The Design of FPGA-Based Real Time Intrusion Detection NIC

2011 ◽  
Vol 58-60 ◽  
pp. 2585-2591
Author(s):  
Bing Yuan Cheng ◽  
Kai Jin Qiu ◽  
Zu Yong Yang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Response Speed ◽  
Pci Bus ◽  
Operation System ◽  
Detection Algorithms ◽  
Network Bandwidth ◽  
Detection Technology ◽  
Organic Combination ◽  
Ideal Method

The amount of intrusion detection calculation based on software is heavy, which can not satisfy the needs of modern network bandwidth; the intrusion detection technology based on hardware is an ideal method for accelerating network processing. The thesis proposes a design scheme for FPGA-based real time intrusion detection NIC, and introduces the hardware and software structure of the system n details. The system communicates with the operation system directly via PCI bus, achieves the organic combination of software detection and hardware detection, and overcomes the slow response speed of the system when only software is used for intrusion detection. In system hardware detection, with FPGA as core, arranging various intrusion detection algorithms in FPGA for parallel running can improve the real time and reliability of the system.

Research and Implementation of Intrusion Detection Based Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 946-951 ◽  
Author(s):  
Guang Cai Cui ◽  
Bai Tong Liu
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Real Time ◽  
Original Data ◽  
Network Data ◽  
Initial Population ◽  
Classification Rules ◽  
Detection Technology ◽  
Network Intrusions ◽  
Data Source

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

The Role of Data Mining in Intrusion Detection Technology

2008 ◽  
pp. 463-473 ◽  
Author(s):  
Amalia Agathou ◽  
Theodoros Tzouramanis
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Real Time ◽  
Computer System ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Past ◽  
Detection Technology

Over the past few years, the Internet has changed computing as we know it. The more possibilities and opportunities develop, the more systems are subject to attack by intruders. Thus, the big question is about how to recognize and handle subversion attempts. One answer is to undertake the prevention of subversion itself by building a completely secure system. However, the complete prevention of breaches of security does not yet appear to be possible to achieve. Therefore these intrusion attempts need to be detected as soon as possible (preferably in real time) so that appropriate action might be taken to repair the damage. This is what an intrusion detection system (IDS) does. IDSs monitor and analyze the events occurring in a computer system in order to detect signs of security problems. However, intrusion detection technology has not yet reached perfection. This fact has provided data mining with the opportunity to make several important contributions and improvements to the field of IDS technology (Julisch, 2002).

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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