Deep reinforcement learning anomaly detection

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Introduction to Anomaly Detection in Python

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You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.This item in chinese. Feb 11, 18 min read. Tom Hanlon. Michael Manapat. Machine learning has long powered many products we interact with daily—from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook.

More recently, machine learning has entered the public consciousness because of advances in "deep learning"—these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. In this series, we'll give an introduction to some powerful but generally applicable techniques in machine learning.

These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Learn more. The increasing accuracy of deep neural networks for solving problems such as speech and image recognition has stoked attention and research devoted to deep learning and AI more generally. But widening popularity has also resulted in confusion. This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data.

Artificial neural networks are algorithms initially conceived to emulate biological neurons.

deep reinforcement learning anomaly detection

The analogy, however, is a loose one. The features of a biological neuron mirrored by artificial neural networks include connections between the nodes and an activation threshold, or trigger, for each neuron to fire.

deep reinforcement learning anomaly detection

By building a system of connected artificial neurons we obtain systems that can be trained to learn higher-level patterns in data and perform useful functions such as regression, classification, clustering, and prediction. The comparison to biological neurons only goes so far. A whole neural network of many nodes can run on a single machine.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

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Email Address. Sign In. Access provided by: anon Sign Out. Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning Abstract: Unmanned aerial vehicles UAVs are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas.

However, the currently available UAVs are prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit.

A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold.

The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor.

Article :. Date of Publication: 09 August DOI: Need Help?As Artificial Intelligence is becoming a mainstream and easily available commercial technology, both organizations and criminals are trying to take full advantage of it. In particular, there are predictions by cyber security experts that going forward, the world will witness many AI-powered cyber attacks 1. This mandates the development of more sophisticated cyber defense systems using autonomous agents which are capable of generating and executing effective policies against such attacks, without human feedback in the loop.

In this series of blog posts, we plan to write about such next generation cyber defense systems. One effective approach of detecting many types of cyber threats is to treat it as an anomaly detection problem and use machine learning or signature-based approaches to build detection systems. Anomaly Detection Systems ADS are also used as the core engines powering authentication and fraud detection platforms, for applications such as continuous authentication which Zighra provides through its SensifyID platform.

Anomaly Detection Systems ADS are designed to find patterns in a dataset that do not conform to expected normal behavior. Most of the anomaly detection problems can be formulated as a typical classification task in machine learning, where a dataset containing labelled instances of normal behavior also of abnormal behavior if data is available is used for training a supervised or semi-supervised machine learning models such as neural networks or support vector machines 2.

Though unsupervised learning also could be used for anomaly detection, they are shown to perform very poorly compared to supervised or semi-supervised learning 3. Since in domains such as cyber defensedefence, the attack scenarios change continuously due to constant evolution by the attackers to avoid detection systems, it is important to have a continuous learning system for anomaly detection.

This could be achieved in principle using online learning where a continuous supervised signal whether the past predictions were correct or not is fed back into the system and the model is continuously trained with more weights given to recent data to incorporate concept shifts in the dataset. However, there are many anomaly detection problems where a straightforward online learning is either not feasible or not good enough to provide highly accurate predictions.

In such scenarios, one could formulate the anomaly detection problem as a reinforcement learning problem 4,5where an autonomous agent interacts with the environment and takes actions such as allowing or denying access and gets rewards from the environment positive rewards for correct predictions of anomaly and negative rewards for wrong predictions and over a period of time learns to predict anomalies with a high level of accuracy.

Reinforcement learning brings the full power of Artificial Intelligence to anomaly detection. In this blog, we will describe how reinforcement learning could be used for anomaly detection giving an example of network intrusion through Bot attacks.

To begin with, let us see how a reinforcement learning problem can be described in a mathematical framework called Markov Decision Process or MDP. There are several approaches for solving a MDP problem. One of the well-known methods is called Q-Learning. Here one defines a Quality function Q s, a which gives an estimate of the maximum total reward or payoff the agent can receive starting from state s and performing action a. The value of Q s, a for all states and actions can be found through solving the Bellman equation :.

Bellman equation is the central theoretical concept that is used in almost all formulations of reinforcement learning.

Then the optimal policy is given by:. In practice, a derivative form of the Bellman equation is used in many implementations. This is an iterative updating algorithm called the Temporal Difference Learning algorithm. However, in many practical scenarios, there are a very large number of states and the above approach would fail to scale. For example, consider the application of RL to play the game MsPacman.

There are over pellets that MsPacman can eat, each of which can be present or absent. To avoid the curse of dimensionality problem, one tries to approximate Q-Values using a Deep Neural Network with a manageable number of parameters 6.

This can be conceptually represented by the following diagram:.This overview is intended for beginners in the fields of data science and machine learning.

Almost no formal professional experience is needed to follow along, but the reader should have some basic knowledge of calculus specifically integralsthe programming language Python, functional programming, and machine learning. Before getting started, it is important to establish some boundaries on the definition of an anomaly. Point anomalies: A single instance of data is anomalous if it's too far off from the rest. Business use case: Detecting credit card fraud based on "amount spent.

This type of anomaly is common in time-series data. Collective anomalies: A set of data instances collectively helps in detecting anomalies.

Traversing mean over time-series data isn't exactly trivial, as it's not static. You would need a rolling window to compute the average across the data points. Mathematically, an n-period simple moving average can also be defined as a "low pass filter. The low pass filter allows you to identify anomalies in simple use cases, but there are certain situations where this technique won't work. Here are a few:. The data contains noise which might be similar to abnormal behavior, because the boundary between normal and abnormal behavior is often not precise.

The definition of abnormal or normal may frequently change, as malicious adversaries constantly adapt themselves. Therefore, the threshold based on moving average may not always apply. The pattern is based on seasonality. This involves more sophisticated methods, such as decomposing the data into multiple trends in order to identify the change in seasonality.

Below is a brief overview of popular machine learning-based techniques for anomaly detection. Assumption: Normal data points occur around a dense neighborhood and abnormalities are far away. The nearest set of data points are evaluated using a score, which could be Eucledian distance or a similar measure dependent on the type of the data categorical or numerical.

They could be broadly classified into two algorithms:. K-nearest neighbor : k-NN is a simple, non-parametric lazy learning technique used to classify data based on similarities in distance metrics such as Eucledian, Manhattan, Minkowski, or Hamming distance. This concept is based on a distance metric called reachability distance.

K-means is a widely used clustering algorithm. It creates 'k' similar clusters of data points. Data instances that fall outside of these groups could potentially be marked as anomalies.

The algorithm learns a soft boundary in order to cluster the normal data instances using the training set, and then, using the testing instance, it tunes itself to identify the abnormalities that fall outside the learned region.

Sunspots are defined as dark spots on the surface of the sun.

Anomaly Detection for Time Series Data with Deep Learning

Convolution is a mathematical operation that is performed on two functions to produce a third function. This way as t changes, different weights are assigned to the input function f T.

In our case, f T represents the sunspot counts at time T. Let's see if the above anomaly detection function could be used for another use case. The x axis represents time in days since and the y axis represents the value of the stock in dollars. Looks like our anomaly detector is doing a decent job.

Anomaly Detection

It is able to detect data points that are 2 sigma away from the fitted curve. Depending on the distribution of a use case in a time-series setting, and the dynamicity of the environment, you may need to use stationary global or non-stationary local standard deviation to stabilize a model. The mathematical function around the standard deviation could be modified very easily to use a customized formulation.

They are not necessarily the most efficient solution. ABE, N.Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this paper, we study deep reinforcement learning based active sequential testing for anomaly detection.

We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. We provide simulation results for both the training phase and testing phase, and compare the proposed framework with the Chernoff test in terms of claim delay and loss.

Chen Zhong. Cenk Gursoy. Senem Velipasalar. To make efficient use of limited spectral resources, we in this work pro We consider the dynamic multichannel access problem, which can be formul Catastrophic forgetting has a serious impact in reinforcement learning, The growing demand on high-quality and low-latency multimedia services h Recent works have validated the possibility of improving energy efficien In sponsored search, keyword recommendations help advertisers to achieve Logic synthesis requires extensive tuning of the synthesis optimization Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Anomaly detection has been extensively studied in various fields, with applications in different domains. For instance, the authors in [ 1 ] provided a survey of anomaly detection techniques for wireless sensor networks.

deep reinforcement learning anomaly detection

In [ 2 ]authors reviewed the problem of anomaly detection in home automation systems. During the detection process, the decision maker is allowed to observe only one of the N processes at a time. The distribution of the observations depends on whether the target is normal or not.

In this setting, the objective of the decision maker is to minimize the observation delay and dynamically determine all abnormal processes. The original active hypothesis testing problem was investigated in [ 3 ]. Based on this work, several recent studies proposed more advanced anomaly detection techniques in more complicated and realistic situations.

For example, the authors in [ 4 ] considered the case where the decision maker has only limited information on the distribution of the observation under each hypothesis. In [ 5 ]the performance measure is the Bayes risk that takes into account not only the sample complexity and detection errors, but also the costs associated with switching across processes. Moreover, authors in [ 6 ] considered the scenario that in some of the experiments, the distributions of the observations under different hypotheses are not distinguishable, and extended this work to a case with heterogenous processes [ 7 ]where the observation in each cell is independent and identically distributed i.

Also, the study of stopping rule has drawn much interest. For instance, in [ 8 ]improvements were achieved over prior studies since the proposed decision threshold can be applied in more general cases. The authors in [ 9 ] leveraged the central limit theorem for the empirical measure in the test statistic of the composite hypothesis Hoeffding test, so as to establish weak convergence results for the test statistic, and, thereby, derive a new estimator for the threshold needed by the test.

Recently, machine learning -based methods have also been applied to such hypothesis testing problems. In this work, we consider N independent processes, where each of the processes could be in either normal or abnormal state.Suppose, you are a credit card holder and on an unfortunate day it got stolen.

Payment Processor Companies like PayPal do keep a track of your usage pattern so as to notify in case of any dramatic change in the usage pattern. The patterns include transaction amounts, the location of transactions and so on. If a credit card is stolen, it is very likely that the transactions may vary largely from the usual ones.

This is where among many other instances the companies use the concepts of anomalies to detect the unusual transactions that may take place after the credit card theft. Noise and anomalies are not the same. So, how noise looks like in the real world? People tend to buy a lot of groceries at the start of a month and as the month progresses the grocery shop owner starts to see a vivid decrease in the sales. Then he starts to give discounts on a number of grocery items and also does not fail to advertise about the scheme.

This discount scheme might cause an uneven increase in sales but are they normal? They, sure, are not. These are noises more specifically stochastic noises. By now, we have a good idea of how anomalies look like in a real-world setting.

Allow me to quote the following from classic book Data Mining. Concepts and Techniques by Han et al. Could not get any better, right? To be able to make more sense of anomalies, it is important to understand what makes an anomaly different from noise. The way data is generated has a huge role to play in this.

For the normal instances of a dataset, it is more likely that they were generated from the same process but in case of the outliers, it is often the case that they were generated from a different process s.

In the above figure, I show you what it is like to be outliers within a set of closely related data-points. The closeness is governed by the process that generated the data points.

From this, it can be inferred that the process for generated those two encircled data-points must have been different from that one that generated the other ones. But how do we justify that those red data points were generated by some other process? While doing anomaly analysis, it is a common practice to make several assumptions on the normal instances of the data and then distinguish the ones that violate these assumptions. More on these assumptions later!

The above figure may give you a notion that anomaly analysis and cluster analysis may be the same things. They are very closely related indeed, but they are not the same! They vary in terms of their purposes. While cluster analysis lets you group similar data points, anomaly analysis lets you figure out the odd ones among a set of data points. We saw how data generation plays a crucial role in anomaly detection.

So, it will be worth enough to discuss what might lead towards the creation of anomalies in data. The way anomalies are generated hugely varies from domain to domain, application to application. In all of the above-mentioned applications, the general idea of normal and abnormal data-points is similar. Abnormal ones are those which deviate hugely from the normal ones. These deviations are based on the assumptions that are taken while associating the data points to normal group.


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