Security of emerging non-volatile memories: Attacks and defenses

2016 ◽  
Author(s):  
Kaveh Shamsi ◽  
Yier Jin
Keyword(s):  
Attacks And Defenses

scholarly journals Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

2021 ◽  
pp. 1-1
Author(s):  
Yao Deng ◽  
Tiehua Zhang ◽  
Guannan Lou ◽  
Xi Zheng ◽  
Jiong Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Driving ◽  
Attacks And Defenses

scholarly journals Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3922
Author(s):  
Sheeba Lal ◽  
Saeed Ur Rehman ◽  
Jamal Hussain Shah ◽  
Talha Meraj ◽  
Hafiz Tayyab Rauf ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Feature Fusion ◽  
Speckle Noise ◽  
Fundus Images ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Adversarial Attack ◽  
Retinal Fundus Images ◽  
Attacks And Defenses ◽  
Retinal Fundus

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

EM Side Channels in Hardware Security: Attacks and Defenses

2021 ◽  
pp. 1-1
Author(s):  
Jiaji He ◽  
Xiaolong Guo ◽  
Mark Tehranipoor ◽  
Apostol Vassilev ◽  
Yier Jin
Keyword(s):  
Hardware Security ◽  
Security Attacks ◽  
Side Channels ◽  
Attacks And Defenses

Information Attacks and Defenses on the Social Web

2018 ◽  
pp. 216-235
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Real World ◽  
Web Sites ◽  
Online Social Networks ◽  
Social Web ◽  
Security Measures ◽  
The Social ◽  
Attacks And Defenses ◽  
Interest In Technology

Social web sites are used daily by many millions of users. They have attracted users with very weak interest in technology, including absolute neophytes of computers in general. Common users of social web sites often have a carefree attitude in sharing information. Moreover, some system operators offer sub-par security measures, which are not adequate for the high value of the published information. For all these reasons, online social networks suffer more and more attacks by sophisticated crackers and scammers. To make things worse, the information gathered from social web sites can trigger attacks to even more sensible targets. This work reviews some typical social attacks that are conducted on social networking systems, describing real-world examples of such violations and analyzing in particular the weakness of password mechanisms. It then presents some solutions that could improve the overall security of the systems.

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