Cryptocurrencies have gained popularity due to their transparency, security, and accessibility compared to traditional financial systems, with Bitcoin, introduced in 2009, leading the market. Bitcoin's security relies on blockchain technology - a decentralized ledger consisting of a consensus and an incentive mechanism. The consensus mechanism, Proof of Work (PoW), requires miners to solve difficult cryptographic puzzles to add new blocks, while the incentive mechanism rewards them with newly minted bitcoins. However, as Bitcoin's acceptance grows, it faces increasing threats from attacks targeting these mechanisms, such as selfish mining, double-spending, and block withholding. These attacks compromise security, efficiency, and reward distribution. Recent research shows that these attacks can be combined with each other or with either malicious strategies, such as network-layer attacks, or non-malicious strategies, like honest mining. These combinations lead to more sophisticated attacks, increasing the attacker's success rates and profitability. Therefore, understanding and evaluating these attacks is essential for developing effective countermeasures and ensuring long-term security. This paper begins by examining individual attacks executed in isolation and their profitability. It then explores how combining these attacks with each other or with other malicious and non-malicious strategies can enhance their overall effectiveness and profitability. The analysis further explores how the deployment of attacks such as selfish mining and block withholding by multiple competing mining pools against each other impacts their economic returns. Lastly, a set of design guidelines is provided, outlining areas future work should focus on to prevent or mitigate the identified threats.

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model focused on DFL communications. In response to these identified risks, this work introduces a security module to counter communication-based attacks for DFL platforms. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform, Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario with physical and virtual deployments have been executed, encompassing three security configurations: (i) a baseline without security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results showed an average F1 score of 95%, with the most secure configuration resulting in CPU usage peaking at 68% (+-9%) in virtual deployments and network traffic reaching 480.8 MB (+-18 MB), effectively mitigating risks associated with eavesdropping or eclipse attacks.

Blockchain security is becoming increasingly relevant in today's cyberspace as it extends its influence in many industries. This paper focuses on protecting the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate and share information. The P2P network layer may be vulnerable to several families of attacks, such as Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. This layer is prone to threats inherited from traditional P2P networks, and it must be analyzed and understood by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain P2P network at an early stage. Tikuna employs an unsupervised Long Short Term Memory (LSTM) method based on Recurrent Neural Network (RNNs) to detect attacks and alert users. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy. Our research findings demonstrate that Tikuna is a valuable security tool for assisting operators to efficiently monitor and safeguard the status of Ethereum validators and the wider P2P network. Keywords: Ethereum blockchain, security, P2P network, deep learning, anomaly detection, vulnerabilities, eclipse attacks.