Breaking News: The Latest Hacker Attacks and Defenses

2014 ◽  
Author(s):  
Claudia V. Mantaras ◽  
Ana M. Mosquera ◽  
Jose Martinez ◽  
Carmen N. Velez ◽  
Vivian Tamayo ◽  
...  

Author(s):  
Tobias Conradi ◽  
Reinhard Keil ◽  
Norbert Otto Eke ◽  
Hartmut Winkler ◽  
Hannelore Bublitz ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Ahmed Al-Rawi ◽  
Vishal Shukla

BACKGROUND In this study, we examined the activities of automated social media accounts or bots that tweet or retweet referencing #COVID-19 and #COVID19. OBJECTIVE The purpose of this study is to identify bot accounts to understand the nature of messages sent by them on COVID-19. Social media bots have been widely discussed in academic literature as some kind of moral panic mostly in relation to spreading controversial and politically polarized messages or in connection to problematic health bots (Broniatowski et al., 2018; Allem & Ferrara, 2018). The findings of this study, however, show that bots that reference COVID-19 mostly mention mainstream media and credible health sources while spreading breaking news on the pandemic or urging people to stay at home. These results align with previous research on the possible benefits, advantages, or possibilities afforded by the use of health chatbots (Brandtzaeg & Følstad, 2018; Skjuve & Brandtzæg, 2018; Kretzschmar et al., 2019; Greer et al., 2019). METHODS We used a mixed approach mostly comprised of several digital methods in this study. First, we collected 50,811,299 tweets and retweets referencing #COVID-19 and #COVID19 for a period of over two months from February 12 until April 18, 2020. We focused on these two hashtags because they are standard terms used by WHO and other official sources. From a total sample of over 50 million tweets, we used a mixed method to extract more than 185,000 messages posted by 127 bots. RESULTS Unlike the literature on health bots that associate them with anti-social activities, our findings show that the majority of these bots tweet, retweet and mention mainstream media outlets and credible official sources, promote health protection and telemedicine, and disseminate breaking news on the number of casualties and deaths caused by COVID-19. CONCLUSIONS Despite that some literature on social media bots highlight the controversial and anti-social nature of automated accounts, the findings of this study show that the majority of bots spread news on and awareness of COVID-19 risks while citing and referencing mainstream media outlets and credible health sources. We argue that there might be financial incentives behind designing some of these bots. However and if monitored and updated with credible information by health agencies themselves, we believe that bots can be useful during health crises due to their efficiency and speed in spreading valuable information, some of which is crucial for public health. CLINICALTRIAL N/A


2020 ◽  
Vol 30 ◽  
pp. S169
Author(s):  
Mr Cedric Happi Mbakam ◽  
Mr Joel Rousseau ◽  
Mr Antoine Guyon ◽  
Mr Guillaume Tremblay ◽  
Mr Francis-Gabriel Bégin ◽  
...  

Author(s):  
Yao Deng ◽  
Tiehua Zhang ◽  
Guannan Lou ◽  
Xi Zheng ◽  
Jiong Jin ◽  
...  

Author(s):  
Méabh Kenny ◽  
Katie Duffy ◽  
Carol Hilliard ◽  
Mary O’Rourke ◽  
Gillian Fortune ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3922
Author(s):  
Sheeba Lal ◽  
Saeed Ur Rehman ◽  
Jamal Hussain Shah ◽  
Talha Meraj ◽  
Hafiz Tayyab Rauf ◽  
...  

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.


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