25 papers · ranked by Valyu relevance
Markus Goldstein, Seiichi Uchida, Dongxiao Zhu
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised…
Ralph Foorthuis
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of publications on the topic, no comprehensive and concrete overviews of the…
Daniel Ramotsoela, Adnan Abu-Mahfouz, Gerhard Hancke
The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these…
Inderjeet Singh, N. Hemachandra
Anomaly detection is an essential problem in machine learning. Application areas include network security, health care, fraud detection, etc., involving high-dimensional datasets. A typical anomaly detection system always faces the class-imbalance problem in the form of a vast difference in the sample sizes of…
Nassir Mohammad
In the fields of statistics and unsupervised machine learning a fundamental and well-studied problem is anomaly detection. Anomalies are difficult to define, yet many algorithms have been proposed. Underlying the approaches is the nebulous understanding that anomalies are rare, unusual or inconsistent with the majority…
Shachar Siboni, Asaf Cohen
Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical…
Mahsa Mozaffari, Keval Doshi, Yasin Yilmaz, Anastasios Doulamis + 2 more
'Nikolaos Doulamis' 'Athanasios Voulodimos'] This paper considers the real-time detection of abrupt and persistent anomalies in high-dimensional data streams. The goal is to detect anomalies quickly and accurately so that the appropriate countermeasures could be taken in time before the system possibly gets harmed. We…
Shlok Mehendale, Aditya Challa, Rahul Yedida, Sravan Danda + 2 more
SHLOK MEHENDALE, CSIS, BITS Pilani KK Birla Goa Campus, India ADITYA CHALLA, CSIS, BITS Pilani KK Birla Goa Campus, India RAHUL YEDIDA, Lexis Nexis, USA SRAVAN DANDA, CSIS, BITS Pilani KK Birla Goa Campus, India SANTONU SARKAR, CSIS, BITS Pilani KK Birla Goa Campus, India SNEHANSHU SAHA, CSIS, BITS Pilani KK Birla Goa…
Guansong Pang, Choubo Ding, Chunhua Shen, Anton van den Hengel
—Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples), assuming no access to any labeled anomaly data. One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples…
Łukasz Wawrowski, Andrzej Białas, Adrian Kajzer, Artur Kozłowski + 10 more
'Rafał Kurianowicz' 'Marek Sikora' 'Agnieszka Szymańska-Kwiecień' 'Mariusz Uchroński' 'Miłosz Białczak' 'Maciej Olejnik' 'Marcin Michalak' 'Leandros Maglaras' 'Helge Janicke' 'Mohamed Amine Ferrag'] It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be…
Andreas Groll, Akshat Khanna, Leonid Zeldin
Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to…
Jaydip Sen, Sidra Mehtab
Machine learning and data mining algorithms play important roles in designing intrusion detection systems. Based on their approaches towards the detection of attacks in a network, intrusion detection systems can be broadly categorized into two types. In the misuse detection systems, an attack in a system is detected…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Neema Davis, Gaurav Raina, Krishna Jagannathan
In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks. We obtain the prediction errors from an LSTM model, and then apply three statistical models based on (i) the Gaussian…
Vito P. Pastore, Thomas G. Zimmerman, Sujoy Biswas, Simone Bianco
The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification…
Dima Kagan, Juman Jubran, Esti Yeger-Lotem, Michael Fire
Anomaly detection in graphs is critical in various domains, notably in medicine and biology, where anomalies often encapsulate pivotal information. Here, we focused on network analysis of molecular interactions between proteins, which is commonly used to study and infer the impact of proteins on health and disease. In…
Paul Morris, Cory Simon
In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal, "business-as-usual" conditions. We provide a methodology, illustrated by example, to computationally predict the performance of a gas sensor array design for detecting anomalous gas compositions. Specifically, we…
Cong Ma, Carl Kingsford
Algorithms to infer isoform expression abundance from RNA-seq have been greatly improved in accuracy during the past ten years. However, due to incomplete reference transcriptomes, mapping errors, incomplete sequencing bias models, or mistakes made by the algorithm, the quantification model sometimes could not explain…
Zhiwei Ma, Daniel S. Reich, Sarah Dembling, Jeff H. Duyn + 1 more
The UK Biobank (UKB) is a large-scale epidemiological study and its imaging component focuses on the pre-symptomatic participants. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes…
Authors not listed
Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility. However, maintaining quality control without constant human oversight remains a critical challenge. Here, we present a novel machine learning framework for…
Victor H. R. Nogueira, Rishabh Sharma, Rafael V. C. Guido, Michael J. Keiser
As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings…
Nima Rafiee, Rahil Gholamipoor, Markus Kollmann
Recent progress in computer-aided technologies has had a considerable impact on helping experts with a reliable and fast diagnosis of abnormal samples. In particular, self-supervised and self-distillation techniques have advanced automated out-of-distribution (OOD) detection in the image domain. Further improvements in…
Tomer Michael-Pitschaze, Niv Cohen, Dan Ofer, Yedid Hoshen + 1 more
Many advances in biomedicine can be attributed to identifying unusual proteins and genes. Many of these proteins’ unique properties were discovered by manual inspection, which is becoming infeasible at the scale of modern protein datasets. Here, we propose to tackle this challenge using anomaly detection methods that…
Hossein Estiri, Shawn N Murphy
To evaluate the utility of encoding for outlier detection in clinical observation data from Electronic Health Records (EHR). This article presents a semi-supervise encoding approach (super-encoding) for constructing a non-linear exemplar data distribution from EHR data and detecting non-conforming observations as…
Raheel Hammad, Sownyak Mondal
Auxetics are a rare class of materials that exhibit a negative Poisson's ratio. The existence of these auxetic materials is rare but has a large number of applications in designing exotic materials. We build a complete machine learning framework to detect Auxetic materials as well as Poisson's ratio of non-auxetic…