A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection

2018 ◽  
pp. 351-371 ◽  
Author(s):  
Hemalatha Jeyaprakash ◽  
KavithaDevi M. K. ◽  
Geetha S.
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Rate ◽  
High Dimensional ◽  
Learning Approaches ◽  
Learning Tools ◽  
High Detection Rate ◽  
Comparative Review ◽  
High Detection

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.

An Intrusion Detection Based on Markov Model

2011 ◽  
Vol 268-270 ◽  
pp. 988-993 ◽  
Author(s):  
Hai Sheng Li
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection Rate ◽  
Markov Chain Model ◽  
Training Data ◽  
Chain Model ◽  
High Detection Rate ◽  
Detection Model ◽  
System Calls ◽  
High Detection

This paper presents an Intrusion detection technique through anomaly-detection, and proposes Modeling algorithm using training data and anomaly detection model. In this technique, a Markov-chain model is founded based on the characteristic pattern, which is a subsequence of system calls if this sequence satisfies the certain support degree. Experiments show that the method with high detection rate and low false alarm rate is valuable to intrusion detection.

scholarly journals Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems

2020 ◽  
Vol 12 (10) ◽  
pp. 167
Author(s):  
Niraj Thapa ◽  
Zhipeng Liu ◽  
Dukka B. KC ◽  
Balakrishna Gokaraju ◽  
Kaushik Roy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
High Detection Rate ◽  
Detection Systems ◽  
Network Intrusion ◽  
High Detection

The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system.

scholarly journals Network Anomaly Detection System with Optimized DS Evidence Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Liu ◽  
Xiaofeng Wang ◽  
Kaiyu Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Computer Network ◽  
Detection System ◽  
Evidence Theory ◽  
Optimization Methods ◽  
High Detection Rate ◽  
Detection Model ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

A Approach to Change Detection for HR Image

2014 ◽  
Vol 971-973 ◽  
pp. 1449-1453
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Change Detection ◽  
Detection Rate ◽  
False Detection Rate ◽  
False Detection ◽  
High Detection Rate ◽  
High Detection ◽  
Self Adaptation ◽  
Ica Algorithm ◽  
Different Characteristics ◽  
Better Than

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.

scholarly journals Machine Learning approaches for Anomaly Detection in Multiphase Flow Meters

2019 ◽  
Vol 52 (11) ◽  
pp. 212-217 ◽  
Author(s):  
Tommaso Barbariol ◽  
Enrico Feltresi ◽  
Gian Antonio Susto
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Multiphase Flow ◽  
Learning Approaches ◽  
Flow Meters

scholarly journals Stroke Risk Analysis, a System With a High Detection Rate of Atrial Fibrillation in Stroke and Transient Ischemic Attack

Stroke ◽  
2020 ◽  
Vol 51 (1) ◽  
pp. 262-267 ◽  
Author(s):  
Meritxell Gomis ◽  
Antoni Dávalos ◽  
Francisco Purroy ◽  
Pere Cardona ◽  
Ana Rodríguez-Campello ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Risk Analysis ◽  
Transient Ischemic Attack ◽  
Detection Rate ◽  
Stroke Risk ◽  
High Detection Rate ◽  
High Detection ◽  
Ischemic Attack
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