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Category Archives: Machine Learning

Duke researchers to monitor brain injury with machine learning – Duke Department of Neurology

Duke neurologists and electrical engineers are teaming up in an ambitious effort to develop a better way to monitor brain health for all patients in the ICU. Dubbed Neurologic Injury Monitoring and Real-time Output Display, the method will use machine learning and continuous electroencephalogram (EEG) data along with other clinical information to assist providers with assessment of brain injury and brain health.

Current practices for monitoring brain-injured patients include regular clinical exam assessments made every few hours around the clock. However, many patients are not able to follow commands or participate in the physical exam, so doctors can only examine gross responses to loud noises, pinches and noxious stimulation as well as rudimentary brain stem reflexes.

Not only are these exams often limited in their scope, imaging only provides a snapshot of the brain at the time the images are taken, said Brad Kolls, MD, PhD, MMCI, associate professor of neurology at Duke University School of Medicine and principal investigator on the new research study.

The new approach will leverage continuous brainwave activity along with other clinical information from the medical record and standard bedside monitoring to allow a more comprehensive assessment of the state of the brain. Kolls and Leslie Collins, professor of electrical and computer engineering at Duke, hope to improve the care of brain-injured patients by correlating this data with outcomes. This will allow clinicians to optimize brain function and personalize recovery.

With extensive experience in combining machine learning applications with biological signals, Collins will use unsupervised learning such as topic modeling and automated feature extraction to delve into the novel dataset.

We have promising results from using this approach to analyze data taken from sleeping patients, said Collins. Were excited to be able to change the care, and potentially the outcomes, of patients with brain injury.

The program is sponsored by CortiCare Inc., a leading provider of electroencephalography services to hospitals in the U.S. and internationally. CortiCare has funded this multi-year research agreement supporting the program and intends to commercialize the work once completed. The program is expected to run until the fall of 2022.

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Survey: Machine learning will (eventually) help win the war against financial crime – Compliance Week

It is still early days for many institutions, but what is clear is the anti-money laundering (AML) function is on the runway to using ML to fight financial crime. The benefits of ML are indisputable, though financial institutions (FIs) vary in levels of adoption.

Guidehouse and Compliance Week tapped into the ICAs network of 150,000-plus global regulatory and financial compliance professionals for the survey, which canvassed 364 compliance professionalsincluding 229 employed by financial institutions (63 percent of all respondents)to determine the degree to which FIs are using ML. It highlights the intended and realized program benefits of ML implementation; top enterprise risks and pitfalls in adopting and integrating ML to fight financial crime; and satisfaction with results post-implementation. The results also offer insights into what kinds of impediments are holding organizations back from full buy-in.

About a quarter of all surveyed respondents (24 percent) reported working at major FIs with assets at or over $40 billion; this cohort, hereafter referred to as large FIs, represents the bleeding edge of ML in AML. More than half (58 percent) have dedicated budgets for ML, and 39 percent are frontrunners in the industry, having developed or fully bought in on ML products already.

Nearly two-thirds (62 percent) of all respondents are individuals working in AML/Bank Secrecy Act (BSA) or compliance roles; this cohort, hereafter referred to as industry stakeholders, represents the population of users in the process of operationalizing ML in fighting financial crime at their respective institutions.

If large FIs are on the front line in ML adoption, then industry stakeholders are taking up the rearguard. Unlike respondents in the large FIs cohort, the majority of professionals in the industry stakeholders cohort are refraining from taking action steps around ML projects focused on fighting financial crime at this time. Nearly a third (32 percent) are abstaining from talking about ML at all at their institutions; another third (33 percent) are just talking about iti.e., they have no dedicated budget, proof of concept, or products under development just yet.

Nonetheless, there is nearly universal interest in ML among large FIs: 80 percent say they hope to reduce risk with its help, and 61 percent report they have realized this benefit already, demonstrating a compelling ROI.

While large FIs are confident in testing the ML waters, many remain judicious in how much they are willing to spend. Dedicated budgets for ML in AML remain conservative; nearly two-thirds of large FIs (61 percent) budgeted $1 million or less, pre-pandemic, toward implementing ML solutions in AML. The most frequently occurring response, at just over one-third, was a budget of less than $500,000 (34 percent).

Workingwith modest budgets, large FIs are relying on their own bandwidth and expertise to build ML technology: 71 percent are building their own in-house solution, eschewing any off-the-shelf technology, and more than half (54 percent) are training internal staff rather than hiring outside consultants.

With the larger banks, theres just a tendency to look inward first. Im a big proponent of leveraging commercially available products, says Tim Mueller, partner in the Financial Crimes practice at Guidehouse. Mueller predicts vendor solutions will become more popular as the external market matures and better options become available. I think thats the only way for this to work down-market, he adds.

A key driver of ML in the AML function has been the allure of enabling a real-time and continuous Know Your Customer (KYC) process. More than half of all surveyed respondents (55 percent) state improving KYC is the top perceived benefit to their organizations in operationalizing ML to fight financial crime, including 54 percent of large FIs and 59 percent of industry stakeholders.

This trend suggests the challenges associated with the KYC process modestly outweigh competing AML priorities as those most in need of an efficiency upgrade. From customer due diligence (CDD) to customer risk-ranking to enhanced due diligence (EDD) to managing increased regulatory scrutiny, the demands of KYC are both laborious and time-intensive. Banks want to harness a way to work smarter, not harder. ML technology may provide a viable means.

ML is getting applied in the areas of greatest pain for financial institutions, notes Mueller, referring to respondents apparent keenness to improve the KYC process. Theres the area of greatest pain, and that usually represents the area of greatest potential. When asked which additional areas have the greatest potential, Mueller cites transaction monitoring and customer risk rating.

The truth, however, is each area of the AML program is part of a larger puzzle; the pieces interconnect. For instance, an alert generated by a transaction-monitoring system about a potentially suspicious customer is not done in a vacuum, but rather is based on the adequacy of the FIs customer risk assessment processes. Because of the cyclical nature of an AML program, applying ML to one area could potentially translate into a holistic improvement to the program overall.

Its really important to remember this: The area of pain is EDD and CDD, and the area of potential is AML transaction monitoring, and making sure youve got the right alerts. Guess what? The alerts are based on the CDD and EDD. They are interdependent, points out Salvatore LaScala, partner in the AML division of Guidehouse.

While ML takes considerable time to implement and fine-tunea typical runway is 6-12 months, Mueller saysa reduction of risk can be realized relatively quickly.

For organizations that have implemented ML to fight financial crime, reducing risk is overwhelmingly the key benefit realized. Nearly two-thirds (61 percent) of large FIs state their companies have realized the benefit of reducing risk since deploying ML to fight financial crime. What is somewhat puzzling, however, is only 44 percent of large FIs state they have realized efficiency gains.

A similar incongruity is found among the industry stakeholders: 61 percent state they have effectively reduced risk, but only 51 percent indicate they have achieved efficiency gains.

Ifthe adoption of ML has increased institutions effectiveness at reducing risk in AML, why does it appear efficiencygains are lagging? Shouldnt effectiveness and efficiency go hand in hand?

Mueller says no. Effectiveness comes first. From the perspective of an AML professional working at an FI, You spend a lot of money implementing machine learning and AI, Mueller explains.You spend a lot of time. You have a lot of SMEs (subject matter experts) dedicated to making sure its working correctly. You get it implemented; then you must watch it work; then you have to improve it over time. Youre not always going to see efficiency gains right away.

LaScala says, While FIs have made tremendous effectiveness and efficiency strides in leveraging machine learning for transaction monitoring, we believe that they will enjoy even greater success leveraging it for customer due diligence in the future. As the technology evolves, we expect that FIs will be able to review potentially high-risk customer populations more frequently and comprehensively, with less effort.

Fifty-one percent of respondents at large FIs and 45 percent of industry stakeholders cite only partial satisfaction with the results of deploying ML. This reaction may be an indicator that the use of ML in this capacity/area is still emerging.

There has been an increase in the number of false matches in name-screening and transaction monitoring cases that end as risk-irrelevant, noted an AML associate working at a large commercial bank headquartered in Europe that conducts business in the Middle East.

No clear results, remarked a chief AML officer working in wealth management at a small FI that is headquartered and conducts business in Europe.

ML is good. However, it is not efficient in full coverage, another AML associate, who indicated s/he does not work at an FI, said. Manpower is still needed for several products of compliance such as enhanced due diligence.

While the lukewarm endorsement of ML from respondents does not surprise Mueller, it does disappoint him. I do think there are significant gains to be had there both from an effectiveness and an efficiency perspective, Mueller maintains. He believes the lack of satisfaction from users may result from unrealistic expectations and poor communication at the outset of development.

If people are starting more with [the mindset of], Hey, this is our strategy, were ready to go, lets launch into this, then leadership will expect big things right out of the gate, and thats hard to accomplish with anything, much less with something thats so data-driven and that takes so long to develop, Mueller says. Instead they need to start with a small project and achieve success. Then the strategy can be defined using that success as a starting point.

FIs will continue to increase investment and reliance on ML to bolster their financial crime prevention and detection efforts, LaScala adds. We believe that these advanced technologies will ultimately become widely adopted so long as they are transparent and can be explained to the regulator. In fact, someday not far off, systems deploying ML might actually be a regulatory expectation.

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The security threat of adversarial machine learning is real – TechTalks

The Adversarial ML Threat Matrix provides guidelines that help detect and prevent attacks on machine learning systems.

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

With machine learning becoming increasingly popular, one thing that has been worrying experts is the security threats the technology will entail. We are still exploring the possibilities: The breakdown of autonomous driving systems? Inconspicuous theft of sensitive data from deep neural networks? Failure of deep learningbased biometric authentication? Subtle bypass of content moderation algorithms?

Meanwhile, machine learning algorithms have already found their way into critical fields such as finance, health care, and transportation, where security failures can have severe repercussion.

Parallel to the increased adoption of machine learning algorithms in different domains, there has been growing interest in adversarial machine learning, the field of research that explores ways learning algorithms can be compromised.

And now, we finally have a framework to detect and respond to adversarial attacks against machine learning systems. Called the Adversarial ML Threat Matrix, the framework is the result of a joint effort between AI researchers at 13 organizations, including Microsoft, IBM, Nvidia, and MITRE.

While still in early stages, the ML Threat Matrix provides a consolidated view of how malicious actors can take advantage of weaknesses in machine learning algorithms to target organizations that use them. And its key message is that the threat of adversarial machine learning is real and organizations should act now to secure their AI systems.

The Adversarial ML Threat Matrix is presented in the style of ATT&CK, a tried-and-tested framework developed by MITRE to deal with cyber-threats in enterprise networks. ATT&CK provides a table that summarizes different adversarial tactics and the types of techniques that threat actors perform in each area.

Since its inception, ATT&CK has become a popular guide for cybersecurity experts and threat analysts to find weaknesses and speculate on possible attacks. The ATT&CK format of the Adversarial ML Threat Matrix makes it easier for security analysts to understand the threats of machine learning systems. It is also an accessible document for machine learning engineers who might not be deeply acquainted with cybersecurity operations.

Many industries are undergoing digital transformation and will likely adopt machine learning technology as part of service/product offerings, including making high-stakes decisions, Pin-Yu Chen, AI researcher at IBM, told TechTalks in written comments. The notion of system has evolved and become more complicated with the adoption of machine learning and deep learning.

For instance, Chen says, an automated financial loan application recommendation can change from a transparent rule-based system to a black-box neural network-oriented system, which could have considerable implications on how the system can be attacked and secured.

The adversarial threat matrix analysis (i.e., the study) bridges the gap by offering a holistic view of security in emerging ML-based systems, as well as illustrating their causes from traditional means and new risks induce by ML, Chen says.

The Adversarial ML Threat Matrix combines known and documented tactics and techniques used in attacking digital infrastructure with methods that are unique to machine learning systems. Like the original ATT&CK table, each column represents one tactic (or area of activity) such as reconnaissance or model evasion, and each cell represents a specific technique.

For instance, to attack a machine learning system, a malicious actor must first gather information about the underlying model (reconnaissance column). This can be done through the gathering of open-source information (arXiv papers, GitHub repositories, press releases, etc.) or through experimentation with the application programming interface that exposes the model.

Each new type of technology comes with its unique security and privacy implications. For instance, the advent of web applications with database backends introduced the concept SQL injection. Browser scripting languages such as JavaScript ushered in cross-site scripting attacks. The internet of things (IoT) introduced new ways to create botnets and conduct distributed denial of service (DDoS) attacks. Smartphones and mobile apps create new attack vectors for malicious actors and spying agencies.

The security landscape has evolved and continues to develop to address each of these threats. We have anti-malware software, web application firewalls, intrusion detection and prevention systems, DDoS protection solutions, and many more tools to fend off these threats.

For instance, security tools can scan binary executables for the digital fingerprints of malicious payloads, and static analysis can find vulnerabilities in software code. Many platforms such as GitHub and Google App Store already have integrated many of these tools and do a good job at finding security holes in the software they house.

But in adversarial attacks, malicious behavior and vulnerabilities are deeply embedded in the thousands and millions of parameters of deep neural networks, which is both hard to find and beyond the capabilities of current security tools.

Traditional software security usually does not involve the machine learning component because itsa new piece in the growing system, Chen says, adding thatadopting machine learning into the security landscape gives new insights and risk assessment.

The Adversarial ML Threat Matrix comes with a set of case studies of attacks that involve traditional security vulnerabilities, adversarial machine learning, and combinations of both. Whats important is that contrary to the popular belief that adversarial attacks are limited to lab environments, the case studies show that production machine learning system can and have been compromised with adversarial attacks.

For instance, in one case study, the security team at Microsoft Azure used open-source data to gather information about a target machine learning model. They then used a valid account in the server to obtain the machine learning model and its training data. They used this information to find adversarial vulnerabilities in the model and develop attacks against the API that exposed its functionality to the public.

Other case studies show how attackers can compromise various aspect of the machine learning pipeline and the software stack to conduct data poisoning attacks, bypass spam detectors, or force AI systems to reveal confidential information.

The matrix and these case studies can guide analysts in finding weak spots in their software and can guide security tool vendors in creating new tools to protect machine learning systems.

Inspecting a single dimension (machine learning vs traditional software security) only provides an incomplete security analysis of the system as a whole, Chen says. Like the old saying goes: security is only asstrong as its weakest link.

Unfortunately, developers and adopters of machine learning algorithms are not taking the necessary measures to make their models robust against adversarial attacks.

The current development pipeline is merely ensuring a model trained on a training set can generalize well to a test set, while neglecting the fact that the model isoften overconfident about the unseen (out-of-distribution) data or maliciously embbed Trojan patteninthe training set, which offers unintended avenues to evasion attacks and backdoor attacks that an adversary can leverage to control or misguide the deployed model, Chen says. In my view, similar to car model development and manufacturing, a comprehensive in-house collision test for different adversarial treats on an AI model should be the new norm to practice to better understand and mitigate potential security risks.

In his work at IBM Research, Chen has helped develop various methods to detect and patch adversarial vulnerabilities in machine learning models. With the advent Adversarial ML Threat Matrix, the efforts of Chen and other AI and security researchers will put developers in a better position to create secure and robust machine learning systems.

My hope is that with this study, the model developers and machine learning researchers can pay more attention to the security (robustness) aspect of the modeland looking beyond a single performance metric such as accuracy, Chen says.

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Altruist: A New Method To Explain Interpretable Machine Learning Through Local Interpretations of Predictive Models – MarkTechPost

Artificial intelligence (AI) and machine learning (ML) are the digital worlds trendsetters in recent times. Although ML models can make accurate predictions, the logic behind the predictions remains unclear to the users. Lack of evaluation and selection criteria make it difficult for the end-user to select the most appropriate interpretation technique.

How do we extract insights from the models? Which features should be prioritized while making predictions and why? These questions remain prevalent. Interpretable Machine Learning (IML) is an outcome of the questions mentioned above. IML is a layer in ML models that helps human beings understand the procedure and logic behind machine learning models inner working.

Ioannis Mollas, Nick Bassiliades, and Grigorios Tsoumakas have introduced a new methodology to make IML more reliable and understandable for end-users.Altruist, a meta-learning method, aims to help the end-user choose an appropriate technique based on feature importance by providing interpretations through logic-based argumentation.

The meta-learning methodology is composed of the following components:




Consulting Intern: Grounded and solution--oriented Computer Engineering student with a wide variety of learning experiences. Passionate about learning new technologies and implementing it at the same time.

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ATL Special Report Podcast: Tactical Use Cases And Machine Learning With Lexis+ – Above the Law

Welcome back listeners to this exclusive Above the Law Lexis+ Special Report Podcast: Introducing a New Era in Legal Research, brought to you by LexisNexis. This is the second episode in our special series.

Join us once again as LexisNexis Chief Product Officer for North America Jeff Pfeifer (@JeffPfeifer) and Evolve the Law Contributing Editor Ian Connett (@QuantumJurist) dive deeper into Lexis+, sharing tactical use cases, new tools like brief analysis and Ravel view utilizing data visualization, and howJeffs engineering team at Lexis Labs took Google machine learning technology to law school to provide Lexis+ users with the ultimate legal research experience.

This is the second episode of our special four part series. You can listen to our first episode with Jeff Pfeifer here for more on Lexis+. We hope you enjoy this special report featuring Jeff Pfeifer and will stay tuned for the next episodes in the series.

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Efficient audits with machine learning and Slither-simil – Security Boulevard

by Sina Pilehchiha, Concordia University

Trail of Bits has manually curated a wealth of datayears of security assessment reportsand now were exploring how to use this data to make the smart contract auditing process more efficient with Slither-simil.

Based on accumulated knowledge embedded in previous audits, we set out to detect similar vulnerable code snippets in new clients codebases. Specifically, we explored machine learning (ML) approaches to automatically improve on the performance of Slither, our static analyzer for Solidity, and make life a bit easier for both auditors and clients.

Currently, human auditors with expert knowledge of Solidity and its security nuances scan and assess Solidity source code to discover vulnerabilities and potential threats at different granularity levels. In our experiment, we explored how much we could automate security assessments to:

Slither-simil, the statistical addition to Slither, is a code similarity measurement tool that uses state-of-the-art machine learning to detect similar Solidity functions. When it began as an experiment last year under the codename crytic-pred, it was used to vectorize Solidity source code snippets and measure the similarity between them. This year, were taking it to the next level and applying it directly to vulnerable code.

Slither-simil currently uses its own representation of Solidity code, SlithIR (Slither Intermediate Representation), to encode Solidity snippets at the granularity level of functions. We thought function-level analysis was a good place to start our research since its not too coarse (like the file level) and not too detailed (like the statement or line level.)

Figure 1: A high-level view of the process workflow of Slither-simil.

In the process workflow of Slither-simil, we first manually collected vulnerabilities from the previous archived security assessments and transferred them to a vulnerability database. Note that these are the vulnerabilities auditors had to find with no automation.

After that, we compiled previous clients codebases and matched the functions they contained with our vulnerability database via an automated function extraction and normalization script. By the end of this process, our vulnerabilities were normalized SlithIR tokens as input to our ML system.

Heres how we used Slither to transform a Solidity function to the intermediate representation SlithIR, then further tokenized and normalized it to be an input to Slither-simil:

Figure 2: A complete Solidity function from the contract TurtleToken.sol.

Figure 3: The same function with its SlithIR expressions printed out.

First, we converted every statement or expression into its SlithIR correspondent, then tokenized the SlithIR sub-expressions and further normalized them so more similar matches would occur despite superficial differences between the tokens of this function and the vulnerability database.

Figure 4: Normalized SlithIR tokens of the previous expressions.

After obtaining the final form of token representations for this function, we compared its structure to that of the vulnerable functions in our vulnerability database. Due to the modularity of Slither-simil, we used various ML architectures to measure the similarity between any number of functions.

Figure 5: Using Slither-simil to test a function from a smart contract with an array of other Solidity contracts.

Lets take a look at the function transferFrom from the ETQuality.sol smart contract to see how its structure resembled our query function:

Figure 6: Function transferFrom from the ETQuality.sol smart contract.

Comparing the statements in the two functions, we can easily see that they both contain, in the same order, a binary comparison operation (>= and <=), the same type of operand comparison, and another similar assignment operation with an internal call statement and an instance of returning a true value.

As the similarity score goes lower towards 0, these sorts of structural similarities are observed less often and in the other direction; the two functions become more identical, so the two functions with a similarity score of 1.0 are identical to each other.

Research on automatic vulnerability discovery in Solidity has taken off in the past two years, and tools like Vulcan and SmartEmbed, which use ML approaches to discovering vulnerabilities in smart contracts, are showing promising results.

However, all the current related approaches focus on vulnerabilities already detectable by static analyzers like Slither and Mythril, while our experiment focused on the vulnerabilities these tools were not able to identifyspecifically, those undetected by Slither.

Much of the academic research of the past five years has focused on taking ML concepts (usually from the field of natural language processing) and using them in a development or code analysis context, typically referred to as code intelligence. Based on previous, related work in this research area, we aim to bridge the semantic gap between the performance of a human auditor and an ML detection system to discover vulnerabilities, thus complementing the work of Trail of Bits human auditors with automated approaches (i.e., Machine Programming, or MP).

We still face the challenge of data scarcity concerning the scale of smart contracts available for analysis and the frequency of interesting vulnerabilities appearing in them. We can focus on the ML model because its sexy but it doesnt do much good for us in the case of Solidity where even the language itself is very young and we need to tread carefully in how we treat the amount of data we have at our disposal.

Archiving previous client data was a job in itself since we had to deal with the different solc versions to compile each project separately. For someone with limited experience in that area this was a challenge, and I learned a lot along the way. (The most important takeaway of my summer internship is that if youre doing machine learning, you will not realize how major a bottleneck the data collection and cleaning phases are unless you have to do them.)

Figure 7: Distribution of 89 vulnerabilities found among 10 security assessments.

The pie chart shows how 89 vulnerabilities were distributed among the 10 client security assessments we surveyed. We documented both the notable vulnerabilities and those that were not discoverable by Slither.

This past summer we resumed the development of Slither-simil and SlithIR with two goals in mind:

We implemented the baseline text-based model with FastText to be compared with an improved model with a tangibly significant difference in results; e.g., one not working on software complexity metrics, but focusing solely on graph-based models, as they are the most promising ones right now.

For this, we have proposed a slew of techniques to try out with the Solidity language at the highest abstraction level, namely, source code.

To develop ML models, we considered both supervised and unsupervised learning methods. First, we developed a baseline unsupervised model based on tokenizing source code functions and embedding them in a Euclidean space (Figure 8) to measure and quantify the distance (i.e., dissimilarity) between different tokens. Since functions are constituted from tokens, we just added up the differences to get the (dis)similarity between any two different snippets of any size.

The diagram below shows the SlithIR tokens from a set of training Solidity data spherized in a three-dimensional Euclidean space, with similar tokens closer to each other in vector distance. Each purple dot shows one token.

Figure 8: Embedding space containing SlithIR tokens from a set of training Solidity data

We are currently developing a proprietary database consisting of our previous clients and their publicly available vulnerable smart contracts, and references in papers and other audits. Together theyll form one unified comprehensive database of Solidity vulnerabilities for queries, later training, and testing newer models.

Were also working on other unsupervised and supervised models, using data labeled by static analyzers like Slither and Mythril. Were examining deep learning models that have much more expressivity we can model source code withspecifically, graph-based models, utilizing abstract syntax trees and control flow graphs.

And were looking forward to checking out Slither-simils performance on new audit tasks to see how it improves our assurance teams productivity (e.g., in triaging and finding the low-hanging fruit more quickly). Were also going to test it on Mainnet when it gets a bit more mature and automatically scalable.

You can try Slither-simil now on this Github PR. For end users, its the simplest CLI tool available:

Slither-simil is a powerful tool with potential to measure the similarity between function snippets of any size written in Solidity. We are continuing to develop it, and based on current results and recent related research, we hope to see impactful real-world results before the end of the year.

Finally, Id like to thank my supervisors Gustavo, Michael, Josselin, Stefan, Dan, and everyone else at Trail of Bits, who made this the most extraordinary internship experience Ive ever had.

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