Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross-blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this article introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of cross-blockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving cross-chain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.

In the realm of enterprise blockchain systems, robust access control is crucial for enabling secure exchange of data and assets across networks. In this paper, we introduce InterAcct, designed to address the intricacies of access control for cross-chain interactions between permissioned networks. InterAcct enforces access control at the source within the originating network as well as for ledger data and assets in the destination network after validating cross-consortium requests. We implement InterAcct using Hyperledger Cacti as the data interoperability layer across Hyperledger Fabric ledgers. We establish that InterAcct handles cross-chain access control for request-responses with less than 2 second latency overhead while adapting to increasing workloads.

Federated Learning (FL) is a collaborative machine learning approach that enables data owning nodes to retain their data locally, preventing its transfer to a central server. It involves sharing only the local model parameters with the server to update a global model, which is then disseminated back to the local nodes. Despite its iterative convergence, FL has several limitations, such as the risk of single-point failure, inadequate incentives for participating nodes, and potential privacy breaches. While Local Differential Privacy (LDP) is often used to mitigate privacy concerns, the other challenges of FL have not yet been addressed comprehensively, even for Locally Differentially Private Federated Learning (LDP-FL). We propose an integrated approach that uses permissioned blockchains to guard against a single point of failure and a token-based incentivization (TBI) mechanism for encouraging participation in LDP-FL. In our scheme, participating nodes receive tokens upon sharing their model parameters, which can subsequently be used to access updated global models. The number of tokens awarded for parameter sharing is determined by ϵ - the privacy factor of LDP, ensuring that the nodes do not overly obfuscate the data they share. We demonstrate the feasibility of our approach by developing the Blockchain-based TBI-LDP-FL framework (hereinafter, referred to as BTLF) on HyperLedger Fabric. Extensive results of experimentation establish the efficacy of BTLF.

The concept of a decentralized web has been realized with the idea of Web 3.0 through interconnecting over multiple blockchain-based networks. However, blockchain incurs significant time and space overhead. This problem can be solved using snapshots that store the blockchain's states compactly. But, the existing snapshot mechanism used in Hyperledger Fabric is limited to siloed operations on a single blockchain only. In this paper, we contribute towards overcoming this drawback by developing a novel mechanism for peer selection, snapshot archival, and cross-blockchain sharing of the snapshot. We extend the snapshot collection mechanism in Hyperledger Fabric to implement the above idea and test it over two blockchain networks emulating a decentralized web architecture.

A botnet (or, a network of bots) is an army of compromised machines that are often under the control and coordination of one or multiple sources or work in a peer-to-peer mode through a remote secure channel. Botnet infection modeling enables design for secure systems. This work proposes an innovative approach using the concept of graph theory-based closeness centrality seeded with deep learning to detect peer-to-peer botnet infection across nodes. Compromised networks can be quite big as botnets and malware infections utilize the ubiquitous internet to spread rapidly. Using closeness centrality as one of the major inclination factors of interacting nodes, the deep neural model is applied. The performance of the proposed method is evaluated by identifying botnet traffic with a high true positive rate (bot-infected node detection rate) and a low false-positive rate.