Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model due to the imbalances in the dataset for discriminating attacks from the overall dataset. Multilayer Perceptron (MLP) technique will provide improvement in accuracy and increase the performance of detecting the attack and benign data from a balanced dataset. We have worked on the UGR'16 dataset publicly available for this work. Data wrangling has been done due to prepare test set from in the original set. We fed the neural network classifier larger input to the neural network in an increasing manner (i.e. 10000, 50000, 1 million) to see the distribution of features over the accuracy. We have implemented a GAN model that can produce samples of different attack labels (e.g. blacklist, anomaly spam, ssh scan). We have been able to generate as many samples as necessary based on the data sample we have taken from the UGR'16. We have tested the accuracy of our model with the imbalance dataset initially and then with the increasing the attack samples and found improvement of classification performance for the latter.

Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of this significant infrastructure could affect important social, environmental, and economic impacts. Furthermore, recognizing the optimized timeline for inspection of sewer pipelines are challenging tasks for the utility managers and other authorities. Regular examination of sewer networks is not cost-effective due to limited time and high cost of assessment technologies and a large inventory of pipes. To avoid such obstacles, various researchers endeavored to improve infrastructure condition assessment methodologies to maintain sewer pipe systems at the desired condition. Sewer condition prediction models are developed to provide a framework to forecast the future condition of pipes to schedule inspection frequencies. The main goal of this study is to develop a predictive model for wastewater pipes using random forest classification. Predictive models can effectively predict sewer pipe condition and can increase the certainty level of the predictive results and decrease uncertainty in the current condition of wastewater pipes. The developed random forest classification model has achieved a stratified test set false negative rate, the false positive rate, and an excellent area under the ROC curve of 0.81 in a case study application for the City of LA, California. An area under the ROC curve > 0.80 indicates the developed model is an "excellent" choice for predicting the condition of individual pipes in a sewer network. The deterioration models can be used in the industry to improve the inspection timeline and maintenance planning.

Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark. Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation.

Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn't just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential. Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score -- typically between 0 and 1 -- to indicate the likelihood that it's fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not -- typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable. False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed.

Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

--In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions. The stunning success of deep learning technology, convolu-tional neural networks (CNN) in particular [1]-[3], has led to a rush towards technology development for new applications that ten years ago would have been considered unrealistic.

In order to get one number that tells us how good our curve is, we can calculate the Area Under the ROC Curve, or ROC AUC score. The more top-left your curve is the higher the area and hence higher ROC AUC score. Alternatively, it can be shown that ROC AUC score is equivalent to calculating the rank correlation between predictions and targets. From an interpretation standpoint, it is more useful because it tells us that this metric shows how good at ranking predictions your model is. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.

We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anomaly measure for a point that we call PIDScore, which measures the minimum density of data points over all subcubes containing the point. We present PIDForest: a random forest based algorithm that finds anomalies based on this definition. We show that it performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.