scholarly journals An IoT based Machine Learning Technique for Efficient Online Load Forecasting

2021 ◽  
Vol 11 (2) ◽  
pp. 547-554
Author(s):  
B. Madhuravani ◽  
Srujan Atluri ◽  
Hema Valpadasu
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Data Set ◽  
Network Attacks ◽  
New Approach ◽  
Training Time ◽  
Network Attack ◽  
Integral Measure ◽  
Parallel Data ◽  
Learning Technique

Internet of Things (IoT) networks are computer networks that have an extreme issue with IT security and an issue with the monitoring of computer threats in specific. The paper proposes a combination of machine learning methods and parallel data analysis to address this challenge. The architecture and a new approach to the combination of the key classifiers intended for IoT network attacks are being developed. The issue classification statement is created in which the consistency ratio to training time is the integral measure of effectiveness. To improve the preparation and assessment pace, it is suggested to use the data processing and multi-threaded mode offered by Spark. In comparison, a preprocessing data set approach is proposed, resulting in a significant reduction in the length of the sample. An experimental review of the proposed approach reveals that the precision of IoT network attack detection is 100%, and the processing speed of the data collection increases with the number of parallel threads.

scholarly journals Research and development automatically generate detection rules for IDS based on machine learning technology

2022 ◽  
Vol 2 (14) ◽  
pp. 45-54
Author(s):  
Nguyen Huy Trung ◽  
Le Hai Viet ◽  
Tran Duc Thang
Keyword(s):  
Machine Learning ◽  
False Alarm ◽  
Attack Detection ◽  
Learning Technology ◽  
Network Attacks ◽  
New Approach ◽  
Network Attack ◽  
Detection Systems ◽  
Resource Requirements ◽  
System Resource

Abstract—Nowadays, there have been many signature-based intrusion detection systems deployed and widely used. These systems are capable of detecting known attacks with low false alarm rates, fast detection times, and little system resource requirements. However, these systems are less effective against new attacks that are not included in the ruleset. In addition, recent studies provide a new approach to the problem of detecting unknown types of network attacks based on machine learning and deep learning. However, this new approach requires a lot of resources, processing time and has a high false alarm rate. Therefore, it is necessary to find a solution that combines the advantages of the two approaches above in the problem of detecting network attacks. In this paper, the authors present a method to automatically generate network attack detection rules for the IDS system based on the results of training machine learning models. Through testing, the author proves that the system that automatically generates network attack detection rules for IDS based on machine learning meets the requirements of increasing the ability to detect new types of attacks, ensuring automatic effective updates of new signs of network attacks. Tóm tắt—Ngày nay, đã có nhiều hệ thống phát hiện xâm nhập dựa trên chữ ký được triển khai và sử dụng rộng rãi. Các hệ thống này có khả năng phát hiện các cuộc tấn công đã biết với tỷ lệ báo động giả thấp, thời gian phát hiện nhanh và yêu cầu ít tài nguyên hệ thống. Tuy nhiên, các hệ thống này kém hiệu quả khi chống lại các cuộc tấn công mới không có trong tập luật. Các nghiên cứu gần đây cung cấp một cách tiếp cận mới cho vấn đề phát hiện các kiểu tấn công mạng mới dựa trên học máy và học sâu. Tuy nhiên, cách tiếp cận này đòi hỏi nhiều tài nguyên, thời gian xử lý. Vì vậy, cần tìm ra giải pháp kết hợp ưu điểm của hai cách tiếp cận trên trong bài toán phát hiện tấn công mạng. Trong bài báo này, nhóm tác giả trình bày phương pháp tự động sinh luật phát hiện tấn công mạng cho hệ thống phát hiện xâm nhập dựa trên kết quả huấn luyện mô hình học máy. Qua thử nghiệm, tác giả chứng minh rằng phương pháp này đáp ứng yêu cầu tăng khả năng phát hiện chính xác các kiểu tấn công mới, đảm bảo tự động cập nhật hiệu quả các dấu hiệu tấn công mạng mới vào tập luật.

scholarly journals Detection of Network Attacks using Machine Learning: A New Approach

2021 ◽  
Vol 9 (12) ◽  
pp. 1881-1890
Author(s):  
Avinash R. Sonule
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Data Set ◽  
Network Attacks ◽  
New Approach ◽  
Detection Techniques ◽  
Network Intrusions

Abstract: The Cyber-attacks become the most important security problems in the today’s world. With the increase in use of computing resources connected to the Internet like computers, mobiles, sensors, IoTs in networks, Big Data, Web Applications/Server, Clouds and other computing resources, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. These intrusions detection techniques have been applied on various IDS datasets. UNSW-NB15 is the latest dataset. This data set contains different modern attack types and wide varieties of real normal activities. In this paper, we compare Naïve Bays algorithm with proposed probability based supervised machine learning algorithms using reduced UNSW NB15 dataset. Keywords: UNSW NB-15, Machine Learning, Naïve Bayes, All to Single (AS) features probability Algorithm

Deep learning for detecting logic-flaw-exploiting network attacks: An end-to-end approach

2021 ◽  
pp. 1-30
Author(s):  
Qingtian Zou ◽  
Anoop Singhal ◽  
Xiaoyan Sun ◽  
Peng Liu
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Network Data ◽  
Learning Models ◽  
Real World Data ◽  
Network Attacks ◽  
Network Attack ◽  
Public Network ◽  
Network Intrusion ◽  
End To End

Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

Network Attack Detection With SNMP-MIB Using Deep Neural Network

2020 ◽  
pp. 66-76
Author(s):  
Mouhammd Sharari Alkasassbeh ◽  
Mohannad Zead Khairallah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Information Technologies ◽  
Deep Neural Network ◽  
Detection System ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Network Attack ◽  
Security Breaches ◽  
Security Issues

Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.

scholarly journals Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

2019 ◽  
Vol 3 (1) ◽  
pp. 1-4
Author(s):  
Simon Hawatichke Chiwamba ◽  
Jackson Phiri ◽  
Philip O. Y. Nkunika ◽  
Mayumbo Nyirenda ◽  
Monica M. Kabemba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Fall Armyworm ◽  
Machine Learning Algorithms ◽  
Training Time ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Learning Technique ◽  
Computer Scientists ◽  
Learning Software

Automated entomology is one of the field that has received a fair attention from the computer scientists and its support disciplines. This can further be confirmed by the recent attention that the Fall Armyworm (FAW) (Spodoptera frugiperda) has received in Africa particularly the Southern African Development Community (SADC). As the FAW is known for its devastating effects, stakeholders such as the Food and Agriculture Organization (FAO), SADC and University of Zambia (UNZA) have agreed to develop robust early monitoring and warning system. To supplement the stakeholders’ efforts, we choose a branch of artificial intelligence that employs deep neural network architectures known as Google TensorFlow. It is an advanced state-of-the-art machine learning technique that can be used to identify the FAW moths. In this paper, we use Google TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models. We use the transfer learning technique to retrain the Inception v3 model in TensorFlow on the insect dataset, which reduces the training time and improve the accuracy of FAW moth identification. Our retrained model achieves a train accuracy of 57 – 60 %, cross entropy of 65 – 70% and validation accuracy of 

scholarly journals Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7070
Author(s):  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Rami Mustafa A. Mohammad ◽  
Sultan H. Almotiri ◽  
Samiha Mirza ◽  
...  
Keyword(s):  
Rapid Development ◽  
Attack Detection ◽  
Network Attacks ◽  
Research Directions ◽  
Network Attack ◽  
Detection Systems ◽  
Increased Risk ◽  
Use Of The Internet ◽  
Main Components ◽  
Network Technologies

The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

A study of job involvement prediction using machine learning technique

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youngkeun Choi ◽  
Jae Won Choi
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Performance ◽  
Job Involvement ◽  
Learning Technology ◽  
Machine Learning Technique ◽  
Data Set ◽  
Content Type ◽  
Hr Management ◽  
Learning Technique

Purpose Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique. Design/methodology/approach This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification. Findings In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance. Practical implications The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems. Originality/value This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.

scholarly journals Cyberattacks Detection and Analysis in a Network Log System Using XGBoost with ELK Stack

2021 ◽  
Author(s):  
Chao-Tung Yang ◽  
Yu-Wei Chan ◽  
Jung-Chun Liu Liu ◽  
Endah Kristiani ◽  
Cing-Han Lai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Abnormal Behavior ◽  
Attack Behavior ◽  
Learning Methods ◽  
Data Set ◽  
Log Data ◽  
Network Attack ◽  
Machine Learning Methods ◽  
Gradient Enhancement

Abstract The usage of artificial intelligence and machine learning methods on cyberattacks increasing significantly recently. For the defense method of cyberattacks, it is possible to detect and identify the attack event by observing the log data and analyzing whether it has abnormal behavior or not. This paper implemented the ELK Stack network log system (NetFlow Log) to visually analyze log data and present several network attack behavior characteristics for further analysis. Additionally, this system evaluated the extreme gradient enhancement (XGBoost), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) model for machine learning methods. Keras was used as a deep learning framework for building a model to detect the attack event. From the experiments, it can be confirmed that the XGBoost model has an accuracy rate of 96.01% for potential threats. The full attack data set can achieve 96.26% accuracy, which is better than RNN and DNN models.

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