scholarly journals Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Martin Sarnovsky ◽  
Jan Paralic
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Models ◽  
Knowledge Model ◽  
Detection Systems ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Multi Stage ◽  
Machine Learning Approach ◽  
Machine Learning Models

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.

scholarly journals Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

2021 ◽  
Vol 1 (2) ◽  
pp. 252-273
Author(s):  
Pavlos Papadopoulos ◽  
Oliver Thornewill von Essen ◽  
Nikolaos Pitropakis ◽  
Christos Chrysoulas ◽  
Alexios Mylonas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Learning Models ◽  
Detection Systems ◽  
Network Intrusion ◽  
Robust Model ◽  
Significant Probability ◽  
Adversarial Examples ◽  
Attack Surface

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

scholarly journals Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

2018 ◽  
pp. 55-74 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Solar Water Heater ◽  
Learning Method ◽  
Optimization Strategy ◽  
Learning Models ◽  
Water Heater ◽  
Solar Water ◽  
Knowledge Based ◽  
Machine Learning Models

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

2020 ◽  
Vol 20 (6) ◽  
pp. 3303-3313 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models
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