionally, economic forces and a lack of industry expertise in security often contribute to a rush to market with minimal consideration for security implications. It is essential that users of these emerging technologies, from consumers to IT professionals, be able to establish and retain trust in the multitude of diverse and pervasive compute devices that are ever more responsible for our critical infrastructure and personal information. Remote attestation is a well-known technique for building such trust between devices. In standard implementations, a potentially untrustworthy prover attests, using public key infrastructure, to a verifier about its configuration or properties of its current state. Attestation is often performed on an ad hoc basis with little concern for historicity. However, controls and sensors manufactured for the Industrial IoT (IIoT) may be expected to operate for decades. Even in the consumer market, so-called smart things can be expected to outlive their manufacturers. This longevity combined with limited software or firmware patching creates an ideal environment for long-lived zero-day vulnerabilities. Knowing both if a device is vulnerable and if so when it became vulnerable is a management nightmare as IoT deployments scale. For network connected machines, with access to sensitive information and real-world physical controls, maintaining some sense of a device\u27s lifecycle would be insightful. In this paper, we propose a novel attestation architecture, DAN: a distributed attestation network, utilizing blockchain to store and share device information. We present the design of this new attestation architecture, and describe a virtualized simulation, as well as a prototype system chosen to emulate an IoT deployment with a network of Raspberry Pi, Infineon TPMs, and a Hyperledger Fabric blockchain. We discuss the implications and potential challenges of such a network for various applications such as identity management, intrusion detection, forensic audits, and regulatory certification

Caveat (IoT) Emptor: Towards Transparency of IoT Device Presence (Full Version)

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation. This prompts, respectively, privacy and security/safety issues. The dangers of hidden IoT devices have been recognized and prior research suggested some means of mitigation, mostly based on traffic analysis or using specialized hardware to uncover devices. While such approaches are partially effective, there is currently no comprehensive approach to IoT device transparency. Prompted in part by recent privacy regulations (GDPR and CCPA), this paper motivates and constructs a privacy-agile Root-of-Trust architecture for IoT devices, called PAISA: Privacy-Agile IoT Sensing and Actuation. It guarantees timely and secure announcements about IoT devices' presence and their capabilities. PAISA has two components: one on the IoT device that guarantees periodic announcements of its presence even if all device software is compromised, and the other that runs on the user device, which captures and processes announcements. Notably, PAISA requires no hardware modifications; it uses a popular off-the-shelf Trusted Execution Environment (TEE) -- ARM TrustZone. This work also comprises a fully functional (open-sourced) prototype implementation of PAISA, which includes: an IoT device that makes announcements via IEEE 802.11 WiFi beacons and an Android smartphone-based app that captures and processes announcements. Both security and performance of PAISA design and prototype are discussed.Comment: 17 pages, 11 figures. To appear at ACM CCS 202

Cyber-physical security of a smart grid infrastructure

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Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime

Cloud Anchor: An Exploration of Service Integrity Attestation with Hardware Roots of Trust

Distributed computing has enabled developers and researchers to solve complex problems at an impressive scale. Users implicitly trust these subtasks to be performed accurately and this trust can be abused by malicious service providers who aim to compromise the integrity of the system. These problems can be solved by using dedicated hardware; however it is expensive or impossible to distribute this solution to all providers in a system. In this paper, we explore InTest, a service integrity attestation framework that uses replay-based consistency checks to detect malicious service providers without the use of dedicated hardware. We investigate if its performance is affected by network topology, its accuracy in the face of incomplete information, and if it can be improved by minimally utilizing dedicated hardware. Our preliminary solution, Cloud Anchor, reduces the number of duplicated tasks by 30% while providing identical detection rates as the prior solution

ACFA: Secure Runtime Auditing & Guaranteed Device Healing via Active Control Flow Attestation

Low-end embedded devices are increasingly used in various smart applications and spaces. They are implemented under strict cost and energy budgets, using microcontroller units (MCUs) that lack security features available in general-purpose processors. In this context, Remote Attestation (RA) was proposed as an inexpensive security service to enable a verifier (Vrf) to remotely detect illegal modifications to a software binary installed on a low-end prover MCU (Prv). Since attacks that hijack the software's control flow can evade RA, Control Flow Attestation (CFA) augments RA with information about the exact order in which instructions in the binary are executed, enabling detection of control flow attacks. We observe that current CFA architectures can not guarantee that Vrf ever receives control flow reports in case of attacks. In turn, while they support exploit detection, they provide no means to pinpoint the exploit origin. Furthermore, existing CFA requires either binary instrumentation, incurring significant runtime overhead and code size increase, or relatively expensive hardware support, such as hash engines. In addition, current techniques are neither continuous (only meant to attest self-contained operations) nor active (offer no secure means to remotely remediate detected compromises). To jointly address these challenges, we propose ACFA: a hybrid (hardware/software) architecture for Active CFA. ACFA enables continuous monitoring of all control flow transfers in the MCU and does not require binary instrumentation. It also leverages the recently proposed concept of Active Roots-of-Trust to enable secure auditing of vulnerability sources and guaranteed remediation when a compromise is detected. We provide an open-source reference implementation of ACFA on top of a commodity low-end MCU (TI MSP430) and evaluate it to demonstrate its security and cost-effectiveness

Monitoring the health and integrity of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) will play a major role in the Internet of Things collecting the data that will support decision-making and enable the automation of many applications. Nevertheless, the introduction of these devices into our daily life raises serious concerns about their integrity. Therefore, at any given point, one must be able to tell whether or not a node has been compromised. Moreover, it is crucial to understand how the compromise of a particular node or set of nodes may affect the network operation. In this thesis, we present a framework to monitor the health and integrity of WSNs that allows us to detect compromised devices and comprehend how they might impact a network’s performance. We start by investigating the use of attestation to identify malicious nodes and advance the state of the art by exploring limitations of existing mechanisms. Firstly, we tackle effectiveness and scalability by combining attestation with measurements inspection and show that the right combination of both schemes can achieve high accuracy whilst significantly reducing power consumption. Secondly, we propose a novel stochastic software-based attestation approach that relaxes a fundamental and yet overlooked assumption made in the literature significantly reducing time and energy consumption while improving the detection rate of honest devices. Lastly, we propose a mathematical model to represent the health of a WSN according to its abilities to perform its functions. Our model combines the knowledge regarding compromised nodes with additional information that quantifies the importance of each node. In this context, we propose a new centrality measure and analyse how well existing metrics can rank the importance each sensor node has on the network connectivity. We demonstrate that while no measure is invariably better, our proposed metric outperforms the others in the vast majority of cases.Open Acces

Detecting Software Attacks on Embedded IoT Devices

Internet of Things (IoT) applications are being rapidly deployed in the context of smart homes, automotive vehicles, smart factories, and many more. In these applications, embedded devices are widely used as sensors, actuators, or edge nodes. The embedded devices operate distinctively on a task or interact with each other to collectively perform certain tasks. In general, increase in Internet-connected things has made embedded devices an attractive target for various cyber attacks, where an attacker gains access and control remote devices for malicious activities. These IoT devices could be exploited by an attacker to compromise the security of victim’s platform without requiring any physical hardware access. In order to detect such software attacks and ensure a reliable and trustworthy IoT application, it is crucial to verify that a device is not compromised by malicious software, and also assert correct execution of the program. In the literature, solutions based on remote attestation, anomaly detection, control-flow and data-flow integrity have been proposed to detect software attacks. However, these solutions have limited applicability in terms of target deployments and attack detection, which we inspect thoroughly. In this dissertation, we propose three solutions to detect software attacks on embedded IoT devices. In particular, we first propose SWARNA, which uses remote attestation to verify a large network of embedded devices and ensure that the application software on the device is not tampered. Verifying the integrity of a software preserves the static properties of a device. To secure the devices from various software attacks, it is imperative to also ensure that the runtime execution of a program is as expected. Therefore, we focus extensively on detecting memory corruption attacks that may occur during the program execution. Furthermore, we propose, SPADE and OPADE, secure program anomaly detection that runs on embedded IoT devices and use deep learning, and machine learning algorithms respectively to detect various runtime software attacks. We evaluate and analyse all the proposed solutions on real embedded hardware and IoT testbeds. We also perform a thorough security analysis to show how the proposed solutions can detect various software attacks