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Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability., Comment: Accepted to IEEE Transactions on Systems, Man, and Cybernetics: Systems. \c{opyright} 2024 IEEE

With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset., Comment: IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, U.S.A, 2024, pp. 1-6

The proliferation of cloud computing technologies has paved the way for deploying networked encrypted control systems, offering high performance, remote accessibility and privacy. However, in scenarios where the control algorithms run on third-party cloud service providers, the control logic might be changed by a malicious agent on the cloud. Consequently, it is imperative to verify the correctness of the control signals received from the cloud. Traditional verification methods, like zero-knowledge proof techniques, are computationally demanding in both proof generation and verification, may require several rounds of interactions between the prover and verifier and, consequently, are inapplicable in realtime control system applications. In this paper, we present a novel computationally inexpensive verifiable computing solution inspired by the probabilistic cut-and-choose approach. The proposed scheme allows the plant's actuator to validate the computations accomplished by the encrypted cloud-based networked controller without compromising the control scheme's performance. We showcase the effectiveness and real-time applicability of the proposed verifiable computation scheme using a remotely controlled Khepera IV differential-drive robot., Comment: Preprint of the manuscript submitted to the IEEE Control Systems Letters (L-CSS)

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency., Comment: Accepted in the IEEE Power and Energy Society General Meeting, 2024

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If the VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works that proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves., Comment: Accepted for publication in IECON 2023; the 49th Annual Conference of the IEEE Industrial Electronics Society (IES)

A diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Considering their key role, these platforms must be properly secured against cyber attacks. In this work, we first explore the core operations/features of leading platforms to design a framework to perform a systematic security evaluation of these platforms. Subsequently, we use our framework to analyze a representative set of 52 IoT management platforms, including 42 web-hosted and 10 locally-deployable platforms. We discover a number of high severity unauthorized access vulnerabilities in 9/52 evaluated IoT management platforms, which could be abused to perform attacks such as remote IoT SIM deactivation, IoT SIM overcharging and IoT device data forgery. More seriously, we also uncover instances of broken authentication in 13/52 platforms, including complete account takeover on 8/52 platforms along with remote code execution on 2/52 platforms. In effect, 17/52 platforms were affected by vulnerabilities that could lead to platform-wide attacks. Overall, vulnerabilities were uncovered in 33 platforms, out of which 28 platforms responded to our responsible disclosure. We were also assigned 11 CVEs and awarded bounty for our findings., Comment: Accepted in IEEE Internet of Things Journal in June 2023

We conduct a large-scale measurement of developers' insecure practices leading to mini-app to super-app authentication bypass, among which hard-coding developer secrets for such authentication is a major contributor. We also analyze the exploitability and security consequences of developer secret leakage in mini-apps by examining individual super-app server-side APIs. We develop an analysis framework for measuring such secret leakage, and primarily analyze 110,993 WeChat mini-apps, and 10,000 Baidu mini-apps (two of the most prominent super-app platforms), along with a few more datasets to test the evolution of developer practices and platform security enforcement over time. We found a large number of WeChat mini-apps (36,425, 32.8%) and a few Baidu mini-apps (112) leak their developer secrets, which can cause severe security and privacy problems for the users and developers of mini-apps. A network attacker who does not even have an account on the super-app platform, can effectively take down a mini-app, send malicious and phishing links to users, and access sensitive information of the mini-app developer and its users. We responsibly disclosed our findings and also put forward potential directions that could be considered to alleviate/eliminate the root causes of developers hard-coding the app secrets in the mini-app's front-end code., Comment: Accepted at RAID 2023: Symposium on Research in Attacks, Intrusions and Defenses

Remotely controlled mobile robots are important examples of Cyber-Physical Systems (CPSs). Recently, these robots are being deployed in many safety critical applications. Therefore, ensuring their cyber-security is of paramount importance. Different control schemes that have been proposed to secure such systems against sophisticated cyber-attacks require the exchange of secret messages between their smart actuators and the remote controller. Thus, these schemes require pre-shared secret keys, or an established Public Key Infrastructure (PKI) that allows for key agreement. Such cryptographic approaches might not always be suitable for the deployment environments of such remotely mobile robots. To address this problem, in this paper, we consider a control theoretic approach for establishing a secret key between the remotely controlled robot and the networked controller without resorting to traditional cryptographic techniques. Our key agreement scheme leverages a nonlinear unknown input observer and an error correction code mechanism to allow the robot to securely agree on a secret key with its remote controller. To validate the proposed scheme, we implement it using a Khepera-IV differential drive robot and evaluate its efficiency and the additional control cost acquired by it. Our experimental results confirm the effectiveness of the proposed key establishment scheme., Comment: This preprint has been submitted to the 2023 IFAC World Congress

Public key infrastructure (PKI) is a certificate-based technology that helps in authenticating systems identities. HTTPS/TLS relies mainly on PKI to minimize fraud over the Internet. Nowadays, websites utilize CDNs to improve user experience, performance, and resilience against cyber attacks. However, combining HTTPS/TLS with CDNs has raised new security challenges. In any PKI system, keeping private keys private is of utmost importance. However, it has become the norm for CDN-powered websites to violate that fundamental assumption. Several solutions have been proposed to make HTTPS CDN-friendly. However, protection of private keys from the very instance of generation; and how they can be made secure against exposure by malicious (CDN) administrators and malware remain unexplored. We utilize trusted execution environments to protect private keys by never exposing them to human operators or untrusted software. We design Blindfold to protect private keys in HTTPS/TLS infrastructures, including CAs, website on-premise servers, and CDNs. We implemented a prototype to assess Blindfold's performance and performed several experiments on both the micro and macro levels. We found that Blindfold slightly outperforms SoftHSM in key generation by 1% while lagging by 0.01% for certificate issuance operations.

The early Universe was characterized by the presence of heavy particles that decoupled at different temperatures leading to different phases of the Universe. This had a consequences on the time evolution of the thermodynamic and the cosmological parameters characterizing each phase of the early Universe. In this study, we derive the analytic expressions of the equations governing the time evolution of these parameters in the early eras of the Universe namely, the radiation era, the quark-gluon plasma era, the hadron era and the mixed era. The parameters under concern include the energy density, the entropy density, the temperature, the pressure in addition to Hubble parameter and the scale factor. Having these expressions allows us to give estimations of the times corresponding to the beginning and ending of each era of the Universe as will be presented in this work., Comment: 23 pages, 4 figures

The Android operating system is currently the most popular mobile operating system in the world. Android is based on Linux and therefore inherits its features including its Inter-Process Communication (IPC) mechanisms. These mechanisms are used by processes to communicate with one another and are extensively used in Android. While Android-specific IPC mechanisms have been studied extensively, Unix domain sockets have not been examined comprehensively, despite playing a crucial role in the IPC of highly privileged system daemons. In this paper, we propose SAUSAGE, an efficient novel static analysis framework to study the security properties of these sockets. SAUSAGE considers access control policies implemented in the Android security model, as well as authentication checks implemented by the daemon binaries. It is a fully static analysis framework, specifically designed to analyze Unix domain socket usage in Android system daemons, at scale. We use this framework to analyze 200 Android images across eight popular smartphone vendors spanning Android versions 7-9. As a result, we uncover multiple access control misconfigurations and insecure authentication checks. Our notable findings include a permission bypass in highly privileged Qualcomm system daemons and an unprotected socket that allows an untrusted app to set the scheduling priority of other processes running on the system, despite the implementation of mandatory SELinux policies. Ultimately, the results of our analysis are worrisome; all vendors except the Android Open Source Project (AOSP) have access control issues, allowing an untrusted app to communicate to highly privileged daemons through Unix domain sockets introduced by hardware manufacturer or vendor customization., Comment: Accepted to EuroS&P 2022

The Open Vote Network is a self-tallying decentralized e-voting protocol suitable for boardroom elections. Currently, it has two Ethereum-based implementations: the first, by McCorry et al., has a scalability issue since all the computations are performed on-chain. The second implementation, by Seifelnasr et al., solves this issue partially by assigning a part of the heavy computations to an off-chain untrusted administrator in a verifiable manner. As a side effect, this second implementation became not dispute-free; there is a need for a tally dispute phase where an observer interrupts the protocol when the administrator cheats, i.e., announces a wrong tally result. In this work, we propose a new smart contract design to tackle the problems in the previous implementations by (i) preforming all the heavy computations off-chain hence achieving higher scalability, and (ii) utilizing zero-knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) to verify the correctness of the off-chain computations, hence maintaining the dispute-free property. To demonstrate the effectiveness of our design, we develop prototype implementations on Ethereum and conduct multiple experiments for different implementation options that show a trade-off between the zk-SNARK proof generation time and the smart contract gas cost, including an implementation in which the smart contract consumes a constant amount of gas independent of the number of voters., Comment: Financial Cryptography and Data Security 2022 (FC 2022) workshops, WTSC'22

Market-based congestion management methods adopt Demand Side Management (DSM) techniques to alleviate congestion in the day-ahead market. Reliance of these methods on the communication layer makes it prone to cyber attacks affecting the security, reliability, and economic operation of the distribution network. In this paper, we focus on Load Altering Attacks that would compromise the operation of market-based congestion management methods. A detection technique is proposed using Long Short-term Memory (LSTM) Recurrent Neural Networks (RNN). IEEE 33 bus system is used as a case study to demonstrate the effectiveness of the proposed technique. An accuracy of 97% was obtained proving the capability of using LSTM-RNN to detect a load altering cyber attack compromising aggregators in the network.

In this study, the authors consider the problem of exchanging secrete messages in cyber-physical systems (CPSs) without resorting to cryptographic solutions. In particular, they consider a CPS where the networked controller wants to send a secrete message to the plant. They show that such a problem can be solved by exploiting a Wyner wiretap-like encoding scheme taking advantage of the closed-loop operations typical of feedback control systems. Specifically, by resorting to the control concept of one-step reachable sets, they first show that a wiretap-like encoding scheme exists whenever there is an asymmetry in the plant model knowledge available to control system (the defender) and to the eavesdropper. The effectiveness of the proposed scheme is confirmed by means of a numerical example. Finally, they conclude the study by presenting open design challenges that can be addressed by the research community to improve, in different directions, the secrete message exchange problem in CPSs

Recently, cloud control systems have gained increasing attention from the research community as a solution to implement networked cyber-physical systems (CPSs). Such an architecture can reduce deployment and maintenance costs albeit at the expense of additional security and privacy concerns. In this paper, first, we discuss state-of-the-art security solutions for cloud control systems and their limitations. Then, we propose a novel control architecture based on Trusted Execution Environments (TEE). We show that such an approach can potentially address major security and privacy issues for cloud-hosted control systems. Finally, we present an implementation setup based on Intel Software Guard Extensions (SGX) and validate its effectiveness on a testbed system.

The increased penetration of Electric Vehicles (EVs) in the transportation sector has increased the requirement of Fast Charging Direct Current (FCDC) stations to meet customer's speedy charging requirements. However, both charging stations and EVs connection to the communication infrastructure as well as the power grid makes it vulnerable to cyber attacks. In this paper, the vulnerability of the EV charging process is initially studied. We then show how a botnet of compromised EVs and FCDC stations can be utilized to launch cyber attacks on the power grid resulting in an increase in the load at a specific time. The effect of such attacks on the distribution network in terms of line congestion and voltage limit violations is investigated. Moreover, the effect of the botnet of the transmission network is also studied., Comment: 5 pages, conference

Coexistence of Human-Type Communications (HTCs) and Machine-Type Communications (MTCs) is inevitable. Ultra-Dense Networks (UDNs) will be efficacious in supporting both types of communications. In a UDN, a massive number of low-power and low-cost Small Cells (SCs) are deployed with density higher than that of the HTC users. In such a scenario, the backhaul capacities constitute an intrinsic bottleneck for the system. Hence, we propose a multiple association scheme where each HTC user associates to and activates multiple SCs to overcome the backhaul capacity constraints. In addition, having more active cells allows for more MTC devices to be supported by the network. Using tools from stochastic geometry, we formulate a novel mathematical framework investigating the performance of the limited-backhaul capacity UDN in terms of Area Spectral Efficiency (ASE) for both HTC and MTC and the density of supported MTC devices. Extensive simulations were conducted to verify the accuracy of the mathematical analysis under different system parameters. Results show the existence of an optimum number of SCs to which an HTC user may connect under backhaul capacity constraints. Besides, the proposed multiple association scheme significantly improves the performance of MTC in terms of both ASE and density of supported devices.

Open access WiFi hotspots are widely deployed in many public places, including restaurants, parks, coffee shops, shopping malls, trains, airports, hotels, and libraries. While these hotspots provide an attractive option to stay connected, they may also track user activities and share user/device information with third-parties, through the use of trackers in their captive portal and landing websites. In this paper, we present a comprehensive privacy analysis of 67 unique public WiFi hotspots located in Montreal, Canada, and shed some light on the web tracking and data collection behaviors of these hotspots. Our study reveals the collection of a significant amount of privacy-sensitive personal data through the use of social login (e.g., Facebook and Google) and registration forms, and many instances of tracking activities, sometimes even before the user accepts the hotspot's privacy and terms of service policies. Most hotspots use persistent third-party tracking cookies within their captive portal site; these cookies can be used to follow the user's browsing behavior long after the user leaves the hotspots, e.g., up to 20 years. Additionally, several hotspots explicitly share (sometimes via HTTP) the collected personal and unique device information with many third-party tracking domains.

The wide deployment of tokens for digital assets on top of Ethereum implies the need for powerful trading platforms. Vickrey auctions have been known to determine the real market price of items as bidders are motivated to submit their own monetary valuations without leaking their information to the competitors. Recent constructions have utilized various cryptographic protocols such as ZKP and MPC, however, these approaches either are partially privacy-preserving or require complex computations with several rounds. In this paper, we overcome these limits by presenting Trustee as a Vickrey auction on Ethereum which fully preserves bids' privacy at relatively much lower fees. Trustee consists of three components: a front-end smart contract deployed on Ethereum, an Intel SGX enclave, and a relay to redirect messages between them. Initially, the enclave generates an Ethereum account and ECDH key-pair. Subsequently, the relay publishes the account's address and ECDH public key on the smart contract. As a prerequisite, bidders are encouraged to verify the authenticity and security of Trustee by using the SGX remote attestation service. To participate in the auction, bidders utilize the ECDH public key to encrypt their bids and submit them to the smart contract. Once the bidding interval is closed, the relay retrieves the encrypted bids and feeds them to the enclave that autonomously generates a signed transaction indicating the auction winner. Finally, the relay submits the transaction to the smart contract which verifies the transaction's authenticity and the parameters' consistency before accepting the claimed auction winner. As part of our contributions, we have made a prototype for Trustee available on Github for the community to review and inspect it. Additionally, we analyze the security features of Trustee and report on the transactions' gas cost incurred on Trustee smart contract., Comment: Presented at Financial Cryptography and Data Security 2019, 3rd Workshop on Trusted Smart Contracts

Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., malware detection) and meeting legal requirements. To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle TLS proxy, by acting as the intended web server to a requesting client (e.g., a browser), and acting as the client to the outside web server. As such, the TLS proxy must implement both a TLS client and a server, and handle a large amount of traffic, preferably, in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be, at least, as secure as a modern, up-to-date web browser, and a properly configured web server. As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. To analyze TLS security of network appliances, we develop a comprehensive framework, by combining and extending tests from existing work on client-end and network-based interception studies. We analyze thirteen representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions), and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely-used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.

Smart toys have captured an increasing share of the toy market, and are growing ubiquitous in households with children. Smart toys are a subset of Internet of Things (IoT) devices, containing sensors, actuators, and/or artificial intelligence capabilities. They frequently have internet connectivity, directly or indirectly through companion apps, and collect information about their users and environments. Recent studies have found security flaws in many smart toys that have led to serious privacy leaks, or allowed tracking a child's physical location. Some well-publicized discoveries of this nature have prompted actions from governments around the world to ban some of these toys. Compared to other IoT devices, smart toys pose unique risks because of their easily-vulnerable user base, and our work is intended to define these risks and assess a subset of toys against them. We provide a classification of threats specific to smart toys in order to unite and complement existing adhoc analyses, and help comprehensive evaluation of other smart toys. Our threat classification framework addresses the potential security and privacy flaws that can lead to leakage of private information or allow an adversary to control the toy to lure, harm, or distress a child. Using this framework, we perform a thorough experimental analysis of eleven smart toys and their companion apps. Our systematic analysis has uncovered that several current toys still expose children to multiple threats for attackers with physical, nearby, or remote access to the toy., Comment: IEEE Internet of Things Journal, accepted Oct 2018 (early access URL: https://ieeexplore.ieee.org/document/8502818)