Semi-supervised anomaly detection in dynamic communication networks

Meng, Xuying; Wang, Suhang; Liang, Zong-Qi; Yao, Di; Zhou, Jihua; Zhang, Yujun

doi:10.1016/j.ins.2021.04.056

Cited by 22 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AnomRank classifies the types of anomalies into structural and node-relational anomalies and proposes a method to detect anomalies in two ways [56]. In addition, GAD approaches that incorporate graph clustering or community detection methods have been proposed for dynamic graphs [57,58]. However, since these methods concentrate on the time features related to the anomaly, the features that distinguish the nodes are somewhat overlooked.…”

Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Kim

Sohn

2022

Sensors

View full text Add to dashboard Cite

Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber–physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.

show abstract

Section: Graph-based Anomaly Detectionmentioning

confidence: 99%

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Kim

Sohn

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Semi-supervised approach is based on a mixed strategy, striving to enrich an unlabeled set with some labeled data, so as to improve the feature selection phase [ 5 ]. For example, Ref [ 18 ] proposes the SemiADC model for semi-supervised anomaly detection in dynamic communication networks, and experimental evaluation on real-world datasets demonstrates the effectiveness of SemiADC. Unsupervised and the semi-supervised approaches exhibit the drawback of neglecting potential correlations among features, resulting in the loss of crucial (as well as deterministic) piece of information.…”

Section: Introductionmentioning

confidence: 99%

Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model

Xue

Chen

Zheng

2022

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive monitoring and real-time alerts and the prediction effect of a single machine learning model on communication data containing multiple features is not ideal. To solve the problem, A prediction-then-detection anomaly detection method was proposed, and quantitative assessment of network anomalies was developed. Specifically, anomaly-free data was obtained by eliminating outliers, and the long short-term memory (LSTM) and autoregressive integral moving average (ARIMA) were combined via residual weighting to predict the future state of the key performance indicators (KPI) without outliers. Anomalies were identified using the error comparison between the prediction and actual values, and the network condition was quantified using the scoring method. It is observed that the proposed LSTM-ARIMA hybrid model has better prediction effect, which can well represent the performance of KPIs of the future state, and the prediction-then-detection anomaly detection method has excellent performance on both precision and recall.

show abstract

Deep Learning-Based Network Intrusion Detection Systems: A Systematic Literature Review

Mutembei,

Senekane,

van Zyl

2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Semi-supervised anomaly detection in dynamic communication networks

Cited by 22 publications

References 29 publications

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model

Deep Learning-Based Network Intrusion Detection Systems: A Systematic Literature Review

Contact Info

Product

Resources

About