scholarly journals Computer Network Attack Detection Using Enhanced Clustering Technologies

2022 ◽  
Vol 9 (6) ◽  
Author(s):  
Dhamyaa Salim Mutar
Keyword(s):  
Machine Learning ◽  
Computer Network ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Learning Technology ◽  
Feed Forward Neural Network ◽  
Network Attack ◽  
The Road ◽  
Network Intrusions ◽  
Smart Machine

The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.

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 Segmentation of Affected Crops using Deep Learning

2019 ◽  
Vol 8 (5S3) ◽  
pp. 232-234
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Monitoring System ◽  
Learning Algorithm ◽  
Target Area ◽  
Learning Technology ◽  
Machine Learning Algorithm ◽  
Normal Human ◽  
Human Monitoring ◽  
Disease Attack

Deep Learning technology can accurately predict the presence of diseases and pests in the agricultural farms. Upon this Machine learning algorithm, we can even predict accurately the chance of any disease and pest attacks in future For spraying the correct amount of fertilizer/pesticide to elimate host, the normal human monitoring system unable to predict accurately the total amount and ardent of pest and disease attack in farm. At the specified target area the artificial percepton tells the value accurately and give corrective measure and amount of fertilizers/ pesticides to be sprayed.

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 Attack Detection in IoT using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 7273-7278
Author(s):  
M. Anwer ◽  
M. U. Farooq ◽  
S. M. Khan ◽  
W. Waseemullah
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Web Traffic ◽  
Local Networks ◽  
Iot Devices

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

scholarly journals Comparative Study on TCP SYN Flood DDoS Attack Detection: A Machine Learning Algorithm Based Approach

2021 ◽  
Vol 16 ◽  
pp. 584-591
Author(s):  
S. Sumathi ◽  
R. Rajesh
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Research Goal ◽  
Ddos Attack Detection ◽  
F Measure

A most common attack on the internet network is a Distributed Denial of Service (DDoS) attack, which involves occupying computational resources and bandwidth to suppress services to potential clients. The attack scenario is to massively flood the packets. The attack is called a denial of service (DoS) if the attack originates from a single server, and a distributed denial of service (DDoS) if the attack originates from multiple servers. Control and mitigation of DDoS attacks have been a research goal for many scholars for over a decade, and they have achieved in delivering a few major DDoS detection and protection techniques. In the current state of internet use, how quickly and early a DDoS attack can be detected in broadcasting network transactions remains a key research goal. After the development of a machine learning algorithm, many potential methods of DDoS attack detection have been developed. The work presents the results of various experiments carried out using data mining and machine learning algorithms as well as a combination of these algorithms on the commonly available dataset named CAIDA for TCP SYN flood attack detection. Also, this work analysis the various performance metrics such as false positive rate, precision, recall, F-measure and receiver operating characteristic (ROC) using various machine learning algorithm. One-R(OR) with an ideal FPR value of 0.05 and recall value of 0.95,decision stump(DS) with an ideal precision value of o.93,PART with an excellent F-measure value of 0.91 are some of the performance metric values while performing TCP SYN flood attack detection.

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