Pervasive Computing
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2021 ◽  
P. Rajasekaran ◽  
V Magudeeswaran

Abstract In the era of information technology, the new types of cyber-attacks affect the performance of the network, which is very risky and cannot be restored quickly. In pervasive computing, there are more chances for such types of attacks since the personal data of the user is closely connected to the social environment. The research is performed using SNMP-MIB dataset, and feature selection are made by using the Enhanced Salp Swarm Optimization to select the optimal features to identify the attacks by using wrapper techniques. Then, various types of attacks are appropriately distinguished with proposed classifier Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging for high detection rate and accuracy. The value of performance metrics obtained from the proposed method outperforms the existing methods in terms of 99.9% accuracy, 99.8% in precision and detection rate is 99% in classifying different types of attacks.

2021 ◽  
Vol 77 ◽  
pp. 101491
Daniela Nicklas ◽  
Octav Chipara ◽  
Salil S. Kanhere ◽  
Delphine Reinhardt

Natã M. Barbosa ◽  
Gang Wang ◽  
Blase Ur ◽  
Yang Wang

To enable targeted ads, companies profile Internet users, automatically inferring potential interests and demographics. While current profiling centers on users' web browsing data, smartphones and other devices with rich sensing capabilities portend profiling techniques that draw on methods from ubiquitous computing. Unfortunately, even existing profiling and ad-targeting practices remain opaque to users, engendering distrust, resignation, and privacy concerns. We hypothesized that making profiling visible at the time and place it occurs might help users better understand and engage with automatically constructed profiles. To this end, we built a technology probe that surfaces the incremental construction of user profiles from both web browsing and activities in the physical world. The probe explores transparency and control of profile construction in real time. We conducted a two-week field deployment of this probe with 25 participants. We found that increasing the visibility of profiling helped participants anticipate how certain actions can trigger specific ads. Participants' desired engagement with their profile differed in part based on their overall attitudes toward ads. Furthermore, participants expected algorithms would automatically determine when an inference was inaccurate, no longer relevant, or off-limits. Current techniques typically do not do this. Overall, our findings suggest that leveraging opportunistic moments within pervasive computing to engage users with their own inferred profiles can create more trustworthy and positive experiences with targeted ads.

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