This blog is based on artificial intelligence and human brain. The main aim of this is to relate machine and human brains together and how fast we are going to make similar like human brain.
AI BASED SECURITY SYSTEM THAT IS 85% ACCURATE
Get link
Facebook
Twitter
Pinterest
Email
Other Apps
Researchers from MIT’s Computer Science, Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have demonstrated an artificial intelligence platform that predicts cyber-attacks with 85% accuracy. Named as AI2, this artificial intelligence platform is roughly three times better than present security systems, and also reduces the number of false positives by a factor of 5.
Drawbacks in the present Security systems
The present security systems are either Human or Machine-centric. The human based Security systems rely on the existing rules created by living experts and therefore miss any attacks that don’t match the rules. Similarly, the machine reliant systems rely on “anomaly detection,” which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway.
However, AI2 is a platform that makes use of human intelligence and machine capabilities simultaneously. It predicts cyber-attacks significantly better than existing systems by continuously incorporating inputs from human experts. (The name comes from merging artificial intelligence with what the researchers call “analyst intuition.”).
How does AI2 predict Cyberattacks
AI2 scrutinizes the present data completely and detects suspicious activity by clustering the data into meaningful patterns using unsupervised machine-learning. These patterns are then taken to security experts who analyze and confirm which events are actual attacks and incorporates that feedback into the AI2 models for the next set of data.
“You can think about the system as a virtual analyst,” says CSAIL research scientist Kalyan Veeramachaneni, who developed AI2 with Ignacio Arnaldo, a chief data scientist at PatternEx and a former CSAIL postdoc. “It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly.”
AI2 is significantly better than the prevailing Artificial Intelligence Security systems as it tends to improve itself without asking too much of human involvement. Combining three different unsupervised-learning methods, AI2 shows up the top events to analysts for them to the label. It then builds a supervised model that it can constantly refine through what is called as a “continuous active learning system.”
Researchers say that AI2 will become more accurate each time it detects an attack. New attacks will lead to more analyst feedback, which in turn will improve the accuracy of the future predictions.
This is the small video which shows how AI2 works and how it will evolve with time and more attacks.
Professors from the University of Notre Dame see AI2 as a security system which can prevent attacks such as fraud, service abuse and account takeover, which are major challenges faced by consumer-facing systems.
Artificial Intelligence & Robotic Brain are referenced from the Human Brain, and as we understand our brain we try to develop it artificially. Human Brain is one of the very complex structure which is hard to understand, comprehend & restructure it, but it restructures itself. It consists of trillions of connections which helps in day to day task & also taking big decisions. What if this decision taking ability in some form can be transferred to Machines, Robots & computers. To develop & make that possible there must be the smallest constitute. That smallest constitute is called Perceptron. Perceptron in simple language is artificial neuron. It works the same way as our neuron works. The meta structure of Perceptron is inspired by the biological neuron. Just like a biological neuron has dendrites to receive signals, a cell body to process them, and an axon to send signals out to other neurons, the artificial neuron has a number of input channels, a processing
We all Know that Computers & Mobile devices getting smarter and smarter day by day, but these all are hardware. They are doing so with the help of NPU(Neural Processing Unit) & Quantum Computing(which is in premature state right now). Apart from these two all the magic of AI & ML comes from software side like predicting Search Results whenever you start typing, predicting physical objects, predicting Human mood by analyzing facial pattern. These all are very fascinating and out of the world things which was only imagination 20-30 years before & now we use them on daily basis. So here is two popular & widely used algorithm. Supervised Learning:- Supervised learning is an approach to Machine Learning that is based on training data that includes expected answers. An Artificial Intelligence uses the data to build general models that map the data to the correct answer. Supervised learning is where you have input variables (x) and an output variable (Y) and you
Stanford University researchers have shown that a machine learning model can identify heart arrhythmias from electrocardiogram(ECG) better than an expert doctor. The automated approach is also significant because it could not make a quality care readily accessible in areas where resources are scares. So, first thing to know what is Heart arrythmias? HEART ARRHYTHMIAS Heart arrhythmias is a group of conditions in which heart beat is irregular, too fast or too slow. A heart rate that is too fast- 100 beats minute in adult is called Tachycardia & a heart rate that is too slow- below 60 beats per minute is called Bradycardia. Cardiac arrhythmia occurs when electrical impulses in the heart don't work properly. There may be no symptoms. Alternatively, symptoms may include a fluttering in the chest, chest pain, fainting or dizziness. It may feel like a fluttering or racing heart & may be harmless. However, some heart arrhythmias may cause bothersome- sometimes ev
Comments
Post a Comment