AutoOC: Automated multi-objective design of deep autoencoders and one-class classifiers using grammatical evolution

One-Class Classification (OCC) corresponds to a subclass of unsupervised Machine Learning (ML) that is valuable when labeled data is non-existent. In this paper, we present AutoOC, a computationally efficient Grammatical Evolution (GE) approach that automatically searches for OCC models. AutoOC assumes a multi-objective optimization, aiming to increase the OCC predictive performance while reducing the ML training time. AutoOC also includes two execution speedup mechanisms, a periodic training sampling, and a multi-core fitness evaluation. In particular, we study two AutoOC variants: a pure Neuroevolution (NE) setup that optimizes two types of deep learning models, namely dense Autoencoder (AE) and Variational Autoencoder (VAE); and a general Automated Machine Learning (AutoML) ALL setup that considers five distinct OCC base learners, specifically Isolation Forest (IF), Local Outlier Factor (LOF), One-Class SVM (OC-SVM), AE and VAE. Several experiments were conducted, using eight public OpenML datasets and two validation scenarios (unsupervised and supervised). The results show that AutoOC requires a reasonable amount of execution time and tends to obtain lightweight OCC models. Moreover, AutoOC provides quality predictive results, outperforming a baseline IF for all analyzed datasets and surpassing the best supervised OpenML human modeling for two datasets.- (undefined

Using supervised and one-class automated machine learning for predictive maintenance

Predictive Maintenance (PdM) is a critical area that is benefiting from the Industry 4.0 advent. Recently, several attempts have been made to apply Machine Learning (ML) to PdM, with the majority of the research studies assuming an expert-based ML modeling. In contrast with these works, this paper explores a purely Automated Machine Learning (AutoML) modeling for PdM under two main approaches. Firstly, we adapt and compare ten recent open-source AutoML technologies focused on a Supervised Learning. Secondly, we propose a novel AutoML approach focused on a One-Class (OC) Learning (AutoOneClass) that employs a Grammatical Evolution (GE) to search for the best PdM model using three types of learners (OC Support Vector Machines, Isolation Forests and deep Autoencoders). Using recently collected data from a Portuguese software company client, we performed a benchmark comparison study with the Supervised AutoML tools and the proposed AutoOneClass method to predict the number of days until the next failure of an equipment and also determine if the equipments will fail in a fixed amount of days. Overall, the results were close among the compared AutoML tools, with supervised AutoGluon obtaining the best results for all ML tasks. Moreover, the best supervised AutoML and AutoOneClass predictive results were compared with two manual ML modeling approaches (using a ML expert and a non-ML expert), revealing competitive results.This work was executed under the project Cognitive CMMS - Cognitive Computerized Maintenance Management System, NUP: POCI-01-0247-FEDER-033574, co-funded by the Incentive System for Research and Technological Development , from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020. We wish to thank the anonymous reviewers for their helpful comments

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

SAME: Sample Reconstruction against Model Extraction Attacks

While deep learning models have shown significant performance across various domains, their deployment needs extensive resources and advanced computing infrastructure. As a solution, Machine Learning as a Service (MLaaS) has emerged, lowering the barriers for users to release or productize their deep learning models. However, previous studies have highlighted potential privacy and security concerns associated with MLaaS, and one primary threat is model extraction attacks. To address this, there are many defense solutions but they suffer from unrealistic assumptions and generalization issues, making them less practical for reliable protection. Driven by these limitations, we introduce a novel defense mechanism, SAME, based on the concept of sample reconstruction. This strategy imposes minimal prerequisites on the defender's capabilities, eliminating the need for auxiliary Out-of-Distribution (OOD) datasets, user query history, white-box model access, and additional intervention during model training. It is compatible with existing active defense methods. Our extensive experiments corroborate the superior efficacy of SAME over state-of-the-art solutions. Our code is available at https://github.com/xythink/SAME.Comment: Accepted by AAAI 202

Analytic Case Study Using Unsupervised Event Detection in Multivariate Time Series Data

Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative pre-survey and tasks participants, in their natural setting to annotate events. Part 2 asks participants to perform annotation with the assistance of EDAS’s pre-annotations. Results from Part 1 and Part 2 exhibit low inter-coder agreement for both human-derived and automation-assisted event annotations. Qualitative analysis of survey results showed low trust and confidence in the event detection automation. One correlation or interpretation to the low confidence is that the low inter-coder agreement means that the humans do not share the same idea of what an annotation product should be

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable