background preloader

ML & DL Security Application Tools and Methods

Facebook Twitter

1210ijcsit13. SystemML - Publications. Feedback Group Name SystemML.

SystemML - Publications

Want an open-source deep learning framework? Take your pick. Earlier this week, Google made a splash when it released its TensorFlow artificial intelligence software on GitHub under an open-source license.

Want an open-source deep learning framework? Take your pick

Google has a sizable stable of AI talent, and AI is working behind the scenes in popular products, including Gmail and Google search, so AI tools from Google are a big deal. Today on GitHub, TensorFlow, primarily written in C++, is the top trending project of the day, the week, and the month, having accrued more than 10,000 stars in about one week. But there are several other open-source tools to choose from on GitHub if you want to improve your app with deep learning, a type of AI that involves training artificial neural networks on a bunch of data and then getting them to make inferences about new data.

Here’s a rundown of some other notable deep learning libraries available today. The big ones Caffe. The long tail Apache Singa. What am I leaving out? Deep Learning Frameworks. The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications.

Deep Learning Frameworks

Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started. Caffe is a deep learning framework made with expression, speed, and modularity in mind. Caffe is developed by the Berkeley Vision and Learning Center (BVLC), as well as community contributors and is popular for computer vision. Caffe supports cuDNN v5 for GPU acceleration. Popular Deep Learning Tools – a review. Deep Learning is the hottest trend now in AI and Machine Learning.

Popular Deep Learning Tools – a review

We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Deep Learning is now of the hottest trends in Artificial Intelligence and Machine Learning, with daily reports of amazing new achievements, like doing better than humans on IQ test. In 2015 KDnuggets Software Poll, a new category for Deep Learning Tools was added, with most popular tools in that poll listed below.

Analytics, Data Mining, and Data Science. The Next Big Inflection in Big Data: Automated Insights. By Evangelos Simoudis, @esimoudis, Corporate Innovation Ventures.

The Next Big Inflection in Big Data: Automated Insights

In previous posts, I wrote about the need for insight generation and provided an example of an insightful application. I maintain that insightful applications are the key to businesses effectively exploiting big data in order to improve decision-making and address important problems. To better understand and appreciate the need for developing such applications, it is important to consider what is happening more broadly in big data and evaluate how our experiences with business intelligence systems should be driving our thinking about insightful applications.

Because I consider insightful applications the next inflection in big data (see recent examples of such applications built using IBM’s Watson platform), I would like to further explore this topic in a series of blog posts. Data analytics over the past 25 years. Interview: Ingo Mierswa, RapidMiner CEO on “Predaction” and Key Turning Points. By Ajay Ohri, DecisionStats, June 2014.

Interview: Ingo Mierswa, RapidMiner CEO on “Predaction” and Key Turning Points

Here is my interview with Ingo Mierswa, co-founder and CEO of RapidMiner. Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the AI Division of the University of Dortmund, Germany. Mierswa has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. Ajay Ohri: Q1. Ingo Mierswa: Everything at RapidMiner follows three simple principles: "predaction", collaboration and simplicity. The biggest value of predictive analytics is not in high-level predictions, but rather in performing millions of micro-predictions and acting on those predictions. "What is the weather predaction for tomorrow? " The value is not in the knowledge that it is going to rain; the true value lies in determining the best option for you when this happens so you can be prepared.

AO: Q2. AO: Q3. The fourth and fifth key milestones took place in 2013. AO: Q4. Security and Intelligence Solutions. Analyst's Notebook 6, from i2 Inc., conducts sophisticated link analysis, timeline analysis and data visualization for complex investigations.

Security and Intelligence Solutions

Centrifuge, offers analysts and investigators an integrated suite of capabilities that can help them rapidly understand and glean insight from new data sources. InferX, remote data mining solutions for law enforcement, intrusion detection, and related applications. NORA™ (Non-Obvious Relationship Awareness™), identifies potentially alarming non-obvious relationships among and between individuals and companies QinetiQ Knowledge Discovery Appliance, provides timely intelligence by real-time processing of high volumes of data using an iterative learning engine that evaluates context based on attributes and associations to determine relevancy and value of data. CDMC2013: Cybersecurity Data Mining Competition. CDMC2013: The 4th International Cybersecurity Data Mining Competition Objectives The purpose of the International Cybersecurity Data Mining Competition is to increase awareness of Cybersecurity and the potential of industrial applications, and give young researchers exposure to the main issues related to the topic and to ongoing work in this area.

CDMC2013: Cybersecurity Data Mining Competition

The focus of this competition is on string sequences analysis towards application of knowledge discovery techniques for protecting personal computer information by means of detection, preventive measures, and responding to various attacks. Tasks and Data eNews Categorization. Prizes and Awards We have set cash prizes for the competition.

Anomaly Detection in Predictive Maintenance with Time Series Analysis. By Rosaria Silipo The Newest Challenge Most of the data science use cases are relatively well established by now: a goal is defined, a target class is selected, a model is trained to recognize/predict the target, and the same model is applied to new never-seen-before productive data.

Anomaly Detection in Predictive Maintenance with Time Series Analysis

The newest challengelies in predicting the “unknown”, i.e. an anomaly. An anomaly is an event that is not part of the system’s past; an event that cannot be found in the system’s historical data. In the case of network data, an anomaly can be an intrusion, in medicine a sudden pathological status, in sales or credit card businesses a fraudulent payment, and, finally, in machinery a mechanical piece breakdown. In the manufacturing industry, the goal is to keep a mechanical pieceworking as long as possible–mechanical pieces are expensive – and at the same time to predict its breaking point before it actually occurs–a machine breakoften triggers a chain reaction of expensive damages. Fraud Detection Solutions. Alaric Systems "Fractals" card fraud detection and prevention systems using proprietary inference techniques based on Bayesian methods.

Fraud Detection Solutions

Data Mining and other covert collection of information.