1,232 research outputs found
On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications
Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified
If a Machine Could Talk, We Would Not Understand It: Canadian Innovation and the Copyright Actâs TPM Interoperability Framework
This analysis examines the legal implications of technological protection measures (ââTPMsâ) under Canadaâs Copyright Act. Through embedded computing systems and proprietary interfaces, TPMs are being used by original equipment manufacturers (ââOEMsâ) of agricultural equipment to preclude reverse engineering and follow-on innovation. This has anti-competitive effects on Canadaâs ââshortlineâ agricultural equipment industry, which produces add-on or peripheral equipment used with OEM machinery. This requires interoperability between the interfaces, data formats, and physical connectors, which are often the subject of TPM control. Exceptions under the Act have provided little assistance to the shortline industry. The research question posed by this analysis is: how does the CanadianCopyright Actâs protection for TPMs and its interoperability exception impact follow-on innovation in secondary markets? Canadaâs protection for TPMs and its interoperability exception is inadequate for protecting follow-on innovation in relation to computerized machinery and embedded systems. This is due to the Actâs broad protection for TPMs, yet limited conceptualization of interoperability as a process that exists only between two ââcomputer programsâ. In legally protecting TPMs which safeguard uncopyrightable processes, data formats and interfaces, the Actâs interoperability exception fails to address the need to access subjects of TPM protection that extend beyond computer programs. This results in an asymmetry of protection and renders the interoperability exception inadequate. This article proposes enacting regulations under the Act to provide new exceptions and limitations to TPM protections which would enable shortline innovation. Both the Copyright Act and the Canada-United States-Mexico Agreement envision such additional TPM exceptions where the effect of protection has adverse effects on competition in a secondary market. In exploring a path forward for Canadaâs shortline industry, the article then examines approaches taken in the United States and France to illustrate potential avenues for TPM regulation in Canada
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.
DeepMem: ML Models as storage channels and their (mis-)applications
Machine learning (ML) models are overparameterized to support generality and
avoid overfitting. Prior works have shown that these additional parameters can
be used for both malicious (e.g., hiding a model covertly within a trained
model) and beneficial purposes (e.g., watermarking a model). In this paper, we
propose a novel information theoretic perspective of the problem; we consider
the ML model as a storage channel with a capacity that increases with
overparameterization. Specifically, we consider a sender that embeds arbitrary
information in the model at training time, which can be extracted by a receiver
with a black-box access to the deployed model. We derive an upper bound on the
capacity of the channel based on the number of available parameters. We then
explore black-box write and read primitives that allow the attacker to: (i)
store data in an optimized way within the model by augmenting the training data
at the transmitter side, and (ii) to read it by querying the model after it is
deployed. We also analyze the detectability of the writing primitive and
consider a new version of the problem which takes information storage
covertness into account. Specifically, to obtain storage covertness, we
introduce a new constraint such that the data augmentation used for the write
primitives minimizes the distribution shift with the initial (baseline task)
distribution. This constraint introduces a level of "interference" with the
initial task, thereby limiting the channel's effective capacity. Therefore, we
develop optimizations to improve the capacity in this case, including a novel
ML-specific substitution based error correction protocol. We believe that the
proposed modeling of the problem offers new tools to better understand and
mitigate potential vulnerabilities of ML, especially in the context of
increasingly large models
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
The most essential component of every Distributed Ledger Technology (DLT) is
the Consensus Algorithm (CA), which enables users to reach a consensus in a
decentralized and distributed manner. Numerous CA exist, but their viability
for particular applications varies, making their trade-offs a crucial factor to
consider when implementing DLT in a specific field. This article provided a
comprehensive analysis of the various consensus algorithms used in distributed
ledger technologies (DLT) and blockchain networks. We cover an extensive array
of thirty consensus algorithms. Eleven attributes including hardware
requirements, pre-trust level, tolerance level, and more, were used to generate
a series of comparison tables evaluating these consensus algorithms. In
addition, we discuss DLT classifications, the categories of certain consensus
algorithms, and provide examples of authentication-focused and
data-storage-focused DLTs. In addition, we analyze the pros and cons of
particular consensus algorithms, such as Nominated Proof of Stake (NPoS),
Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the
applicability of these consensus algorithms to various Cyber Physical System
(CPS) use cases, including supply chain management, intelligent transportation
systems, and smart healthcare.Comment: 50 pages, 20 figure
Doing Research. Wissenschaftspraktiken zwischen Positionierung und Suchanfrage
Forschung wird zunehmend aus Sicht ihrer Ergebnisse gedacht - nicht zuletzt aufgrund der UmwĂ€lzungen im System Wissensschaft. Der Band lenkt den Fokus jedoch auf diejenigen Prozesse, die Forschungsergebnisse erst ermöglichen und Wissenschaft konturieren. Dabei ist der Titel Doing Research als Verweis darauf zu verstehen, dass forschendes Handeln von spezifischen Positionierungen, partiellen Perspektiven und Suchbewegungen geformt ist. So knĂŒpfen alle Beitragenden auf reflexive Weise an ihre jeweiligen Forschungspraktiken an. Ausgangspunkt sind AbkĂŒrzungen - die vermeintlich kleinsten Einheiten wissenschaftlicher Aushandlung und VerstĂ€ndigung. Der in den Erziehungs-, Sozial-, Medien- und Kunstwissenschaften verankerte Band zeichnet ein vieldimensionales Bild gegenwĂ€rtigen Forschens mit transdisziplinĂ€ren AnknĂŒpfungspunkten zwischen DigitalitĂ€t und Bildung. (DIPF/Orig.
Robust Watermarking Using FFT and Cordic QR Techniques
Digital media sharing and access in todayâs world of the internet is very frequent for every user. The management of digital rights may come into threat easily as the accessibility of data through the internet become wide. Sharing digital information under security procedures can be easily compromised due to the various vulnerabilities floating over the internet. Existing research has been tied to protecting internet channels to ensure the safety of digital data. Researchers have investigated various encryption techniques to prevent digital rights management but certain challenges including external potential attacks cannot be avoided that may give unauthorized access to digital media. The proposed model endorsed the concept of watermarking in digital data to uplift media security and ensure digital rights management. The system provides an efficient procedure to conduct over-watermarking in digital audio signals and confirm the avoidance of ownership of the host data. The proposed technique uses a watermark picture as a signature that has been initially encrypted with Arnold's cat map and cyclic encoding before being embedded. The upper triangular R-matrix component of the energy band was then created by using the Fast Fourier transform and Cordic QR procedures to the host audio stream. Using PN random sequences, the encrypted watermarking image has been embedded in the host audio component of the R-matrix. The same procedure has been applied to extract the watermark image from the watermarked audio. The proposed model evaluates the quality of the watermarked audio and extracted watermark image. The average PSNR of the watermarked audio is found to be 37.01 dB. It has also been seen that the average PSNR, Normal cross-correlation, BER, SSMI (structure similarity index matric) value for the extracted watermark image is found to be 96.30 dB, 0.9042 units, 0.1033 units, and 0.9836 units respectively. Further, the model has been tested using various attacks to check its robustness. After applying attacks such as noising, filtering, cropping, and resampling on the watermarked audio, the watermark image has been extricated and its quality has been checked under the standard parameters. It has been found that the quality of the recovered watermark image satisfying enough to justify the digital ownership of the host audio. Hence, the proposed watermarking model attains a perfect balance between imperceptibility, payload, and robustness
Physical layer authenticated image encryption for Iot network based on biometric chaotic signature for MPFrFT OFDM system
In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transformâOrthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of (Formula presented.). The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application. © 2023 by the authors
Adversarial Deep Learning and Security with a Hardware Perspective
Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research
Deciphering the Nexus: Blockchain-Smart Contracts and Their Transformative Potential in the Construction Industry
The construction industry, characterized by its intricate processes and extensive stakeholder networks, stands at the cusp of a digital revolution. The adoption of blockchain-smart contract (BCSC) technology is at the heart of this transformation. This research delves deep into the BCSC within the construction arena to provide comprehensive insight into its probable applications, inherent challenges, and potential future trajectories. Leveraging the PRISMA analysis technique, a curated collection of relevant academic research articles was assembled, shedding light on the existing body of knowledge regarding the application of BCSC technology in construction. The authors developed an innovative user interface tool customized to automatically analyze Excel files exported from Scopus and Science Direct databases to ensure a rigorous approach. Preliminary findings highlight the existing gaps between the theoretical potential of blockchain and its tangible implementation in the construction domain. This study consolidates existing literature and emphasizes the critical domains and key parameters that future studies should address. The paper paves the way for innovative breakthroughs by pinpointing these gaps, pushing the boundaries of how blockchain and smart contracts might reshape the construction industry\u27s future landscape
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