Approximate computing design exploration through data lifetime metrics

When designing an approximate computing system, the selection of the resources to modify is key. It is important that the error introduced in the system remains reasonable, but the size of the design exploration space can make this extremely difficult. In this paper, we propose to exploit a new metric for this selection: data lifetime. The concept comes from the field of reliability, where it can guide selective hardening: the more often a resource handles "live" data, the more critical it be-comes, the more important it will be to protect it. In this paper, we propose to use this same metric in a new way: identify the less critical resources as approximation targets in order to minimize the impact on the global system behavior and there-fore decrease the impact of approximation while increasing gains on other criteria

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin

UNI Magazine, issue 01, 2019

Inside This Issue: --Trans-for-ma-tion --I Don\u27t Belong Here --Roots --UNI Impact --On Campus --Alumni Highlights --Campus News --Class Noteshttps://scholarworks.uni.edu/unimagazinenews/1000/thumbnail.jp

Fooling an Unbounded Adversary with a Short Key, Repeatedly: The Honey Encryption Perspective

This article is motivated by the classical results from Shannon that put the simple and elegant one-time pad away from practice: key length has to be as large as message length and the same key could not be used more than once. In particular, we consider encryption algorithm to be defined relative to specific message distributions in order to trade for unconditional security. Such a notion named honey encryption (HE) was originally proposed for achieving best possible security for password based encryption where secrete key may have very small amount of entropy. Exploring message distributions as in HE indeed helps circumvent the classical restrictions on secret keys.We give a new and very simple honey encryption scheme satisfying the unconditional semantic security (for the targeted message distribution) in the standard model (all previous constructions are in the random oracle model, even for message recovery security only). Our new construction can be paired with an extremely simple yet "tighter" analysis, while all previous analyses (even for message recovery security only) were fairly complicated and require stronger assumptions. We also show a concrete instantiation further enables the secret key to be used for encrypting multiple messages

Towards Multidimensional Verification: Where Functional Meets Non-Functional

Trends in advanced electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends towards the multidimensional verification concept. The concept is motivated by an example for the functional and power verification dimensions.Comment: 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC

Stochastic Modeling Of Decarbonizing Strategy, Policy, And Market-induced Incentives For The US Electricity Sector

In line with the global pursuit of achieving net-zero carbon emissions, integrating carbon capture and storage (CCS) and renewable energy (RE) technologies is important in power production. This study evaluates the profitability of CCS and RE technologies as alternative ways of achieving climate change goals. While past research focused on costs, technological advancements, and capture methods, there is a need for more studies on assessing the financial feasibility of these climate change solutions under uncertain conditions, alongside specific performance goals and strategies to entice power producers. Using a comprehensive framework featuring deterministic and stochastic modeling approaches, this research explores the impact of policy and market incentives on CCS and RE investments within the U.S. power sector. It analyzes the interactions of variables such as market uncertainties, technical factors, and policy dynamics on the financial viability of adopting CCS and RE for targeted CO2 reductions. The results reveal that, given the status quo of policies, RE and CCS exhibit annualized net present values of $4.62 and$1.76, respectively, for each metric ton (MT) of CO2. Uncertainties in policy incentives emerge as a primary hindrance to achieving cost-effective carbon reduction mandates using CCS, while changes in the green electricity price premium cause high variability in RE returns. The study proposes a hypothetical market, featuring the sale of CCS-linked net-zero electricity at a distinctive premium price of $0.03/kWh. The study\u27s findings underscore the importance of both policy and market incentives to enable power producers to deploy carbon management technologies at a large scale

Metal binding to the dynamic cytoplasmic domain of the cation diffusion facilitator (CDF) protein MamM induces a 'locked-in' configuration

Cation diffusion facilitator (CDF) proteins are a conserved family of transmembrane transporters that ensure cellular homeostasis of divalent transition metal cations. Metal cations bind to CDF protein's cytoplasmic C-terminal domain (CTD), leading to closure from its apo open V-shaped dimer to a tighter packed structure, followed by a conformational change of the transmembrane domain thus enabling transport of the metal cation. By implementing a comprehensive range of biochemical and biophysical methods, we studied the molecular mechanism of metal binding to the magnetotactic bacterial CDF protein MamM CTD. Our results reveal that the CTD is rather dynamic in its apo form, and that two dependent metal binding sites, a single central binding site and two symmetrical, peripheral sites, are available for metal binding. However, only cation binding to the peripheral sites leads to conformational changes that lock the protein in a compact state. Thus, this work reveals how metal binding is regulating the sequential uptakes of metal cations by MamM, and extends our understanding of the complex regulation mechanism of CDF proteins. This article is protected by copyright. All rights reserved

A Survey on Security Threats and Countermeasures in IEEE Test Standards

International audienceEditor's note: Test infrastructure has been shown to be a portal for hackers. This article reviews the threats and countermeasures for IEEE test infrastructure standards

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively