Log-Based Malicious Activity Detection Using Machine and Deep Learning

Author(s):  
Katarzyna A. Tarnowska ◽  
Araav Patel
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe

Author(s):  
G. Vallathan ◽  
A. John ◽  
Chandrasegar Thirumalai ◽  
SenthilKumar Mohan ◽  
Gautam Srivastava ◽  
...  

Author(s):  
Stevan Cakic ◽  
Stevan Sandi ◽  
Daliborka Nedic ◽  
Srdan Krco ◽  
Tomo Popovic

Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.


2018 ◽  
Vol 22 (2) ◽  
pp. 571-601 ◽  
Author(s):  
Karishma Pawar ◽  
Vahida Attar

2018 ◽  
Vol 9 (1) ◽  
pp. 32-61 ◽  
Author(s):  
Nivedita Nahar ◽  
Prerna Dewan ◽  
Rakesh Kumar

With the steady advancements in the technology, the network security is really important these days to protect information from attackers. In this research, the main focus is on designing strong firewall filtering rules so that detection of malicious code is achieved to an optimal level. A proposed framework is introduced to improve the performance parameters such as Server response time, Web content analysis, Bandwidth, and the performance of the Network traffic load. This research work defines a new set of IPtable rules achieved by modifying the kernel source code. This is done using OpenBSD kernel source code, which results in the formation of a mini-firewall. Therefore, a new hybrid approach is proposed by adding packet filtering rules and SNORT technology in mini-firewall for malicious activity detection. It is an efficient and practical technique which will be helpful to mitigate the malware attacks and secure LAMP server. Experimental analysis has been done to conclude that around 70-75% malicious activity can be reduced by using the proposed technique.


2021 ◽  
Vol 102 ◽  
pp. 102153
Author(s):  
Amit Shlomo ◽  
Meir Kalech ◽  
Robert Moskovitch

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1545
Author(s):  
Chiman Kwan ◽  
Bence Budavari ◽  
Bulent Ayhan

Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area


2020 ◽  
Vol 194 ◽  
pp. 40-48 ◽  
Author(s):  
Abozar Nasirahmadi ◽  
Jennifer Gonzalez ◽  
Barbara Sturm ◽  
Oliver Hensel ◽  
Ute Knierim

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