DaeHun Nyang

TL;DR: This paper systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains, and outlines several attacks, including selfish mining, the 51% attack, DNS attacks, distributed denial-of-service (DDoS) attacks, consensus delay, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks.

TL;DR: This paper systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains, and outlines several attacks, including selfish mining, the 51% attack, Domain Name System attacks, distributed denial-of-service (DDoS) attacks, consensus delay, orphaned blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks.

TL;DR: This paper builds a detection mechanism of IoT malware utilizing control flow graphs (CFGs), and shows that IoT malware samples have a large number of edges despite a smaller number of nodes, which demonstrate a richer flow structure and higher complexity.

TL;DR: AUToSen is a deep-learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user based on deep learning to identify user distinct behavior from the embedded sensors with and without the user’s interaction with the smartphone.

TL;DR: Wang et al. as discussed by the authors pointed out that Das's protocol is vulnerable to an offline password guessing attack, and also showed a countermeasure to overcome the vulnerability without sacrificing any efficiency and usability.