Trust-based federated learning for network anomaly detection

2021 ◽  
pp. 1-11
Author(s):  
Naiyue Chen ◽  
Yi Jin ◽  
Yinglong Li ◽  
Luxin Cai
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Private Information ◽  
Information Disclosure ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Local Data ◽  
Daily Work ◽  
Network Anomaly Detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

BANBAD

2011 ◽  
pp. 253-276
Author(s):  
Rajeev Agrawal ◽  
Chaoli Cai ◽  
Ajay Gupta ◽  
Rajib Paul ◽  
Raed Salih
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
False Alarm ◽  
Joint Probability ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Training Dataset ◽  
Joint Probability Distribution ◽  
High Detection ◽  
Hoc Networks

Anomaly detection is an important aspect of any security mechanism. We present an efficient anomaly detection algorithm, named BANBAD. Using Belief Networks (BNs), the algorithm identifies abnormal behavior of a feature, like inappropriate energy consumption of a node in a network. By applying structure learning techniques to training dataset, BANBAD establishes a joint probability distribution among relevant features, such as average velocity, displacement, local computation and communication time, energy consumption, and response time of a node of the network. A directed acyclic graph (DAG) is used to represent the features and their dependencies. Using a training process, BANBAD maintains dynamic, updated profiles of network node behaviors and uses specific Bayesian inference algorithm to distinguish abnormal behavior during testing. BANBAD works especially well in ad hoc networks. Extensive simulation results demonstrate that a centralized BANBAD achieves low false alarm rates, below 5%, and high detection rates, greater than 95%. We also show that BANBAD detects anomaly efficiently and accurately in two real datasets. The key for achieving such high performance is bounding the false alarm rate at certain predefined threshold value. By fine-tuning at the threshold, we can achieve high detection rate as well.

IP Network Anomaly Detection using Machine Learning

2019 ◽  
Author(s):  
Roshan Nair ◽  
Chaithanya Pramodh Kasula ◽  
Sravanthi Vankayala ◽  
Niloy Chakraborty
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Ip Network ◽  
Network Anomaly Detection

scholarly journals A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study

10.2196/26320 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e26320
Author(s):  
Takafumi Koyama ◽  
Shusuke Sato ◽  
Madoka Toriumi ◽  
Takuro Watanabe ◽  
Akimoto Nimura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carpal Tunnel Syndrome ◽  
Data Collection ◽  
Anomaly Detection ◽  
Carpal Tunnel ◽  
Medical Condition ◽  
Screening Method ◽  
Detection Algorithm ◽  
Thenar Muscle ◽  
Tunnel Syndrome

Background Carpal tunnel syndrome (CTS) is a medical condition caused by compression of the median nerve in the carpal tunnel due to aging or overuse of the hand. The symptoms include numbness of the fingers and atrophy of the thenar muscle. Thenar atrophy recovers slowly postoperatively; therefore, early diagnosis and surgery are important. While physical examinations and nerve conduction studies are used to diagnose CTS, problems with the diagnostic ability and equipment, respectively, exist. Despite research on a CTS-screening app that uses a tablet and machine learning, problems with the usage rate of tablets and data collection for machine learning remain. Objective To make data collection for machine learning easier and more available, we developed a screening app for CTS using a smartphone and an anomaly detection algorithm, aiming to examine our system as a useful screening tool for CTS. Methods In total, 36 participants were recruited, comprising 36 hands with CTS and 27 hands without CTS. Participants controlled the character in our app using their thumbs. We recorded the position of the thumbs and time; generated screening models that classified CTS and non-CTS using anomaly detection and an autoencoder; and calculated the sensitivity, specificity, and area under the curve (AUC). Results Participants with and without CTS were classified with 94% sensitivity, 67% specificity, and an AUC of 0.86. When dividing the data by direction, the model with data in the same direction as the thumb opposition had the highest AUC of 0.99, 92% sensitivity, and 100% specificity. Conclusions Our app could reveal the difficulty of thumb opposition for patients with CTS and screen for CTS with high sensitivity and specificity. The app is highly accessible because of the use of smartphones and can be easily enhanced by anomaly detection.

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

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