A Localized Learning Approach Applied to Human Activity Recognition

Amer, Ahmed Youssef Ali; Aerts, Jean‐Marie; Vanrumste, Bart; Luca, Stijn

doi:10.1109/mis.2020.2964738

Cited by 8 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At runtime, the substitution policies are of paramount importance because of their capability to update the instances stored with more representative ones but need to deal with very stringent and strict requirements. Previous work has avoided the use of kNN (considering it offline and/or online learning) in the context of wearable devices due to the constraints of memory on those devices, see, e.g., [24], and in the context of online learning, e.g., [54]. The authors are thus forced to use other techniques, in most cases with higher online learning complexity, and without analyzing the impact of kNN.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

2020

View full text Add to dashboard Cite

The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes.

show abstract

Section: Analysis Of Resultsmentioning

confidence: 99%

kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

2020

View full text Add to dashboard Cite

show abstract

“…A localised learning method involves local data samples collected from a single source, the learning and testing occur locally [18], where it is generally more effective with a larger amount of data. This method often provides a high detection accuracy over IID data samples with a similar probability distribution to the training data samples.…”

Section: Localised Learningmentioning

confidence: 99%

Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection

Sarhan

Layeghy

Moustafa

et al. 2022

Preprint

View full text Add to dashboard Cite

The uses of Machine Learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational network. However, it has been very challenging to design an ML-based detection system using heterogeneous network data samples originating from different sources and organisations. This is mainly due to privacy concerns and the lack of a universal format of datasets. In this paper, we propose a collabora-tive cyber threat intelligence sharing scheme to allow multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system. The threat intelligence sharing scheme utilises two critical aspects for its application; the availability of network data traffic in a common format to allow for the extraction of meaningful patterns across data sources and the adoption of a federated learning mechanism to avoid the necessity of sharing sensitive users’ information between organisations. As a result, each organisation benefits from the intelligence of other organisations while maintaining the privacy of its data internally. In this paper, the framework has been designed and evaluated using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. In addition, two other common scenarios are considered in the evaluation process; a centralised training method where local data samples are directly shared with other organisations and a localised training method where no threat intelligence is shared. The results demonstrate the efficiency and effectiveness of the proposed framework by designing a universal ML model effectively classifying various benign and intrusive traffic types originating from multiple organisations without the need for inter-organisational data exchange.

show abstract

“…Standard ML techniques usually follow either a localised or a centralised learning method [13]. A localised learning method involves local data samples collected from a single endpoint, the learning and testing occur locally on the endpoint [14], where it is generally more effective with a larger amount of data. This method often provides a high detection accuracy over Independent and Identically Distributed (IID) [15] data samples with a similar probability distribution to the local data samples.…”

Section: Introductionmentioning

confidence: 99%

A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection

Sarhan¹,

Layeghy²,

Moustafa³

et al. 2021

Preprint

View full text Add to dashboard Cite

The uses of Machine Learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated in a single organisation. However, it has been very challenging to design an ML-based detection system by utilising heterogeneous network data samples originating from several sources. This is mainly due to privacy concerns and the lack of a universal format of datasets. In this paper, we propose a collaborative federated learning scheme to address these issues. The proposed framework allows multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system. The threat intelligence scheme utilises two critical aspects for its application; the availability of network data traffic in a common format to allow for the extraction of meaningful patterns across data sources. Secondly, the adoption of a federated learning mechanism to avoid the necessity of sharing sensitive users' information between organisations. As a result, each organisation benefits from other organisations cyber threat intelligence while maintaining the privacy of its data internally/ The model is trained locally and only the updated weights are shared with the remaining participants in the federated averaging process. The framework has been designed and evaluated in this paper by using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. Two other common scenarios are considered in the evaluation process; a centralised training method where the local data samples are shared with other organisations and a localised training method where no threat intelligence is shared. The results demonstrate the efficiency and effectiveness of the proposed framework by designing a universal ML model effectively classifying benign and intrusive traffic originating from multiple organisations without the need for local data exchange.

show abstract

A Localized Learning Approach Applied to Human Activity Recognition

Cited by 8 publications

References 47 publications

kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection

A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection

Contact Info

Product

Resources

About