Environment–Reactive Malware Behavior: Detection and Categorization

Author(s):  
Smita Naval ◽  
Vijay Laxmi ◽  
Manoj S. Gaur ◽  
Sachin Raja ◽  
Muttukrishnan Rajarajan ◽  
...  
Keyword(s):  
2008 ◽  
Vol 128 (11) ◽  
pp. 1649-1656 ◽  
Author(s):  
Hironobu Satoh ◽  
Fumiaki Takeda ◽  
Yuhki Shiraishi ◽  
Rie Ikeda

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1635
Author(s):  
Neeraj Chugh ◽  
Geetam Singh Tomar ◽  
Robin Singh Bhadoria ◽  
Neetesh Saxena

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.


2016 ◽  
Vol 59 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Xi Wang ◽  
Nickolai Zeldovich ◽  
M. Frans Kaashoek ◽  
Armando Solar-Lezama

Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.


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