Keshav Kasichainula

Keshav Kasichainula, Hadi Mansourifar, Weidong Shi

Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. It reveals the vulnerability of deep convolutional neural networks (CNNs) as state-of-the-art building block for face recognition models against adversarial examples, which can cause certain consequences for secure systems. Gradient-based adversarial attacks are widely studied before and proved to be successful against face recognition models. However, finding the optimized perturbation per each face needs to submitting the significant number of queries to the target model. In this paper, we propose recursive adversarial attack on face recognition using automatic face warping which needs extremely limited number of queries to fool the target model. Instead of a random face warping procedure, the warping functions are applied on specific detected regions of face like eyebrows, nose, lips, etc. We evaluate the robustness of proposed method in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but hard-label predictions and confidence scores are provided by the target model.

Rabimba Karanjai, Yang Lu, Dana Alsagheer et al.

Logs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone to errors. LogBabylon is a centralized log data consolidating solution that leverages Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) technology. LogBabylon interprets the log data in a human-readable way and adds insight analysis of the system performance and anomaly alerts. It provides a paramount view of the system landscape, enabling proactive management and rapid incident response. LogBabylon consolidates diverse log sources and enhances the extracted information's accuracy and relevancy. This facilitates a deeper understanding of log data, supporting more effective decision-making and operational efficiency. Furthermore, LogBabylon streamlines the log analysis process, significantly reducing the time and effort required to interpret complex datasets. Its capabilities extend to generating context-aware insights, offering an invaluable tool for continuous monitoring, performance optimization, and security assurance in dynamic computing environments.

Lin Chen, Lei Xu, Zhimin Gao et al.

Recent advances in the blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behaviors of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for performance improvements, such as IOTA and Graphcoin. The first question that comes to people's minds is what improvements that a blockchain system would see by leveraging these new advances. In this paper, we consider three major metrics for a blockchain system: full verification, scalability, and finality-duration. We { establish a formal framework and} prove that no blockchain system can achieve full verification, high scalability, and low finality-duration simultaneously. We observe that classical blockchain systems like Bitcoin achieves full verification and low finality-duration, Harmony and Ethereum 2.0 achieve low finality-duration and high scalability. As a complementary, we design a non-linear blockchain system that achieves full verification and scalability. We also establish, for the first time, the trade-off between scalability and finality-duration.

Zhimin Gao, Lei Xu, Keshav Kasichainula et al.

Bitcoin brings a new type of digital currency that does not rely on a central system to maintain transactions. By benefiting from the concept of decentralized ledger, users who do not know or trust each other can still conduct transactions in a peer-to-peer manner. Inspired by Bitcoin, other cryptocurrencies were invented in recent years such as Ethereum, Dash, Zcash, Monero, Grin, etc. Some of these focus on enhancing privacy for instance crypto note or systems that apply the similar concept of encrypted notes used for transactions to enhance privacy (e.g., Zcash, Monero). However, there are few mechanisms to support the exchange of privacy-enhanced notes or assets on the chain, and at the same time preserving the privacy of the exchange operations. Existing approaches for fair exchanges of assets with privacy mostly rely on off-chain/side-chain, escrow or centralized services. Thus, we propose a solution that supports oblivious and privacy-protected fair exchange of crypto notes or privacy enhanced crypto assets. The technology is demonstrated by extending zero-knowledge based crypto notes. To address "privacy" and "multi-currency", we build a new zero-knowledge proving system and extend note format with new property to represent various types of tokenized assets or cryptocurrencies. By extending the payment protocol, exchange operations are realized through privacy enhanced transactions (e.g., shielded transactions). Based on the possible scenarios during the exchange operation, we add new constraints and conditions to the zero-knowledge proving system used for validating transactions publicly.