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A 3D simulation of optical photogenreation, electrical characteristics, and thermal/heat distribution across the structure of a perovskite solar cell with a reduced graphene oxide (RGO) contact is presented. COMSOL Multiphysics package has been used to solve the coupled optical-electrical-thermal modules for this hybrid cell where the RGO added as the bottom electrode instead of a conventional metallic contact to enhance the heat dissipation towards a higher device stability. The Wave Optic module, Semiconductor module, and Heat Transfer in Solid module were coupled and solved for the proper input parameter values taken from relevant literature. The optical photogeneration, current-voltage characteristics, electric-field and the thermal maps of the cell are presented. The RGO contact doesn’t significantly impact on the optical and electrical output of the cell, but it accelerates the heat dissipation. The heat is mainly generated across the cell from the light absorption, Shockley-Read-Hall non-radiative recombination, and Joule heating. Compared to the cell with the Au electrode, the RGO contacted cell is showing a minimized heat accumulation and gradient at the bottom junction of the RGO/Spiro interface which promises a thermal stability of the cell. The nan-radiative and joule heat distribution also show a moderated density for the RGO contacted cell which are assigned to the high heat conductivity of the RGO layer compared to traditional metallic electrodes. Our simulations results are of the rarely presented thermal simulations for such devices and prove the superiority of graphene over plane metallic contacts for heat dissipation and thermodynamic aspect of a solar cell.

The heat dissipation has been rarely investigated in solar cells although it has a significant impact on their performance and reliability. For the first time, an extended three-dimensional (3-D) simulation of heat distribution in perovskite solar cells is presented here. We use COMSOL Multiphysics to investigate the temperature distribution in conventional perovskite solar cells through a coupled optical–electrical–thermal modules. Wave optics module, semiconductor module, and heat transfer in solid module are coupled in COMSOL Multiphysics package to perform the simulation in 3-D wizard. The electrical behavior, optical absorption, and heat conduction or convection are considered to gain insight into heat dissipation across the cell. The simulation results suggest that the heat produced in the cell is best dissipated from the metallic contact where the PbI $_2$ defect forms because of oxidation or decomposition of the perovskite layer at moisture exposure. The generated heat varies significantly from the front FTO contact to bottom metallic electrode. The more heat dissipation and accumulation is observed at the junction and electrode sides too. In our simulations, we consider the Joule heating, nonradiative recombination heating, and heat flux in every layer of the cell and calculate the carrier's concentration, electric field distribution, Joule heating, Shockley–Read–Hall heating, total heat flux, and temperature distribution across the solar cell structure. The simulations reveal that the metallic contact must be selected as a highly heat conductive material in order to accelerate the heat dissipation on the bottom of the cell and to enhance the cell reliability against temperature increase under normal operation.

Automatic vulnerability diagnosis can help security analysts identify and, therefore, quickly patch disclosed vulnerabilities. The vulnerability localization problem is to automatically find a program point at which the "root cause" of the bug can be fixed. This paper employs a statistical localization approach to analyze a given exploit. Our main technical contribution is a novel procedure to systematically construct a test-suite which enables high-fidelity localization. We build our techniques in a tool called VulnLoc which automatically pinpoints vulnerability locations, given just one exploit, with high accuracy. VulnLoc does not make any assumptions about the availability of source code, test suites, or specialized knowledge of the type of vulnerability. It identifies actionable locations in its Top-5 outputs, where a correct patch can be applied, for about 88% of 43 CVEs arising in large real-world applications we study. These include 6 different classes of security flaws. Our results highlight the under-explored power of statistical analyses, when combined with suitable test-generation techniques.

Sustainability is becoming more and more important as a decision attribute in the manufacturing environment. However, quantitative metrics for all the aspects of the triple bottom line are difficult to assess. Within the present paper, the sustainability metrics are considered in tandem with other traditional manufacturing metrics such as time, flexibility, and quality and a novel framework is presented that integrates information and requirements from Computer-Aided Technologies (CAx) systems. A novel tool is outlined for considering a number of key performance indicators related to the triple bottom line when deciding the most appropriate process route. The implemented system allows the assessment of alternative process plans considering the market demands and available resources.

Neural networks are increasingly employed in safety-critical domains. This has prompted interest in verifying or certifying logically encoded properties of neural networks. Prior work has largely focused on checking existential properties, wherein the goal is to check whether there exists any input that violates a given property of interest. However, neural network training is a stochastic process, and many questions arising in their analysis require probabilistic and quantitative reasoning, i.e., estimating how many inputs satisfy a given property. To this end, our paper proposes a novel and principled framework to quantitative verification of logical properties specified over neural networks. Our framework is the first to provide PAC-style soundness guarantees, in that its quantitative estimates are within a controllable and bounded error from the true count. We instantiate our algorithmic framework by building a prototype tool called NPAQ that enables checking rich properties over binarized neural networks. We show how emerging security analyses can utilize our framework in 3 concrete point applications: quantifying robustness to adversarial inputs, efficacy of trojan attacks, and fairness/bias of given neural networks.

The peer-assisted CDN is a new content distribution paradigm supported by CDNs (e.g., Akamai), which enables clients to cache and distribute web content on behalf of a website. Peer-assisted CDNs bring significant bandwidth savings to website operators and reduce network latency for users. In this work, we show that the current designs of peer-assisted CDNs expose clients to privacy-invasive attacks, enabling one client to infer the set of browsed resources of another client. To alleviate this, we propose an anonymous peer-assisted CDN (APAC), which employs content delivery while providing initiator anonymity (i.e., hiding who sends the resource request) and responder anonymity (i.e., hiding who responds to the request) for peers. APAC can be a web service, compatible with current browsers and requiring no client-side changes. Our anonymity analysis shows that our APAC design can preserve a higher level of anonymity than state-of-the-art peer-assisted CDNs. In addition, our evaluation demonstrates that APAC can achieve desired performance gains.

Bitcoin provides only pseudo-anonymous transactions, which can be exploited to link payers and payees -- defeating the goal of anonymous payments. To thwart such attacks, several Bitcoin mixers have been proposed, with the objective of providing unlinkability between payers and payees. However, existing Bitcoin mixers can be regarded as either insecure or inefficient. We present Obscuro, a highly efficient and secure Bitcoin mixer that utilizes trusted execution environments (TEEs). With the TEE's confidentiality and integrity guarantees for code and data, our mixer design ensures the correct mixing operations and the protection of sensitive data (i.e., private keys and mixing logs), ruling out coin theft and address linking attacks by a malicious service provider. Yet, the TEE-based implementation does not prevent the manipulation of inputs (e.g., deposit submissions, blockchain feeds) to the mixer, hence Obscuro is designed to overcome such limitations: it (1) offers an indirect deposit mechanism to prevent a malicious service provider from rejecting benign user deposits; and (2) scrutinizes blockchain feeds to prevent deposits from being mixed more than once (thus degrading anonymity) while being eclipsed from the main blockchain branch. In addition, Obscuro provides several unique anonymity features (e.g., minimum mixing set size guarantee, resistant to dropping user deposits) that are not available in existing centralized and decentralized mixers. Our prototype of Obscuro is built using Intel SGX and we demonstrate its effectiveness in Bitcoin Testnet. Our implementation mixes 1000 inputs in just 6.49 seconds, which vastly outperforms all of the existing decentralized mixers.

Smart contracts---stateful executable objects hosted on blockchains like Ethereum---carry billions of dollars worth of coins and cannot be updated once deployed. We present a new systematic characterization of a class of trace vulnerabilities, which result from analyzing multiple invocations of a contract over its lifetime. We focus attention on three example properties of such trace vulnerabilities: finding contracts that either lock funds indefinitely, leak them carelessly to arbitrary users, or can be killed by anyone. We implemented MAIAN, the first tool for precisely specifying and reasoning about trace properties, which employs inter-procedural symbolic analysis and concrete validator for exhibiting real exploits. Our analysis of nearly one million contracts flags 34,200 (2,365 distinct) contracts vulnerable, in 10 seconds per contract. On a subset of3,759 contracts which we sampled for concrete validation and manual analysis, we reproduce real exploits at a true positive rate of 89%, yielding exploits for3,686 contracts. Our tool finds exploits for the infamous Parity bug that indirectly locked 200 million dollars worth in Ether, which previous analyses failed to capture.

Many blockchain consensus protocols have been proposed recently to scale the throughput of a blockchain with available bandwidth. However, these protocols are becoming increasingly complex, making it more and more difficult to produce proofs of their security guarantees. We propose a novel permissionless blockchain protocol OHIE which explicitly aims for simplicity. OHIE composes as many parallel instances of Bitcoin's original (and simple) backbone protocol as needed to achieve excellent throughput. We formally prove the safety and liveness properties of OHIE. We demonstrate its performance with a prototype implementation and large-scale experiments with up to 50,000 nodes. In our experiments, OHIE achieves linear scaling with available bandwidth, providing about 4-10 Mbps transaction throughput (under 8-20 Mbps per-node available bandwidth configurations) and at least about 20x better decentralization over prior works.

In a sybil attack , an adversary creates many fake identities/nodes and have them join the system. Computational puzzles have long been investigated as a possible sybil defense: nodes that fail to solve the puzzle in time will no longer be accepted by other nodes. However, a malicious node can behave in such a way that it is accepted by some honest nodes but not other honest nodes. This results in different honest nodes having different views on which set of nodes constitute the system. Such view divergence , unfortunately, breaks the overarching assumption required by many existing security protocols. Partly spurred by the growing popularity of Bitcoin, researchers have recently formalized the above view divergence problem and proposed interesting solutions (which we call view reconciliation protocols ). All existing view reconciliation protocols so far have a similar $\Theta (N)$ time complexity, with $N$ being the number of honest nodes in the system. As this paper’s main contribution, we propose a novel view reconciliation protocol whose time complexity is only $\Theta ({\ln N}/{\ln \ln N})$ . To achieve such an exponential improvement, we aggressively exploit randomization. The hidden constant factor in the asymptotic complexity of our protocol, however, is considerably larger than in previous protocols. Concrete numerical comparisons show that our protocol is more suitable for large-scale systems, while existing protocols are better for smaller-scale systems.

In the last decade, several permissionless proof-of-work blockchain protocols have focused on scalability. Since these protocols are very difficult to change once deployed, their robustness and security are of paramount importance. This paper summarizes the desired end properties of blockchain consensus protocols and sheds light on the critical role of theoretical analyses of their design. We summarize the major paradigms in prior constructions and discuss open issues in this space.

In cloud computing, where clients are billed based on the consumed resources for outsourced tasks, both the cloud providers and the clients have the incentive to manipulate claims about resource usage. Both desire an accurate and verifiable resource accounting system, which is neutral and can be trusted to refute any disputes. In this work, we present VeriCount—a verifiable resource accounting system coupled with refutable billing support for Linux container-based applications. To protect VeriCount logic, we propose a novel approach called self-accounting that combines hardware-based isolation guarantees from trusted computing mechanisms and software fault isolation techniques. The self-accounting engine in VeriCount leverages security features present in trusted computing solutions, such as Intel SGX, to measure user CPU time, memory, I/O bytes and network bandwidth while simultaneously detecting resource usage inflation attacks. We claim three main results. First, VeriCount incurs an average performance overhead of 3.62% and 16.03% over non-accounting but SGX-compatible applications in hardware and simulation mode respectively. Next, it contributes only an additional 542 lines of code to the trusted computing base. Lastly, it generates highly accurate, fine-grained resource accounting, with no discernible difference to the resource measuring tool available with the OS.

We investigate a family of bugs in blockchain-based smart contracts, which we call event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contract events, i.e., calls of its functions on the blockchain, and enable potential exploits of millions of USD worth of Ether. Known examples of such bugs and prior techniques to detect them have been restricted to a small number of event orderings, typicall 1 or 2. Our work provides a new formulation of this general class of EO bugs as finding concurrency properties arising in long permutations of such events. The technical challenge in detecting our formulation of EO bugs is the inherent combinatorial blowup in path and state space analysis, even for simple contracts. We propose the first use of partial-order reduction techniques, using happen-before relations extracted automatically for contracts, along with several other optimizations built on a dynamic symbolic execution technique. We build an automatic tool called ETHRACER that requires no hints from users and runs directly on Ethereum bytecode. It flags 7-11% of over ten thousand contracts analyzed in roughly 18.5 minutes per contract, providing compact event traces that human analysts can run as witnesses. These witnesses are so compact that confirmations require only a few minutes of human effort. Half of the flagged contracts have subtle EO bugs, including in ERC-20 contracts that carry hundreds of millions of dollars worth of Ether. Thus, ETHRACER is effective at detecting a subtle yet dangerous class of bugs which existing tools miss., Comment: 18 pages, 12 figures

Bitcoin and hundreds of other cryptocurrencies employ a consensus protocol called Nakamoto consensus which rewards miners for maintaining a public blockchain. In this paper, we study the security of this protocol with respect to rational miners and show how a minority of the computation power can incentivize the rest of the network to accept a blockchain of the minority’s choice. By deviating from the mining protocol, a mining pool which controls at least 38.2% of the network’s total computational power can, with modest financial capacity, gain mining advantage over honest mining. Such an attack creates a longer valid blockchain by forking the honest blockchain, and the attacker’s blockchain need not disrupt any “legitimate” non-mining transactions present on the honest blockchain. By subverting the consensus protocol, the attacking pool can double-spend money or simply create a blockchain that pays mining rewards to the attacker’s pool. We show that our attacks are easy to encode in any Nakamoto-consensus-based cryptocurrency which supports a scripting language that is sufficiently expressive to encode its own mining puzzles.

Privacy and anonymity are important desiderata in the use of cryptocurrencies. Monero—a privacy centric cryptocurrency has rapidly gained popularity due to its unlinkability and untraceablity guarantees. It has a market capitalization of USD 290M. In this work, we quantify the efficacy of three attacks on Monero’s untraceability guarantee, which promises to make it hard to trace the origin of a received fund, by analyzing its blockchain data. To this end, we develop three attack routines and evaluate them on the Monero blockchain. Our results show that in 88% of cases, the origin of the funds can be easily determined with certainty. Moreover, we have compelling evidence that two of the attack routines also extend to Monero RingCTs—the second generation Monero that even hides the transaction amount. We further observe that over 98% of the results can in fact be obtained by a simple temporal analysis. In light of our findings, we discuss mitigations to strengthen Monero against these attacks. We shared our findings with the Monero development team and the general community. This has resulted into several discussions and proposals for fixes.

Intel SGX, a new security capability in emerging CPUs, allows user-level application code to execute in hardware isolated enclaves. Enclave memory is isolated from all other software on the system, even from the privileged OS or hypervisor. While being a promising hardware-rooted building block, enclaves have severely limited capabilities, such as no native access to system calls and standard OS abstractions. These OS abstractions are used ubiquitously in real-world applications. In this paper, we present a new system called PANOPLY which bridges the gap between the SGX-native abstractions and the standard OS abstractions which feature-rich, commodity Linux applications require. PANOPLY provides a new abstraction called a micro-container (or a “micron”), which is a unit of code and data isolated in SGX enclaves. Microns expose the standard POSIX abstractions to application logic, including access to filesystems, network, multi-threading, multi-processing and thread synchronization primitives. Further, PANOPLY enforces a strong integrity property for the inter-enclave interactions, ensuring that the execution of the application follows the legitimate control and data-flow even if the OS misbehaves. Thus, commodity Linux applications can enhance security by splitting their application logic in one or more microns, or by importing micron-libraries, with little effort. In contrast to previous systems that enable comparable richness, PANOPLY offers two orders of magnitude lower TCB (about 20 KLOC in total), more than half of which is boiler-plate and can be automatically verified in the future. We demonstrate how PANOPLY enables much stronger security in 4 real-world applications — including Tor, OpenSSL, and web services — which can base security on hardware-root of trust.

Deep learning in a collaborative setting is emerging as a corner-stone of many upcoming applications, wherein untrusted users collaborate to generate more accurate models. From the security perspective, this opens collaborative deep learning to poisoning attacks, wherein adversarial users deliberately alter their inputs to mis-train the model. These attacks are known for machine learning systems in general, but their impact on new deep learning systems is not well-established. We investigate the setting of indirect collaborative deep learning --- a form of practical deep learning wherein users submit masked features rather than direct data. Indirect collaborative deep learning is preferred over direct, because it distributes the cost of computation and can be made privacy-preserving. In this paper, we study the susceptibility of collaborative deep learning systems to adversarial poisoning attacks. Specifically, we obtain the following empirical results on 2 popular datasets for handwritten images (MNIST) and traffic signs (GTSRB) used in auto-driving cars. For collaborative deep learning systems, we demonstrate that the attacks have 99% success rate for misclassifying specific target data while poisoning only 10% of the entire training dataset. As a defense, we propose Auror, a system that detects malicious users and generates an accurate model. The accuracy under the deployed defense on practical datasets is nearly unchanged when operating in the absence of attacks. The accuracy of a model trained using Auror drops by only 3% even when 30% of all the users are adversarial. Auror provides a strong guarantee against evasion; if the attacker tries to evade, its attack effectiveness is bounded.

Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol --- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3--7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to $1, 600$ nodes confirm ELASTICO's theoretical scaling properties.

Process-based isolation, suggested by several research prototypes, is a cornerstone of modern browser security architectures. Google Chrome is the first commercial browser that adopts this architecture. Unlike several research prototypes, Chrome's process-based design does not isolate different web origins, but primarily promises to protect "the local system" from "the web". However, as billions of users now use web-based cloud services (e.g., Dropbox and Google Drive), which are integrated into the local system, the premise that browsers can effectively isolate the web from the local system has become questionable. In this paper, we argue that, if the process-based isolation disregards the same-origin policy as one of its goals, then its promise of maintaining the "web/local system (local)" separation is doubtful. Specifically, we show that existing memory vulnerabilities in Chrome's renderer can be used as a stepping-stone to drop executables/scripts in the local file system, install unwanted applications and misuse system sensors. These attacks are purely data-oriented and do not alter any control flow or import foreign code. Thus, such attacks bypass binary-level protection mechanisms, including ASLR and in-memory partitioning. Finally, we discuss various full defenses and present a possible way to mitigate the attacks presented.

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins. In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

New hardware primitives such as Intel SGX secure a user-level process in presence of an untrusted or compromised OS. Such "enclaved execution" systems are vulnerable to several side-channels, one of which is the page fault channel. In this paper, we show that the page fault side-channel has sufficient channel capacity to extract bits of encryption keys from commodity implementations of cryptographic routines in OpenSSL and Libgcrypt -- leaking 27% on average and up to 100% of the secret bits in many case-studies. To mitigate this, we propose a software-only defense that masks page fault patterns by determinising the program's memory access behavior. We show that such a technique can be built into a compiler, and implement it for a subset of C which is sufficient to handle the cryptographic routines we study. This defense when implemented generically can have significant overhead of up to 4000X, but with help of developer-assisted compiler optimizations, the overhead reduces to at most 29.22% in our case studies. Finally, we discuss scope for hardware-assisted defenses, and show one solution that can reduce overheads to 6.77% with support from hardware changes.

As control-flow hijacking defenses gain adoption, it is important to understand the remaining capabilities of adversaries via memory exploits. Non-control data exploits are used to mount information leakage attacks or privilege escalation attacks program memory. Compared to control-flow hijacking attacks, such non-control data exploits have limited expressiveness, however, the question is: what is the real expressive power of non-control data attacks? In this paper we show that such attacks are Turing-complete. We present a systematic technique called data-oriented programming (DOP) to construct expressive non-control data exploits for arbitrary x86 programs. In the experimental evaluation using 9 programs, we identified 7518 data-oriented x86 gadgets and 5052 gadget dispatchers, which are the building blocks for DOP. 8 out of 9 real-world programs have gadgets to simulate arbitrary computations and 2 of them are confirmed to be able to build Turing-complete attacks. We build 3 end-to-end attacks to bypass randomization defenses without leaking addresses, to run a network bot which takes commands from the attacker, and to alter the memory permissions. All the attacks work in the presence of ASLR and DEP, demonstrating how the expressiveness offered by DOP significantly empowers the attacker.

The increasing popularity of blockchain-based cryptocurrencies has made scalability a primary and urgent concern. We analyze how fundamental and circumstantial bottlenecks in Bitcoin limit the ability of its current peer-to-peer overlay network to support substantially higher throughputs and lower latencies. Our results suggest that reparameterization of block size and intervals should be viewed only as a first increment toward achieving next-generation, high-load blockchain protocols, and major advances will additionally require a basic rethinking of technical approaches. We offer a structured perspective on the design space for such approaches. Within this perspective, we enumerate and briefly discuss a number of recently proposed protocol ideas and offer several new ideas and open challenges.