There is enormous interest in and momentum around using AI to reduce the need for human monitoring while improving enterprise security. Machine learning and other techniques are used for behavioral threat analytics, anomaly detection and reducing false-positive alerts. At the same time, private and nation-state cybercriminals are applying AI to the other side of the security coin. Artificial intelligence is used to find vulnerabilities, shape exploits and conduct targeted attacks. How does an enterprise protect the tools it is building and secure those it is running during the production process?

This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).

Self-supervised learning is one of the most popular fields in modern deep-learning research. As Yann Lecun likes to say self-supervised learning is the dark matter of intelligence and the way to create common sense in AI systems. The ideas and techniques of this paradigm attract many researchers to try and enlarge the application of self-supervised learning into new research fields. Of course, anomaly detection is not an exception. In Part 1 of this article, we discussed the definition of anomaly detection and a technique called Kernel Density Estimation.

Anomalies, or outliers, can be a serious issue when training machine learning algorithms or applying statistical techniques. They are often the result of errors in measurements or exceptional system conditions and therefore do not describe the common functioning of the underlying system. Indeed, the best practice is to implement an outlier removal phase before proceeding with further analysis. In some cases, outliers can give us information about localized anomalies in the whole system; so the detection of outliers is a valuable process because of the additional information they can provide about your dataset. There are many techniques to detect and optionally remove outliers from a dataset.

We use T 10 for max-softmax and T 2 for divergence-based scoring functions. We report average performance over last three epochs. Row 1 shows the standard setting where the loss function is KL divergence between the uniform distribution and the softmax output Lee et al.; Hendrycks et al. while the anomaly score is max-softmax. Row 3 features the reversed KL divergence. Minimizing the reversed divergence between the uniform distribution and the softmax distribution is equivalent to maximizing the softmax entropy.

When it comes to artificial intelligence (AI), most banks have focused first on productivity gains, such as automating repetitive tasks, and on reducing fraud or regulatory risks with improved anomaly detection and monitoring methods. Some banks have started to use AI in capital market operations. While not every bank needs to be a frontrunner (one of five archetypes shown), frontrunners do share some instructive patterns. They've built out AI maturity and adoption throughout the organization. They focus on generating value for customers by, for instance, anticipating personalized offerings.

Many people do not understand the difference between terms such as artificial intelligence (AI), machine learning (ML), and other advanced computing concepts. Machine learning (ML) is a type of artificial intelligence (AI) that can learn from data. Without providing a system with specific instructions, ML can determine patterns, make assessments, and continuously relearn to improve model accuracy and performance using labeled data, algorithms, and statistical models. Data – whether it be text files, images, videos, etc. – is labeled by adding informative tags that identify the context so the ML algorithms can learn from it. ML develops knowledge and expertise but is limited in how it is applied.

For the longest time, I was quite against using R for no other reason other than the fact that it wasn't Python. But after playing around with R for the past few months, I realized that R outclasses Python in several use cases, particularly for statistical analyses. As well, R has some powerful packages that were built by the world's biggest tech companies, and they aren't in Python! And so, in this article, I wanted to go over three R packages that I highly recommend that you take the time to learn and equip in your arsenal of tools because they are seriously powerful tools. Let's say your company launched a new TV ad for the Super Bowl and they wanted to see how it impacted conversions. Causal impact analysis attempts to predict what would have happened if the campaign never occurred -- this is called the counterfactual.

This course is designed to help you develop the skill necessary to perform ETL operations in Databricks, build unsupervised anomaly detection models, learn MLOPS, perform CI/CD operations in databricks and Deploy machine learning models into production.

One prominent tactic used to keep malicious behavior from being detected during dynamic test campaigns is logic bombs, where malicious operations are triggered only when specific conditions are satisfied. Defusing logic bombs remains an unsolved problem in the literature. In this work, we propose to investigate Suspicious Hidden Sensitive Operations (SHSOs) as a step towards triaging logic bombs. To that end, we develop a novel hybrid approach that combines static analysis and anomaly detection techniques to uncover SHSOs, which we predict as likely implementations of logic bombs. Concretely, Difuzer identifies SHSO entry-points using an instrumentation engine and an inter-procedural data-flow analysis. Then, it extracts trigger-specific features to characterize SHSOs and leverages One-Class SVM to implement an unsupervised learning model for detecting abnormal triggers. We evaluate our prototype and show that it yields a precision of 99.02% to detect SHSOs among which 29.7% are logic bombs. Difuzer outperforms the state-of-the-art in revealing more logic bombs while yielding less false positives in about one order of magnitude less time. All our artifacts are released to the community.