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Arxiv

Multi-factor authentication for users of non-internet based applications of blockchain-based platforms

Authors: Andrew Kinai, Fred Otieno, Nelson Bore, Komminist Weldemariam

Abstract: Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-factor authentication methods due to the nature of their interaction interfaces and mode of operations. To address this problem, we propose an approach that augments blockchain with multi-factor authentication based on evidence from blockchain transactions combined with risk analysis. A profile of how a user performs transactions is built overtime and is used to analyse the risk level of each new transaction. If a transaction is flagged as high risk, we generate n-factor layers of authentication using past endorsed blockchain transactions. A demonstration of how we used the proposed approach to authenticate critical financial transactions in a blockchain-based asset financing platform is also discussed.

Comment: 7 papes

Date: 15 Oct 2020

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Federated Learning in Adversarial Settings

Authors: Raouf Kerkouche, Gergely Ács, Claude Castelluccia

Abstract: Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, therefore, mitigates the associated privacy risks. However, it still remains vulnerable to various security attacks where malicious participants aim at degrading the generated model, inserting backdoors, or inferring other participants' training data. This paper presents a new federated learning scheme that provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy. Our scheme uses biased quantization of model updates and hence is bandwidth efficient. It is also robust against state-of-the-art backdoor as well as model degradation attacks even when a large proportion of the participant nodes are malicious. We propose a practical differentially private extension of this scheme which protects the whole dataset of participating entities. We show that this extension performs as efficiently as the non-private but robust scheme, even with stringent privacy requirements but are less robust against model degradation and backdoor attacks. This suggests a possible fundamental trade-off between Differential Privacy and robustness.

Date: 15 Oct 2020

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Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

Authors: Yanghao Zhang, Wenjie Ruan, Fu Wang, Xiaowei Huang

Abstract: The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However, current methods for universal adversarial attacks are based on additive perturbation, which cause misclassification when the perturbation is directly added to the input images. In this paper, for the first time, we show that a universal adversarial attack can also be achieved via non-additive perturbation (e.g., spatial transformation). More importantly, to unify both additive and non-additive perturbations, we propose a novel unified yet flexible framework for universal adversarial attacks, called GUAP, which is able to initiate attacks by additive perturbation, non-additive perturbation, or the combination of both. Extensive experiments are conducted on CIFAR-10 and ImageNet datasets with six deep neural network models including GoogleLeNet, VGG16/19, ResNet101/152, and DenseNet121. The empirical experiments demonstrate that GUAP can obtain up to 90.9% and 99.24% successful attack rates on CIFAR-10 and ImageNet datasets, leading to over 15% and 19% improvements respectively than current state-of-the-art universal adversarial attacks. The code for reproducing the experiments in this paper is available at https://github.com/TrustAI/GUAP.

Comment: A short version of this work will appear in the ICDM 2020 conference proceedings

Date: 15 Oct 2020

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EnCoD: Distinguishing Compressed and Encrypted File Fragments

Authors: Fabio De Gaspari, Dorjan Hitaj, Giulio Pagnotta, Lorenzo De Carli, Luigi V. Mancini

Abstract: Reliable identification of encrypted file fragments is a requirement for several security applications, including ransomware detection, digital forensics, and traffic analysis. A popular approach consists of estimating high entropy as a proxy for randomness. However, many modern content types (e.g. office documents, media files, etc.) are highly compressed for storage and transmission efficiency. Compression algorithms also output high-entropy data, thus reducing the accuracy of entropy-based encryption detectors. Over the years, a variety of approaches have been proposed to distinguish encrypted file fragments from high-entropy compressed fragments. However, these approaches are typically only evaluated over a few, select data types and fragment sizes, which makes a fair assessment of their practical applicability impossible. This paper aims to close this gap by comparing existing statistical tests on a large, standardized dataset. Our results show that current approaches cannot reliably tell apart encryption and compression, even for large fragment sizes. To address this issue, we design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data, starting with fragments as small as 512 bytes. We evaluate EnCoD against current approaches over a large dataset of different data types, showing that it outperforms current state-of-the-art for most considered fragment sizes and data types.

Comment: 19 pages, 6 images, 2 tables. Accepted for publication at the 14th International Conference on Network and System Security (NSS2020)

Date: 15 Oct 2020

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Garou: An Efficient and Secure Off-Blockchain Multi-Party Payment Hub

Authors: Yongjie Ye, Weigang Wu

Abstract: To mitigate the scalability problem of decentralized cryptocurrencies such as Bitcoin and Ethereum, the payment channel, which allows two parties to perform secure coin transfers without involving the blockchain, has been proposed. The payment channel increases the transaction throughput of two parties to a level that is only limited by their network bandwidth. Recent proposals focus on extending the two-party payment channel to the N-party payment hub. Unfortunately, none of them can achieve efficiency, flexibility in the absence of a trusted third-party. In this paper, we propose Garou, a secure N-party payment hub that allows multiple parties to perform secure off-chain coin transfers. Except in the case of disputes, participants within the payment hub can make concurrent and direct coin transfers with each other without the involvement of the blockchain or any third-party intermediaries. This allows Garou to achieve both high-performance and flexibility. Garou also guarantees that an honest party always maintains its balance security against strong adversarial capabilities. To demonstrate the feasibility of the Garou protocol, we develop a proof of concept prototype for the Ethereum network. Our evaluation results show that the maximum transaction throughput of Garou is 20 times higher than that of state-of-art payment hubs.

Comment: 11 pages, 6 figures

Date: 15 Oct 2020

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Adversarial Images through Stega Glasses

Authors: Benoît Bonnet, Teddy Furon, Patrick Bas

Abstract: This paper explores the connection between steganography and adversarial images. On the one hand, ste-ganalysis helps in detecting adversarial perturbations. On the other hand, steganography helps in forging adversarial perturbations that are not only invisible to the human eye but also statistically undetectable. This work explains how to use these information hiding tools for attacking or defending computer vision image classification. We play this cat and mouse game with state-of-art classifiers, steganalyzers, and steganographic embedding schemes. It turns out that steganography helps more the attacker than the defender.

Comment: Submitted to IEEE WIFS

Date: 15 Oct 2020

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Securing Manufacturing Using Blockchain

Authors: Zahra Jadidi, Ali Dorri, Raja Jurdak, Colin Fidge

Abstract: Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security data can provide comprehensive and system-wide anomaly detection in industrial networks. In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWAT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors.

Date: 15 Oct 2020

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SpaML: a Bimodal Ensemble Learning Spam Detector based on NLP Techniques

Authors: Jaouhar Fattahi, Mohamed Mejri

Abstract: In this paper, we put forward a new tool, called SpaML, for spam detection using a set of supervised and unsupervised classifiers, and two techniques imbued with Natural Language Processing (NLP), namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). We first present the NLP techniques used. Then, we present our classifiers and their performance on each of these techniques. Then, we present our overall Ensemble Learning classifier and the strategy we are using to combine them. Finally, we present the interesting results shown by SpaML in terms of accuracy and precision.

Comment: This paper was accepted, on October 13, 2020, for pulication and oral presentation at the 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP 2021) to be held in Zhuhai, China during January 8-10, 2021 and hosted by Beijing Normal University (Zhuhai)

Date: 15 Oct 2020

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BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture

Authors: Harsh Bimal Desai, Mustafa Safa Ozdayi, Murat Kantarcioglu

Abstract: Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets with each other, or a third-party. This makes FL particularly suitable for settings where data privacy is desired. At the same time, concealing training data gives attackers an opportunity to inject backdoors into the trained model. It has been shown that an attacker can inject backdoors to the trained model during FL, and then can leverage the backdoor to make the model misclassify later. Several works tried to alleviate this threat by designing robust aggregation functions. However, given more sophisticated attacks are developed over time, which by-pass the existing defenses, we approach this problem from a complementary angle in this work. Particularly, we aim to discourage backdoor attacks by detecting, and punishing the attackers, possibly after the end of training phase. To this end, we develop a hybrid blockchain-based FL framework that uses smart contracts to automatically detect, and punish the attackers via monetary penalties. Our framework is general in the sense that, any aggregation function, and any attacker detection algorithm can be plugged into it. We conduct experiments to demonstrate that our framework preserves the communication-efficient nature of FL, and provide empirical results to illustrate that it can successfully penalize attackers by leveraging our novel attacker detection algorithm.

Date: 14 Oct 2020

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Concentrated Stopping Set Design for Coded Merkle Tree: Improving Security Against Data Availability Attacks in Blockchain Systems

Authors: Debarnab Mitra, Lev Tauz, Lara Dolecek

Abstract: In certain blockchain systems, light nodes are clients that download only a small portion of the block. Light nodes are vulnerable to data availability (DA) attacks where a malicious node hides an invalid portion of the block from the light nodes. Recently, a technique based on erasure codes called Coded Merkle Tree (CMT) was proposed by Yu et al. that enables light nodes to detect a DA attack with high probability. The CMT is constructed using LDPC codes for fast decoding but can fail to detect a DA attack if a malicious node hides a small stopping set of the code. To combat this, Yu et al. used well-studied techniques to design random LDPC codes with high minimum stopping set size. Although effective, these codes are not necessarily optimal for this application. In this paper, we demonstrate a more specialized LDPC code design to improve the security against DA attacks. We achieve this goal by providing a deterministic LDPC code construction that focuses on concentrating stopping sets to a small group of variable nodes rather than only eliminating stopping sets. We design these codes by modifying the Progressive Edge Growth algorithm into a technique called the entropy-constrained PEG (EC-PEG) algorithm. This new method demonstrates a higher probability of detecting DA attacks and allows for good codes at short lengths.

Comment: 6 pages, 5 figures, submitted for ITW 2020

Date: 14 Oct 2020

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