Enhanced adaptive matched filter for automated identification and measurement of electrocardiographic alternans

Light nodes in blockchain systems are known to be vulnerable to data availability (DA) attacks where they accept an invalid block with unavailable portions. Previous works have used LDPC and 2-D Reed Solomon (2D-RS) codes with Merkle Trees to mitigate DA attacks. While these codes have demonstrated improved performance across a variety of metrics such as DA detection probability, they are difficult to apply to blockchains with large blocks due to generally intractable code guarantees for large codelengths (LDPC), large decoding complexity (2D-RS), or large coding fraud proof sizes (2D-RS). We address these issues by proposing the novel Polar Coded Merkle Tree (PCMT) which is a Merkle Tree built from the encoding graphs of polar codes and a specialized polar code construction called Sampling-Efficient Freezing (SEF). We demonstrate that the PCMT with SEF polar codes performs well in detecting DA attacks for large block sizes. 

/pdf/polar-coded-merkle-tree-improved-detection-of-data-3fyhzhaj.pdf

Polar Coded Merkle Tree: Improved Detection of Data Availability Attacks in Blockchain Systems

Light nodes are clients in blockchain systems that only store a small portion of the blockchain ledger. In certain blockchains, light nodes are vulnerable to a data availability (DA) attack where a malicious node makes the light nodes accept an invalid block by hiding the invalid portion of the block from the nodes in the system. Recently, a technique based on LDPC codes called Coded Merkle Tree was proposed by Yu et al. that enables light nodes to detect a DA attack by randomly requesting/sampling portions of the block from the malicious node. However, light nodes fail to detect a DA attack with high probability if a malicious node hides a small stopping set of the LDPC code. In this paper, we demonstrate that a suitable co-design of specialized LDPC codes and the light node sampling strategy leads to a high probability of detection of DA attacks. We consider different adversary models based on their computational capabilities of finding stopping sets. For the different adversary models, we provide new specialized LDPC code constructions and coupled light node sampling strategies and demonstrate that they lead to a higher probability of detection of DA attacks compared to approaches proposed in earlier literature. 

/pdf/overcoming-data-availability-attacks-in-blockchain-systems-1mexxzlk.pdf

Overcoming Data Availability Attacks in Blockchain Systems: Short Code-Length LDPC Code Design for Coded Merkle Tree

Light nodes in blockchain systems are known to be vulnerable to data availability (DA) attacks where they accept an invalid block with unavailable portions. Previous works have used LDPC and 2-D Reed Solomon (2D-RS) codes with Merkle Trees to mitigate DA attacks. While these codes have demonstrated improved performance across a variety of metrics such as DA detection probability, they are difficult to apply to blockchains with large blocks due to generally intractable code guarantees for large codelengths (LDPC), large decoding complexity (2D-RS), or large coding fraud proof sizes (2D-RS). We address these issues by proposing the novel Polar Coded Merkle Tree (PCMT) which is a Merkle Tree built from the encoding graphs of polar codes and a specialized polar code construction called Sampling-Efficient Freezing (SEF). We demonstrate that the PCMT with SEF polar codes performs well in detecting DA attacks for large block sizes.

http://www.cell.com/article/S2405844023001548/pdf

Machine Learning approach for TWA detection relying on ensemble data design

Alternans is an electrophysiological phenomenon consisting in a beat-to-beat variation of the morphology of an electrocardiographic (ECG) waveform. Literature has particularly studied T-wave alternans (TWA) because it has been widely recognized as a noninvasive and clinically useful index to predict occurrence of malignant ventricular arrhythmias and, eventually, sudden cardiac death. Historically, alternans of other segments of ECG, like P wave (PWA), or QRS complex (QRSA) gained less interest than TWA, but this is an incomplete vision of the action potential (AP). AP is influenced by electrical activity of all myocardial cells, so it is reasonable that all ECG waveforms could be affected by alternans phenomenon. ECG alternans (ECGA) can be intended as the prevalent nature of alternans. This study aimed to use the heart-rate adaptive match filter (AMF) method, previously applied for TWA applications, to detect ECGA. AMF effectiveness was tested on simulated alternating ECG (alternans-amplitude range: 10 µV–200 µV), characterized by single- and multiple-wave alternans (always of the same amplitude and morphology). AMF method proved to be specific, being able to recognize ECGA absence, and particularly sensitive to TWA. In general, in case of singular-wave alternans, AMF correctly identified the type of alternans and correctly determined its amplitude (mean error: 0%). When TWA was combined to PWA or QRSA, only TWA was identified with an overestimation of its amplitude (mean error: 23%). In conclusion, overall AMF proved its effectiveness and specificity in revealing and discriminating ECGA.

Electrocardiographic Alternans: A New Approach

The assessment of cyber risk plays a crucial role for cybersecurity management, and has become a compulsory task for certain types of companies and organizations. This makes the demand for reliable cyber risk assessment tools continuously increasing, especially concerning quantitative tools based on statistical approaches. Probabilistic cyber risk assessment methods, however, follow the general paradigm of probabilistic risk assessment, which requires the magnitude and the likelihood of incidents as inputs. Unfortunately, for cyber incidents, the likelihood of occurrence is hard to estimate based on historical and publicly available data; so, expert evaluations are commonly used, which however leave space to subjectivity. In this paper, we propose a novel probabilistic model, called MAGIC (Method for AssessinG cyber Incidents oCcurrence), to compute the likelihood of occurrence of a cyber incident, based on the evaluation of the cyber posture of the target organization. This allows deriving tailor-made inputs for probabilistic risk assessment methods, like HTMA (How To Measure Anything in cybersecurity risk), FAIR (Factor Analysis of Information Risk) and others, thus considerably reducing the margin of subjectivity in the assessment of cyber risk. We corroborate our approach through a qualitative and a quantitative comparison with several existing methods.

pdf/magic-a-method-for-assessing-cyber-incidents-occurrence-258m8qbz.pdf

MAGIC: A Method for Assessing Cyber Incidents Occurrence

In a blockchain Data Availability Attack (DAA), a malicious node publishes a block header but withholds part of the block, which contains invalid transactions. Honest full nodes, which can download and store the full blockchain, are aware that some data are not available but they have no formal way to prove it to light nodes, i.e., nodes that have limited resources and are not able to access the whole blockchain data. A common solution to counter these attacks exploits linear error correcting codes to encode the block content. A recent protocol, called SPAR, employs coded Merkle trees and low-density parity-check (LDPC) codes to counter DAAs. We show that the sparse nature of LDPC matrices and the use of the so-called peeling decoder make the protocol less secure than expected, owing to a new possible attack strategy that can be followed by malicious nodes.

A data availability attack on a blockchain protocol based on LDPC codes

The growing interest in Internet of Things (IoT) and Industrial IoT (IIoT) poses the challenge of finding robust solutions for the certification and notarization of data produced and collected by embedded devices. The blockchain and distributed ledger technologies represent a promising solution to address these issues, but rise other questions, for example regarding their practical feasibility. In fact, IoT devices have limited resources and, consequently, may not be able to easily perform all the operations required to participate in a blockchain. In this paper we propose a minimal architecture to allow IoT devices performing data certification and notarization on the Ethereum blockchain. We develop a hardware-software platform through which a lightweight device (e.g., an IoT sensor), holding a secret key and the associated public address, produces signed transactions, which are then submitted to the blockchain network. This guarantees data integrity and authenticity and, on the other hand, minimizes the computational burden on the lightweight device. To show the practicality of the proposed approach, we report and discuss the results of benchmarks performed on ARM Cortex-M4 hardware architectures, sending transactions over the Ropsten testnet. Our results show that all the necessary operations can be performed with small latency, thus proving that an IoT device can directly interact with the blockchain, without apparent bottlenecks.

Implementation of Ethereum Accounts and Transactions on Embedded IoT Devices

Cyber risk assessment is one of the top priorities of modern organizations and companies, owing to the massive amount of data they process on a daily basis and to the increasing number of successful cyber attacks. The probability of occurrence of these cyber incidents can be estimated by means of statistical tools, which exploit numerical categories to compute the probability that the organization will be breached by one or more cyber attacks. However, these approaches heavily rely on experts' estimates and/or on past data, which are not always available. In this paper we show that, by exploiting machine learning tools, cyber risk can be assessed by using some easily obtainable parameters (called maturity, complexity, attractiveness) representing the cyber posture of the organization under exam. To validate the method we propose, we apply it to three organizations in the healthcare sector having different values of maturity and complexity. The results highlight how the model can be successfully used to assign each organization a class of cyber risk, even in a crucial sector such as healthcare.

A Machine Learning-based Method for Cyber Risk Assessment

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