Cloud-centric blockchain public key infrastructure for big data applications

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface

Detecting compromised social network accounts using deep learning for behavior and text analyses

Social networks allow people to connect to one another. Over time, these accounts become an essential part of one’s online identity. The account stores various personal data and contains one’s network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected

Design and Evaluation of Multipoint-to-Point Multicast Flow Control

Multipoint communication has been an increasingly focused topic in computer communication networks, including both the Internet and the ATM networks. We have previously presented, analyzed, and evaluated new point-tomultipoint ABR flow control algorithms. In this paper, we focus on multipoint-to-point flow control. As the major objective of ABR service is to provide minimum-loss, fair service to data traffic, an effective merge-point scheme for multipoint-to-point flow control should guarantee some suitable fairness. In this paper, we first examine the "essential fairness" concept proposed by Wang and Schwartz for point-to-multipoint flow control in the Internet. We extend and enhance the concept to the multipoint-to-point ABR flow control. A general algorithm guaranteeing essential fairness is presented, with a detailed implementation on top of the ERICA unicast algorithm proposed by Jain, et. al. The general algorithm may be used for a wide range of fairness specifications to accommo..