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A Review of Confidentiality Threats Against Embedded Neural Network Models [article]

Raphaël Joud, Pierre-Alain Moellic, Rémi Bernhard, Jean-Baptiste Rigaud
2021 arXiv   pre-print
In this review, we cover the landscape of attacks targeting the confidentiality of embedded DNN models that may have a major impact on critical IoT systems, with a particular focus on model extraction  ...  Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems.  ...  of the Investissements d'avenir program (ANR-10-AIRT-05, irtnanoelec); and supported (Mines Saint-Etienne) by the French funded ANR program PICTURE (AAPG2020).  ... 
arXiv:2105.01401v1 fatcat:fo6kowqg2rfvxeqcoxuroeeefe

An Overview of Laser Injection against Embedded Neural Network Models [article]

Mathieu Dumont, Pierre-Alain Moellic, Raphael Viera, Jean-Max Dutertre, Rémi Bernhard
2021 arXiv   pre-print
The latest is particularly critical since the demonstrations of severe flaws impacting the integrity, confidentiality and accessibility of neural network models.  ...  However, the attack surface of such embedded systems cannot be reduced to abstract flaws but must encompass the physical threats related to the implementation of these models within hardware platforms  ...  of the Investissements d'avenir program (ANR-10-AIRT-05, irtnanoelec); and supported (for Mines Saint-Etienne) by the French funded ANR program PICTURE (AAPG2020).  ... 
arXiv:2105.01403v1 fatcat:i3y5cmwyorc4jgedxx35izbvea

Artificial Neural Networks and Fault Injection Attacks [article]

Shahin Tajik, Fatemeh Ganji
2021 arXiv   pre-print
This chapter is on the security assessment of artificial intelligence (AI) and neural network (NN) accelerators in the face of fault injection attacks.  ...  This is a crucial step that must be taken in order to define the threat models precisely. With respect to that, fault attacks mounted on NNs and AI accelerators are explored.  ...  In this regard, we briefly review a few examples of threat models and the main target assets in NNs.  ... 
arXiv:2008.07072v2 fatcat:guzvxrv6pfahdc6t4ack57p6ua

Security Threats and Artificial Intelligence based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Shakila Zaman, Khaled Alhazmi, Mohammed Aseeri, Muhammad Raisuddin Ahmed, Risala Tasin Khan, M. Shamim Kaiser, Mufti Mahmud
2021 IEEE Access  
Network (CNN), Deep Q Network (DQN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM); Extreme Gradient Boosting (EGB)) for countermeasure of the layer wise threats  ...  [81] have reviewed ML-based network layer anomaly detection systems to impede the most common network threats by explaining cyber kill chain models and cyber-attacks.  ...  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see  ... 
doi:10.1109/access.2021.3089681 fatcat:fatpywnjzzfilidakyduz6qz44

Physical Side-Channel Attacks on Embedded Neural Networks: A Survey

Maria Méndez Real, Rubén Salvador
2021 Applied Sciences  
Without a complete review of this emerging field in the literature so far, this paper surveys state-of-the-art physical SCA attacks relative to the implementation of embedded DNNs on micro-controllers  ...  During the last decade, Deep Neural Networks (DNN) have progressively been integrated on all types of platforms, from data centers to embedded systems including low-power processors and, recently, FPGAs  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/app11156790 fatcat:h6ucd5p5yrgbnbrco2t2o3s2rm

Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future Prospective

Georgios Kornaros
2022 IEEE Access  
This review aims to illuminate the value of various approaches for addressing IoT security in a truly effective, flexible, and seamless manner, as well as to provide answers to questions about tradeoffs  ...  computing and user privacy, as well as protecting against attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping.  ...  of ML models and derived services against malicious actors.  ... 
doi:10.1109/access.2022.3179047 fatcat:damwrncpzzbxzamtghwlmrg6v4

A novel approach for Linguistic steganography evaluation based on artificial neural networks

R Gurunath, Ahmed H. Alahmadi, Debabrata Samanta, Mohammad Zubair Khan, Abdulrahman Alahmadi
2021 IEEE Access  
Here the RNN model follows Long Short Term Memory (LSTM) neural network.  ...  The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods.  ...  The authors declare that there is no conflict of interest regarding the publication of this paper References.  ... 
doi:10.1109/access.2021.3108183 fatcat:or6kkpbyqncwnnyhvazzxdndsi

An Overview on CryptDb and Word2vec Approaches

Hana Yousuf, Asma Qassem Al-Hamad, Said Salloum
2020 Advances in Science, Technology and Engineering Systems  
CryptDB is a functional system that provides security and confidentiality through a set of operations. The obvious confidentiality of these attacks is for applications supported by SQL databases.  ...  Online applications are subject to theft of confidential information because opponents can exploit software errors to access private data, and because curious or malicious officials can capture and lose  ...  Acknowledgment This is a part of project done in British University in Dubai.  ... 
doi:10.25046/aj0505154 fatcat:zzs7mmrji5b5dnq3uaqw6xadty

Crypto Makes AI Evolve [article]

Behrouz Zolfaghari
2022 arXiv   pre-print
We start with reviewing existing relevant surveys, noting their shortcomings, especially the lack of a close look at the evolution process and solid future roadmap.  ...  Then, we establish a future roadmap for further research in this area, focusing on the role of quantum-inspired and bio-inspired AI.  ...  Security threats against ML, along with the related defensive techniques, have been reviewed in [36] .  ... 
arXiv:2206.12669v1 fatcat:gm7hoplpnngrnc3ty53yfyfcrq

How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing [article]

Samuel Sousa, Roman Kern
2022 arXiv   pre-print
Finally, this review presents future research directions to guide successive research and development of privacy-preserving NLP models.  ...  Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures.  ...  To provide defenses against these threats, a model has to meet functional requirements related to data types and PETs.  ... 
arXiv:2205.10095v1 fatcat:rksy7oxxlbde5bol3ay44yycru

Information Security Methods—Modern Research Directions

Alexander Shelupanov, Oleg Evsyutin, Anton Konev, Evgeniy Kostyuchenko, Dmitry Kruchinin, Dmitry Nikiforov
2019 Symmetry  
This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them  ...  One of the directions is the development of a comprehensive approach to assessing the security of the information systems.  ...  Table 1 shows the classification of security mechanisms against confidentiality threats in a virtual environment.  ... 
doi:10.3390/sym11020150 fatcat:mf7w3jloszgw7pqb7pj53x27lm

A Study of Data Security on E-Governance using Steganographic Optimization Algorithms

Sk Anamul Hoda, Dr. Abhoy Chand Mondal
2022 International Journal on Recent and Innovation Trends in Computing and Communication  
In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented.  ...  A new perspective on using this technique to protect the information for e-governance is also presented.  ...  ACKNOWLEDGEMENTS This review work was supported and got technical help from the department of computer science, The University of Burdwan, West Bengal, India. We are especially thanks to Dr.  ... 
doi:10.17762/ijritcc.v10i5.5548 fatcat:vxojh7t5tnao7dhkntchh3uuja

A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis

Hichem Mrabet, Sana Belguith, Adeeb Alhomoud, Abderrazak Jemai
2020 Sensors  
Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions.  ...  Transport protocols are exhibited and the security threats against them are discussed while providing common solutions.  ...  (LVQ) model of Artificial Neural Network (ANN), and the Back-Propagation (BP) model of ANN.  ... 
doi:10.3390/s20133625 pmid:32605178 pmcid:PMC7374330 fatcat:rk52vsqrrzd2hebt2xddhrdt54

Toward Identifying APT Malware through API System Calls

Chaoxian Wei, Qiang Li, Dong Guo, Xiangyu Meng, Angel M. Del Rey
2021 Security and Communication Networks  
This study aims to reduce the burden of network security staff from reviewing a large number of suspicious files when defending against APT attacks.  ...  The model of similar studies also lacks an explanation about it.  ...  the complexity of the neural network model.  ... 
doi:10.1155/2021/8077220 fatcat:3ycfzgdhajfeldpkjp7ccertoe

A Comprehensive Study of Deep Learning Based Covert Communication

Ashima Anand, Amit Kumar Singh
2022 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
This paper presents a review of deep learning-based covert communication scheme for protecting digital contents, devices and models.  ...  In addition to conventional applications, this model can be widely used for cover communication, i.e., information hiding.  ...  Deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN) are some of the commonly used deep learning models.  ... 
doi:10.1145/3508365 fatcat:kboo4h4gn5gahd6yimd3i4d5my
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