Anomaly detection on in-home activities data based on time interval

Poh, Soon-Chang; Tan, Yi-Fei; Cheong, Soon-Nyean; Ooi, Chee‐Pun; Tan, Wooi-Haw

doi:10.11591/ijeecs.v15.i2.pp778-785

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…While Recall or also known as sensitivity is the ratio of predicted positive examples correctly to total positive examples. The F1 score is a balanced measure indicator based on Precision and Recall (Poh et al, 2019). For each model, the Precision, Recall, F1 score, and Accuracy are calculated.…”

Section: Model Evaluationmentioning

confidence: 99%

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Elhadad¹,

Tan²,

Tan³

2022

IJTech

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Electricity demand is increasing proportionally to the increase in power usage. Without a doubt, energy efficiency has gained significant importance and attention, with one of the primary concerns being the detection and forecasting of abnormal consumption. In this paper, the authors proposed a method to predict the occurrence of abnormal consumption behavior in advance. The proposed method utilizes the Isolation Forest algorithm to label the smart meter electricity consumption readings as normal or abnormal. It generates a sequence of data with varying lengths. Based on the data sequence, two supervised machine learning algorithms, Random Forest, and Decision Tree were developed to forecast the occurrence of power anomaly consumption. Experiment results showed that the proposed methods consistently detect and predict the abnormal status 30 minutes ahead. There is no significant difference between Random Forest and Decision Tree performance on different smart meter readings, dataset sizes, and other data sequence lengths. The proposed methods portray an alternative approach that is capable of autolabel normal and abnormal data and, as a result, dealing with the sequence of label data in the prediction process while avoiding the dynamic behavior of the power consumption data.

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Section: Model Evaluationmentioning

confidence: 99%

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Elhadad¹,

Tan²,

Tan³

2022

IJTech

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“…Wherefore, this gap is a favorable area for research to construct or devise a model regarding the behaviors of DDoS attacks. For this reason, there are several approaches that had not focused on detection ICMPv6-DDoS attacks are proposed which is based on anomaly detection [13], [33], [41], [42]. Additionally, must design a model detection the ICMPv6-DDoS patterns in IPv6 network depends the review outlines the features and protection constraints of IPv6 detection systems focusing mainly on ICMPv6-DDoS attacks.…”

Section: Figure 1 Users Reaching Google Services Over Ipv6mentioning

confidence: 99%

“…From anomaly detection advantages, is determined attacks more effectively than the signature-based IDS with few incorrect positive rates, in addition, automated training is generally used to distinction normal system behavior from abnormal [41]. The anomaly detection consists of four steps the procedure data collection, data preparation, modelling and model evaluation [42]. In this paper there are divided the anomaly detection into two types are rule-based and artificial Intelligence-based.…”

Section: Figure 5 Taxonomy Of Anomaly-based Detectionmentioning

confidence: 99%

Detection of ICMPv6-based DDoS attacks using anomaly based intrusion detection system: A comprehensive review

AIghuraibawi

Abdullah

Manickam

et al. 2021

IJECE

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Security network systems have been an increasingly important discipline since the implementation of preliminary stages of Internet Protocol version 6 (IPv6) for exploiting by attackers. IPv6 has an improved protocol in terms of security as it brought new functionalities, procedures, i.e., Internet Control Message Protocol version 6 (ICMPv6). The ICMPv6 protocol is considered to be very important and represents the backbone of the IPv6, which is also responsible to send and receive messages in IPv6. However, IPv6 Inherited many attacks from the previous internet protocol version 4 (IPv4) such as distributed denial of service (DDoS) attacks. DDoS is a thorny problem on the internet, being one of the most prominent attacks affecting a network result in tremendous economic damage to individuals as well as organizations. In this paper, an exhaustive evaluation and analysis are conducted anomaly detection DDoS attacks against ICMPv6 messages, in addition, explained anomaly detection types to ICMPv6 DDoS flooding attacks in IPv6 networks. Proposed using feature selection technique based on bio-inspired algorithms for selecting an optimal solution which selects subset to have a positive impact of the detection accuracy ICMPv6 DDoS attack. The review outlines the features and protection constraints of IPv6 intrusion detection systems focusing mainly on DDoS attacks.

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“…In an aging world, it is essential to maintain good health and independence for as long as possible. Instead of hospitalization or institutionalization, the elderly and disabled can be assisted in their own environment 24 hours a day with many "smart" devices [19]. Solutions are needed to address the care demands of older adults to live independently.…”

Section: Technologiesmentioning

confidence: 99%

Fall risk in the aging population: fall prevention using smartphones technology and multiscale sample entropy

et al. 2020

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Falls are an important aspect of older people's health because they trigger major injuries and even death in one-third of fallen patients, making them a major public health problem. Given the risk of physical and psychological injury, if serious injuries occur as a result of a fall, prevention is an important consideration in today's health care landscape, where the population is predominantly adult worldwide. This paper presents the applicability of a simple technique of analysis of gait signals captured by mobile devices with the objective to the generation of early warnings on the risk of falls in older adults, which correlates with subjective scales. The technique is tested in a population of patients showing results of the significant risk of falls in patients that the subjective scales could not detect, demonstrating that mobile devices and signal processing can become important tools in the service of elderly care in fall risk prevention.

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Anomaly detection on in-home activities data based on time interval

Cited by 3 publications

References 20 publications

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Detection of ICMPv6-based DDoS attacks using anomaly based intrusion detection system: A comprehensive review

Fall risk in the aging population: fall prevention using smartphones technology and multiscale sample entropy

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