scholarly journals SALAD: An Exploration of Split Active Learning based Unsupervised Network Data Stream Anomaly Detection using Autoencoders

2021 ◽  
Author(s):  
Christopher Nixon ◽  
Mohamed Sedky ◽  
Mohamed Hassan
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Data Streams ◽  
Data Stream ◽  
Learning Strategy ◽  
Network Data ◽  
Anomaly Detector ◽  
Active Learning Strategy

<div>Machine learning based intrusion detection systems monitor network data streams for cyber attacks. Challenges in this space include detection of unknown attacks, adaptation to changes in the data stream such as changes in underlying behaviour, the human cost of labeling data to retrain the machine learning model and the processing and memory constraints of a real-time data stream. Failure to manage the aforementioned factors could result in missed attacks, degraded detection performance, unnecessary expense or delayed detection times. This research evaluated autoencoders, a type of feed-forward neural network, as online anomaly detectors for network data streams. The autoencoder method was combined with an active learning strategy to further reduce labeling cost and speed up training and adaptation times, resulting in a proposed Split Active Learning Anomaly Detector (SALAD) method. The proposed method was evaluated with the NSL-KDD, KDD Cup 1999, and UNSW-NB15 data sets, using the scikit-multiflow framework. Results demonstrated that a novel Adaptive Anomaly Threshold method, combined with a split active learning strategy offered superior anomaly detection performance with a labeling budget of just 20%, significantly reducing the required human expertise to annotate the network data. Processing times of the autoencoder anomaly detector method were demonstrated to be significantly lower than traditional online learning methods, allowing for greatly improved responsiveness to attacks occurring in real time. Future research areas are applying unsupervised threshold methods, multi-label classification, sample annotation, and hybrid intrusion detection.</div>

scholarly journals SALAD: An Exploration of Split Active Learning based Unsupervised Network Data Stream Anomaly Detection using Autoencoders

2021 ◽  
Author(s):  
Christopher Nixon ◽  
Mohamed Sedky ◽  
Mohamed Hassan
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Data Streams ◽  
Data Stream ◽  
Learning Strategy ◽  
Network Data ◽  
Anomaly Detector ◽  
Active Learning Strategy

<div>Machine learning based intrusion detection systems monitor network data streams for cyber attacks. Challenges in this space include detection of unknown attacks, adaptation to changes in the data stream such as changes in underlying behaviour, the human cost of labeling data to retrain the machine learning model and the processing and memory constraints of a real-time data stream. Failure to manage the aforementioned factors could result in missed attacks, degraded detection performance, unnecessary expense or delayed detection times. This research evaluated autoencoders, a type of feed-forward neural network, as online anomaly detectors for network data streams. The autoencoder method was combined with an active learning strategy to further reduce labeling cost and speed up training and adaptation times, resulting in a proposed Split Active Learning Anomaly Detector (SALAD) method. The proposed method was evaluated with the NSL-KDD, KDD Cup 1999, and UNSW-NB15 data sets, using the scikit-multiflow framework. Results demonstrated that a novel Adaptive Anomaly Threshold method, combined with a split active learning strategy offered superior anomaly detection performance with a labeling budget of just 20%, significantly reducing the required human expertise to annotate the network data. Processing times of the autoencoder anomaly detector method were demonstrated to be significantly lower than traditional online learning methods, allowing for greatly improved responsiveness to attacks occurring in real time. Future research areas are applying unsupervised threshold methods, multi-label classification, sample annotation, and hybrid intrusion detection.</div>

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Class Prior Estimation in Active Positive and Unlabeled Learning

2020 ◽  
Author(s):  
Lorenzo Perini ◽  
Vincent Vercruyssen ◽  
Jesse Davis
Keyword(s):  
Active Learning ◽  
Anomaly Detection ◽  
Learning Strategy ◽  
Important Task ◽  
Positive And Unlabeled Learning ◽  
Active Learning Strategy

Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random, which is the primary assumption underpinning existing approaches to estimating the class prior from PU data. We analyze this new setting and design an algorithm that is able to estimate the class prior for a given active learning strategy. Empirically, we show that our approach accurately recovers the true class prior on a benchmark of anomaly detection datasets and that it does so more accurately than existing methods.

scholarly journals Enhancing generic capabilities and metacognitive awareness of first-year nursing students using active learning strategy

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Carmen Wing Han Chan ◽  
Fiona Wing Ki Tang ◽  
Ka Ming Chow ◽  
Cho Lee Wong
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Active Learning ◽  
Flipped Classroom ◽  
Learning Strategy ◽  
Interactive Learning ◽  
Metacognitive Awareness ◽  
University Education ◽  
Strong Impact ◽  
Active Learning Strategy

Abstract Background Developing students’ generic capabilities is a major goal of university education as it can help to equip students with life-long learning skills and promote holistic personal development. However, traditional didactic teaching has not been very successful in achieving this aim. Kember and Leung’s Teaching and Learning Model suggests an interactive learning environment has a strong impact on developing students’ generic capabilities. Metacognitive awareness is also known to be related to generic capability development. This study aimed to assess changes on the development of generic capabilities and metacognitive awareness after the introduction of active learning strategy among nursing students. Methods This study adopted a quasi-experimental single group, matched pre- and posttest design. It was conducted in a school of nursing at a university in Hong Kong. Active learning approaches included the flipped classroom (an emphasis on pre-reading) and enhanced lectures (the breaking down of a long lecture into several mini-lectures and supplemented by interactive learning activities) were introduced in a foundational nursing course. The Capabilities Subscale of the Student Engagement Questionnaire and the Metacognitive Awareness Inventory were administered to two hundred students at the start (T0) and at the end of the course (T1). A paired t-test was performed to examine the changes in general capabilities and metacognitive awareness between T0 and T1. Results A total of 139 paired pre- and post-study responses (69.5 %) were received. Significant improvements were observed in the critical thinking (p < 0.001), creative thinking (p = 0.03), problem-solving (p < 0.001) and communication skills (p = 0.04) with the implementation of active learning. Significant changes were also observed in knowledge of cognition (p < 0.001) and regulation of cognition (p < 0.001) in the metacognitive awareness scales. Conclusions Active learning is a novel and effective teaching approach that can be applied in the nursing education field. It has great potential to enhance students’ development of generic capabilities and metacognitive awareness.

STORYTELLING AND STORYACTING AS AN ACTIVE LEARNING STRATEGY

1999 ◽  
Vol 20 (3) ◽  
pp. 347-352 ◽  
Author(s):  
Karen Cachevki Williams ◽  
Margaret Cooney ◽  
Jane Nelson
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Active Learning Strategy

scholarly journals PENGGUNAAN STRATEGI PEMBELAJARAN AKTIF EVERYONE IS A TEACHER HERE (ETH) TERHADAP KEMAMPUAN PEMAHAMAN KONSEP MATEMATIKA SISWA KELAS VII

2018 ◽  
Vol 4 (1) ◽  
pp. 95-100
Author(s):  
Sri Yunita Ningsih ◽  
Gustimalasari Gustimalasari
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Control Group ◽  
Control Group Design ◽  
Concept Understanding ◽  
Group Design ◽  
Quasi Experimental ◽  
Active Learning Strategy ◽  
And Control ◽  
Better Than

Abstract. This research has been made to know skill of student’s concept by using active learning strategy everyone is teacher here (ETH). Beside that this study aims to measure student’s concept understanding with statistical test between Experimental Class (Active Learning Strategy Everyone Is Teacher Here) and control class (Conventional Learning ). The population was seventh grade of SMPN 3 Lirik consist 94 students in three classes. Sample was took randomly, experiment class ( VII.2 ) and control class ( VII.I ) This research was experiment, the form of this research was Quasi Experimental Design with randomized subject posttest only control group design. based on statistic data processing has been retrieved - t hitung -3,159 smaller than - t table was -2,000 and based on t test has been retrieved -thitung < -t table so Ho rejected and Ha received. So that the writer conclude that skill of math student’s concept understanding by using active learning Strategy Everyone Is Teacher Here (ETH) is better than conventional concept understanding.Keywords: Everyone Is A Teacher Here, Concept Understanding

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