Applied artificial immune on P2P network virus detection technology for information security

2014 ◽  
Author(s):  
Qiao Peili ◽  
Sun Wanting
Keyword(s):  
Information Security ◽  
Virus Detection ◽  
P2p Network ◽  
Artificial Immune ◽  
Detection Technology

scholarly journals Information Security Field Event Detection Technology Based on SAtt-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wentao Yu ◽  
Xiaohui Huang ◽  
Qingjun Yuan ◽  
Mianzhu Yi ◽  
Sen An ◽  
...  
Keyword(s):  
Information Security ◽  
Event Detection ◽  
Chain Structure ◽  
Data Set ◽  
Multimodal Data ◽  
Joint Learning ◽  
Candidate Event ◽  
Detection Technology ◽  
Sentence Classification ◽  
Lstm Network

Detecting information security events from multimodal data can help analyze the evolution of events in the security field. The Tree-LSTM network that introduces the self-attention mechanism was used to construct the sentence-vectorized representation model (SAtt-LSTM: Tree-LSTM with self-attention) and then classify the candidate event sentences through the representation results of the SAtt-LSTM model to obtain the event of the candidate event sentence types. Event detection using sentence classification methods can solve the problem of error cascade based on pipeline methods, and the problem of CNN or RNN cannot make full use of the syntactic information of candidate event sentences in methods based on joint learning. The paper treats the event detection task as a sentence classification task. In order to verify the effectiveness and superiority of the method in this paper, the DuEE data set was used for experimental verification. Experimental results show that this model has better performance than methods that use chain structure LSTM, CNN, or only Tree-LSTM.

scholarly journals Colorimetric Reverse Transcription–Loop-Mediated Isothermal Amplification Assay for Rapid Detection of SARS-CoV-2

2021 ◽  
Author(s):  
Meng Yee Lai ◽  
Soo Nee Tang ◽  
Yee Ling Lau
Keyword(s):  
Sensitivity And Specificity ◽  
Reverse Transcription ◽  
Virus Detection ◽  
Lamp Assay ◽  
Isothermal Amplification ◽  
Clinical Sensitivity ◽  
Loop Mediated Isothermal Amplification ◽  
Resource Limited ◽  
Detection Technology ◽  
Simple Detection

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been spreading rapidly all over the world. In the absence of effective treatments or a vaccine, there is an urgent need to develop a more rapid and simple detection technology of COVID-19. We describe a WarmStart colorimetric reverse transcription–loop-mediated isothermal amplification (RT-LAMP) assay for the detection of SARS-CoV-2. The detection limit for this assay was 1 copy/µL SARS-CoV-2. To test the clinical sensitivity and specificity of the assay, 37 positive and 20 negative samples were used. The WarmStart colorimetric RT-LAMP had 100% sensitivity and specificity. End products were detected by direct observation, thereby eliminating the need for post-amplification processing steps. WarmStart colorimetric RT-LAMP provides an opportunity to facilitate virus detection in resource-limited settings without a sophisticated diagnostic infrastructure.

A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

2019 ◽  
Author(s):  
Nguyen Vu Thanh ◽  
Dung Hoang Le ◽  
Tuan Dinh Le
Keyword(s):  
Detection Rate ◽  
False Positive Rate ◽  
Virus Detection ◽  
Deep Belief Network ◽  
Immune Network ◽  
Artificial Immune ◽  
High Detection Rate ◽  
Smart System ◽  
Belief Network ◽  
Artificial Immune Network

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system and the deep learning technique. The first stage is data extraction to create the main feature set. In the second stage, the Artificial Immune Network (aiNet) is used to build a clonal generation of malware detectors and improve the accuracy of unknown virus detection rate. Then they are trained with a deep belief network model to evaluate the performance of the system. As a result, our method can achieve a high detection rate of 98.86% on average with a very low false positive rate.

Analysis of Intrusion Detection System

2011 ◽  
Vol 460-461 ◽  
pp. 451-454
Author(s):  
Yue Sheng Gu ◽  
Hong Yu Feng ◽  
Jian Ping Wang
Keyword(s):  
Information Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Model ◽  
Detection Systems ◽  
Detection Technology ◽  
Technology Concepts

Intrusion detection system is an important device of information security. This article describes intrusion detection technology concepts, classifications and universal intrusion detection model, and analysis of the intrusion detection systems weaknesses and limitations. Finally, some directions for future research are addressed.

scholarly journals A Combination of Artificial Neural Network and Artificial Immune System for Virus Detection

2016 ◽  
Author(s):  
Mai Trong Khang ◽  
Vu Thanh Nguyen ◽  
Tuan Dinh Le
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
False Positive Rate ◽  
Virus Detection ◽  
Malicious Code ◽  
Immune Network ◽  
Artificial Immune ◽  
Positive Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, we propose an Artificial Neural Immune Network (ANIN) for virus detection. ANIN is a combination of Artificial Neural Network (ANN) and Artificial Immune Network (AiNet). In ANIN, each ANN is considered as a detector. A pool of initial detectors then undergoes a mature process, called AiNet, to improve its recognizing ability. Thus, more than one ANN objects can cooperate to detect malicious code. The experimental results show that ANIN can achieve a detection rate of 87.98% on average with an acceptable false positive rate.

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