Nasrin Sohrabi

Pengkun Ren, Dong Hai, Nasrin Sohrabi et al.

Directed acyclic graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols achieve high throughput by decoupling dissemination from agreement and allowing many vertices to be committed concurrently. This same concurrency, however, weakens ordering evidence at the execution boundary: once units are committed in a shared DAG frontier, their final linearization is driven by traversal or deterministic tie-breaking rather than verifiable structural precedence. Prior fair-ordering designs address ambiguity by collecting or reconstructing transaction-level ordering evidence within the consensus workflow. While effective, this couples ordering with agreement and places ordering logic on the critical path. This paper presents Multi-Round Visibility (MRV), a post-consensus structural ordering layer for DAG-based BFT that reinterprets the committed DAG as an ordering evidence substrate. Committed vertices inherently carry authenticated creator, round, and ancestry metadata, enabling replicas to derive multi-round structural visibility without extra consensus-path messages. MRV accumulates this visibility within a bounded evidence horizon, compares concurrently committed atomic units of fairness (AUFs) after they coexist in the DAG, and derives precedence constraints from Byzantine-robust visibility advantages. When the DAG lacks such constraints, MRV exposes and resolves the remaining ambiguity through deterministic graph completion rather than hiding it inside traversal rules. We implement MRV on a Narwhal/Tusk-based prototype. Evaluation across 5-50 replicas under various fault settings shows MRV preserves the high-throughput regime of the DAG-BFT stack, proving it provides post-consensus structural ordering without burdening the consensus-critical path.

Minh Trung Tran, Nasrin Sohrabi, Zahir Tari et al.

We identify the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), posing a large-scale, persistent, and growing risk to the ecosystem. Unlike traditional scams such as rug pulls or honeypots (USENIX Sec'19, USENIX Sec'23), SLID gradually siphons funds from liquidity pools over extended periods, making detection significantly more challenging. In this paper, we conducted the first large-scale empirical analysis of 319,166 liquidity pools across six major decentralized exchanges (DEXs) since 2018. We identified 3,117 SLID affected liquidity pools, resulting in cumulative losses of more than US$103 million. We propose a rule-based heuristic and an enhanced machine learning model for early detection. Our machine learning model achieves a detection speed 4.77 times faster than the heuristic while maintaining 95% accuracy. Our study establishes a foundation for protecting DeFi investors at an early stage and promoting transparency in the DeFi ecosystem.

Qi Xiong, Hai Dong, Nasrin Sohrabi et al.

Sybil attacks pose a significant threat to federated learning, as malicious nodes can collaborate and gain a majority, thereby overwhelming the system. Therefore, it is essential to develop countermeasures that ensure the security of federated learning environments. We present a novel defence method against targeted data poisoning, which is one of the types of Sybil attacks, called Linear Algebra-based Detection (FedLAD). Unlike existing approaches, such as clustering and robust training, which struggle in situations where malicious nodes dominate, FedLAD models the federated learning aggregation process as a linear problem, transforming it into a linear algebra optimisation challenge. This method identifies potential attacks by extracting the independent linear combinations from the original linear combinations, effectively filtering out redundant and malicious elements. Extensive experimental evaluations demonstrate the effectiveness of FedLAD compared to five well-established defence methods: Sherpa, CONTRA, Median, Trimmed Mean, and Krum. Using tasks from both image classification and natural language processing, our experiments confirm that FedLAD is robust and not dependent on specific application settings. The results indicate that FedLAD effectively protects federated learning systems across a broad spectrum of malicious node ratios. Compared to baseline defence methods, FedLAD maintains a low attack success rate for malicious nodes when their ratio ranges from 0.2 to 0.8. Additionally, it preserves high model accuracy when the malicious node ratio is between 0.2 and 0.5. These findings underscore FedLAD's potential to enhance both the reliability and performance of federated learning systems in the face of data poisoning attacks.

Chamara Madarasingha, Nasrin Sohrabi, Zahir Tari

Time-series prediction or forecasting is critical across many real-world dynamic systems, and recent studies have proposed using Large Language Models (LLMs) for this task due to their strong generalization capabilities and ability to perform well without extensive pre-training. However, their effectiveness in handling complex, noisy, and multivariate time-series data remains underexplored. To address this, we propose LLMPred which enhances LLM-based time-series prediction by converting time-series sequences into text and feeding them to LLMs for zero shot prediction along with two main data pre-processing techniques. First, we apply time-series sequence decomposition to facilitate accurate prediction on complex and noisy univariate sequences. Second, we extend this univariate prediction capability to multivariate data using a lightweight prompt-processing strategy. Extensive experiments with smaller LLMs such as Llama 2 7B, Llama 3.2 3B, GPT-4o-mini, and DeepSeek 7B demonstrate that LLMPred achieves competitive or superior performance compared to state-of-the-art baselines. Additionally, a thorough ablation study highlights the importance of the key components proposed in LLMPred.