scholarly journals Decentralized Time-Window based Real-Time Anomaly Detection Mechanism (DTRAD) in Iot

2019 ◽  
Vol 8 (2) ◽  
pp. 1619-1625
Keyword(s):  
Anomaly Detection ◽  
High Performance ◽  
Time Window ◽  
Training Data ◽  
Weighted Voting ◽  
Detection Mechanism ◽  
Benchmark Data ◽  
Proposed Model ◽  
Mandatory Service ◽  
Training Data Selection

Detecting intrusions has become a mandatory service in IoT environments. This is due to the power and resource con-strained nature of the networks. This paper presents a Decentralized Time-Window based Anomaly Detection (DTRAD) model for cost and time effective intrusion detection in IoT environments. The proposed model is composed of time win-dow based training data selection module, which enables better detection and reduced bias. Training data are selected based on their temporal significance and the bag creation process is also temporally performed such that data with similar temporal signatures are grouped into same bags. The ensemble model is created and weighted voting is performed to ena-ble better results. The data reinforcement module enables new data to be appended to the training data, hence maintaining the recency of the data. Further, the entire process is decentralized, hence enabling data processing at appropriate nodes. This keeps the size of the training data low, hence reducing the computational complexity of the model to a large extent. Experiments were performed with benchmark data and comparisons were performed with recent models. Results indicate high performance of the proposed models.

scholarly journals Detecting Suspicious Timber Trades

2020 ◽  
Vol 34 (08) ◽  
pp. 13248-13254
Author(s):  
Debanjan Datta ◽  
M. Raihanul Islam ◽  
Nathan Self ◽  
Amelia Meadows ◽  
John Simeone ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Domain Knowledge ◽  
Open Data ◽  
Training Data ◽  
Application Framework ◽  
Domain Experts ◽  
Current State ◽  
Detection Approach ◽  
Proposed Model ◽  
Conservation Organizations

Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades.

Heterogeneous Weighted Voting-Based Ensemble (HWVE) for Root-Cause Analysis

2020 ◽  
Vol 13 (4) ◽  
pp. 63-74
Author(s):  
Blessy Selvam ◽  
Ravimaran S. ◽  
Sheba Selvam
Keyword(s):  
Root Cause Analysis ◽  
Training Data ◽  
Huge Number ◽  
Weighted Voting ◽  
Cause Analysis ◽  
Aspect Extraction ◽  
Root Cause ◽  
Proposed Model ◽  
Identification Phase ◽  
F Measure

Root-cause analysis has been one of the major requirements of the current information-rich world due to the huge number of opinions available online. This paper presents a heterogeneous weighted voting-based ensemble (HWVE) model for root-cause analysis. The proposed model is composed of an aspect extraction and filtering module, a model-based sentiment identification module, and a ranking module. Domain-based aspect ontologies are created using the available training data and is used for categorization. The input data is passed to the HWVE model for opinion identification and is in-parallel passed to the significance identification phase for aspect identification. The identified aspects are combined with their corresponding sentiments and ranked based on their ontological occurrence levels to provide the final categorized root-causes. Experiments were performed with the five-product dataset, and comparisons were performed with recent models. Results indicate that the proposed model exhibits improved performances of 5%-13% in terms of F-measure when compared to other models.

scholarly journals Brain Tumor Magnetic Resonance Image Classification: A Deep Learning Approach

2021 ◽  
Author(s):  
Jaya Lakshmi Machiraju ◽  
S. Nagaraja Rao
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Loss Function ◽  
Magnetic Resonance Images ◽  
Training Data ◽  
Learning Approach ◽  
Detection Mechanism ◽  
Validation Data ◽  
Proposed Model

Abstract From the past decade, many researchers are focused on the brain tumor detection mechanism using magnetic resonance images. The traditional approaches follow the feature extraction process from bottom layer in the network. This scenario is not suitable to the medical images. To address this issue, the proposed model employed Inception-v3 convolution neural network model which is a deep learning mechanism. This model extracts the multi-level features and classifies them to find the early detection of brain tumor. The proposed model uses the deep learning approach and hyper parameters. These parameters are optimized using the Adam Optimizer and loss function. The loss function helps the machines to model the algorithm with input data. The softmax classifier is used in the proposed model to classify the images in to multiple classes. It is observed that the accuracy of the Inception-v3 algorithm is recorded as 99.34% in training data and 89% accuracy at validation data.

scholarly journals Multi-Level Credit Card Fraud Detection

2019 ◽  
Vol 8 (4) ◽  
pp. 7288-7292
Keyword(s):  
Credit Card ◽  
High Performance ◽  
Fraud Detection ◽  
Huge Amount ◽  
Credit Card Fraud ◽  
Significance Level ◽  
Benchmark Data ◽  
Proposed Model ◽  
Level Model ◽  
Multi Level

Fraud detection in credit card transactions is one of the major requirements of the current business scenario due to the huge amount of losses associated with the domain. This work presents a multi-level model that can provide highly effective fraud detection in credit card transactions. The model is based on the amount for which the transaction is committed. The proposed MLFD model identifies the nature of the transaction and depending on the significance level of the transaction, the appropriate learning model is selected. Experiments were performed with the standard benchmark data and comparisons were performed with existing model in literature. Results shows that the proposed model exhibits high performance compared to the existing model.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

scholarly journals GAMPAL: an anomaly detection mechanism for Internet backbone traffic by flow size prediction with LSTM-RNN

2021 ◽  
Author(s):  
Taku Wakui ◽  
Takao Kondo ◽  
Fumio Teraoka
Keyword(s):  
Anomaly Detection ◽  
Short Term Memory ◽  
Denial Of Service ◽  
General Purpose ◽  
Traffic Information ◽  
Traffic Patterns ◽  
Detection Mechanism ◽  
Internet Service ◽  
Internet Backbone ◽  
Backbone Network

AbstractThis paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data). GAMPAL does not require labeled data to achieve general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Border Gateway Protocol Routing Information Base), GAMPAL introduces prefix aggregate. The BGP RIB entries are classified into prefix aggregates, each of which is identified with the first three AS (Autonomous System) numbers in the AS_PATH attribute. GAMPAL establishes a prediction model for traffic sizes based on past traffic sizes. It adopts a LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model that focuses on the periodicity of the Internet traffic patterns at a weekly scale. The validity of GAMPAL is evaluated using real traffic information, BGP RIBs exported from the WIDE backbone network (AS2500), a nationwide backbone network for research and educational organizations in Japan, and the dataset of an ISP (Internet Service Provider) in Spain. As a result, GAMPAL successfully detects anomalies such as increased traffic due to an event, DDoS (Distributed Denial of Service) attacks targeted at a stub organization, a connection failure, an SSH (Secure Shell) scan attack, and anomaly spam.

Sign in / Sign up

Export Citation Format

Share Document