Log-Based Malicious Activity Detection Using Machine and Deep Learning

2020 ◽  
pp. 581-604
Author(s):  
Katarzyna A. Tarnowska ◽  
Araav Patel
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Malicious Activity

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques

Suspicious activity detection using deep learning in secure assisted living IoT environments

2020 ◽  
Author(s):  
G. Vallathan ◽  
A. John ◽  
Chandrasegar Thirumalai ◽  
SenthilKumar Mohan ◽  
Gautam Srivastava ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Activity Detection ◽  
Suspicious Activity

Human Activity Detection Using Deep Learning and Bracelet with Bluetooth Transmitter

2021 ◽  
Author(s):  
Stevan Cakic ◽  
Stevan Sandi ◽  
Daliborka Nedic ◽  
Srdan Krco ◽  
Tomo Popovic
Keyword(s):  
Deep Learning ◽  
Human Activity ◽  
Activity Detection ◽  
Human Activity Detection

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

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.

Deep learning approaches for video-based anomalous activity detection

2018 ◽  
Vol 22 (2) ◽  
pp. 571-601 ◽  
Author(s):  
Karishma Pawar ◽  
Vahida Attar
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
Activity Detection

An Approach to Mitigate Malware Attacks Using Netfilter's Hybrid Frame in Firewall Security

2018 ◽  
Vol 9 (1) ◽  
pp. 32-61 ◽  
Author(s):  
Nivedita Nahar ◽  
Prerna Dewan ◽  
Rakesh Kumar
Keyword(s):  
Source Code ◽  
Research Work ◽  
Hybrid Approach ◽  
Optimal Level ◽  
Malicious Code ◽  
Traffic Load ◽  
Activity Detection ◽  
Web Content ◽  
Packet Filtering ◽  
Malicious Activity

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.

Temporal pattern-based malicious activity detection in SCADA systems

2021 ◽  
Vol 102 ◽  
pp. 102153
Author(s):  
Amit Shlomo ◽  
Meir Kalech ◽  
Robert Moskovitch
Keyword(s):  
Temporal Pattern ◽  
Activity Detection ◽  
Malicious Activity ◽  
Scada Systems

scholarly journals Comparing Video Activity Classifiers within a Novel Framework

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1545
Author(s):  
Chiman Kwan ◽  
Bence Budavari ◽  
Bulent Ayhan
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
General Framework ◽  
Experimental Results ◽  
Analysis Tool ◽  
Activity Detection ◽  
Rule Based ◽  
Event Classification ◽  
Hybrid Combination ◽  
Rule Based Approach

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

Pecking activity detection in group-housed turkeys using acoustic data and a deep learning technique

2020 ◽  
Vol 194 ◽  
pp. 40-48 ◽  
Author(s):  
Abozar Nasirahmadi ◽  
Jennifer Gonzalez ◽  
Barbara Sturm ◽  
Oliver Hensel ◽  
Ute Knierim
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Acoustic Data ◽  
Learning Technique
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