Network Malicious Behavior Detection Using Bidirectional LSTM

2018 ◽  
pp. 627-635
Author(s):  
Wenwu Chen ◽  
Su Yang ◽  
Xu An Wang ◽  
Wei Zhang ◽  
Jindan Zhang
Keyword(s):  
Behavior Detection ◽  
Malicious Behavior ◽  
Bidirectional Lstm

Vision-Based Real-time Human Malicious Behavior Detection

2021 ◽  
Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin
Keyword(s):  
Real Time ◽  
Behavior Detection ◽  
Malicious Behavior

Android Malicious Behavior Detection Based on Sensitive API Monitoring

2013 ◽  
Author(s):  
Quan QIAN ◽  
◽  
Jing CAI ◽  
Rui ZHANG ◽  
◽  
...  
Keyword(s):  
Behavior Detection ◽  
Malicious Behavior

Analysis Based on iOS Application Malicious Behavior Detection

2014 ◽  
Vol 602-605 ◽  
pp. 2321-2325
Author(s):  
Yin He Wu ◽  
Dai Ping Li
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Modern Technology ◽  
Smart Phones ◽  
Matching Algorithm ◽  
Behavior Detection ◽  
Malicious Behavior ◽  
Ios Application

Due to the highly developed modern technology,Smart phones and other mobile devices are become more and more universal. Most of those devices are used to process or store sensitive and confidential data.Consequently,it may cause many problems,such as privacy disclosure,mobile phone virus,spyware,etc. In order to solve those issues,We need to monitor applications`s behaviour to tell those malicious ones. Here we use MobileSubstrate to hook every sensitive system API the application invokes in iOS planform,and send this invocation to our matching algorithm,the matching algorithm will evaluate if the API are being invoked in a safe way according to Application API Review Model.If a application trying to call some APIs which is totally unnecessary,we can reject this invoke and give user a warning.

scholarly journals A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 529
Author(s):  
Mahdi Rabbani ◽  
Yongli Wang ◽  
Reza Khoshkangini ◽  
Hamed Jelodar ◽  
Ruxin Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Learning Approaches ◽  
Behavior Detection ◽  
Detection Systems ◽  
Malicious Behavior ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Detection And Recognition

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.

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