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Cryptocurrency software implements the cryptocurrency operations, including the distributed consensus protocol and the peer-to-peer networking. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight due to the dynamic nature of the peer connections and the large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. In addition, the Version++ verification overhead is small compared to the overall handshaking process; our measured overhead of 2.22% with minimal networking latency between the virtual machines provides an upper bound in the real-world networking with greater handshaking duration, i.e., the relative Version++ overhead in the real world with physically separate machines will be smaller. 

Cryptocurrency software implements the cryptocurrency operations. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. More specifically, we focus on Bitcoin for implementation and integration and advance its Version-message based hand-shaking and thus call our scheme Version++, The Version++ protocol provides software assurance, which is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Utilizing Merkle Tree for the verification efficiency, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. This paper for the conference demonstration supplements our technical paper at CCNC 2023 for synergy but highlights the prototyping and demonstration components of our research. 

De novo lipogenesis is a highly regulated metabolic process, which is known to be activated through transcriptional regulation of lipogenic genes, including fatty acid synthase (FASN). Unexpectedly, we find that the expression of FASN protein remains unchanged during Drosophila larval development from the second to the third instar larval stages (L2 to L3) when lipogenesis is hyperactive. Instead, acetylation of FASN is significantly upregulated in fast-growing larvae. We further show that lysine K813 residue is highly acetylated in developing larvae, and its acetylation is required for elevated FASN activity, body fat accumulation, and normal development. Intriguingly, K813 is autoacetylated by acetyl-CoA (AcCoA) in a dosage-dependent manner independent of acetyltransferases. Mechanistically, the autoacetylation of K813 is mediated by a novel P-loop-like motif (N-xx-G-x-A). Lastly, we find that K813 is deacetylated by Sirt1, which brings FASN activity to baseline level. In summary, this work uncovers a previously unappreciated role of FASN acetylation in developmental lipogenesis and a novel mechanism for protein autoacetylation, through which Drosophila larvae control metabolic homeostasis by linking AcCoA, lysine acetylation, and de novo lipogenesis. 

Blockchain relies on the underlying peer-to-peer (P2P) networking to broadcast and get up-to-date on the blocks and transactions. Because of the blockchain operations’ reliance on the information provided by P2P networking, it is imperative to have high P2P connectivity for the quality of the blockchain system operations and performances. High P2P networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless Bitcoin cryptocurrency network, using the peer identifiers – including the current approach of counting the number of distinct IP addresses and port numbers – can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating identities. We build a robust estimation engine for the P2P networking connectivity by sensing and processing the P2P networking traffic. We take a systematic approach to study our engine and analyze the followings: the different components of the connectivity estimation engine and how they affect the accuracy performances, the role and the effectiveness of an outlier detection to enhance the connectivity estimation, and the engine’s interplay with the Bitcoin protocol. We implement a working Bitcoin prototype connected to the Bitcoin mainnet to validate and improve our engine’s performances and evaluate the estimation accuracy and cost efficiency of our connectivity estimation engine. Our results show that our scheme effectively counters the identity-manipulations threats, achieves 96.4% estimation accuracy with a tolerance of one peer connection, and is lightweight in the overheads in the mining rate, thus making it appropriate for the miner deployment. 

The distributed cryptocurrency networking is critical because the information delivered through it drives the mining consensus protocol and the rest of the operations. However, the cryptocurrency peer-to-peer (P2P) network remains vulnerable, and the existing security approaches are either ineffective or inefficient because of the permissionless requirement and the broadcasting overhead. We design a Lightweight and Identifier-Oblivious eNgine (LION) for the anomaly detection of the cryptocurrency networking. LION is not only effective in permissionless networking but is also lightweight and practical for the computation-intensive miners. We build LION for anomaly detection and use traffic analyses so that it minimally affects the mining rate and is substantially superior in its computational efficiency than the previous approaches based on machine learning. We implement a LION prototype on an active Bitcoin node to show that LION yields less than 1% of mining rate reduction subject to our prototype, in contrast to the state-of-the-art machine-learning approaches costing 12% or more depending on the algorithms subject to our prototype, while having detection accuracy of greater than 97% F1-score against the attack prototypes and real-world anomalies. LION therefore can be deployed on the existing miners without the need to introduce new entities in the cryptocurrency ecosystem. 

Blockchain relies on the underlying peer-to-peer (p2p) networking to broadcast and get up-to-date on the blocks and transactions. It is therefore imperative to have high p2p connectivity for the quality of the blockchain system operations. High p2p networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless blockchain network, using the peer identifiers—including the current approach of counting the number of distinct IP addresses and port numbers—can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating the identifiers. We build a robust estimation engine for the p2p networking connectivity by sensing and processing the p2p networking traffic. We implement a working Bitcoin prototype connected to the Bitcoin Mainnet to validate and improve our engine’s performances and evaluate the estimation accuracy and cost efficiency of our estimation engine. 

While the blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results support the effectiveness of the presented security mechanism for accurate, online detection of malicious events from blockchain networking traffic data. We also show further reduction in time complexity (up to 66.8% for training and 85.7% for testing), without any performance degradation using feature prioritization compared to the utilization of the entire features.