Container Network Performance Anomaly Detection Based on Extended Berkeley Packet Filter and Machine Learning

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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Overview of Anomaly Detection techniques in Machine Learning

2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) ◽

10.1109/i-smac49090.2020.9243329 ◽

2020 ◽

Author(s):

Akanksha Toshniwal ◽

Kavi Mahesh ◽

Jayashree R.

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Detection Techniques

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A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks

Electronics ◽

10.3390/electronics10141635 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1635

Author(s):

Neeraj Chugh ◽

Geetam Singh Tomar ◽

Robin Singh Bhadoria ◽

Neetesh Saxena

Keyword(s):

Anomaly Detection ◽

Ad Hoc Networks ◽

Mobile Ad Hoc Networks ◽

Network Performance ◽

Ad Hoc ◽

Abnormal Behavior ◽

Energy Awareness ◽

Behavior Detection ◽

Mobile Ad Hoc ◽

Hoc Networks

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.

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