Cyber-attacks are getting more sophisticated and nuanced. Intrusion Detection Systems (IDSs) are commonly used in a variety of networks to assist in the timely detection of intrusions. In recent years, blockchain technology has got a lot of attention as a way to share data without the need for a trusted third party. In particular, data recorded in a single block cannot be modified without impacting all subsequent blocks. For an effective update, an attacker will need to monitor the majority of network nodes, which is not feasible given the current network size. This work aims to create a deep learning-based IDS model with the potential of integrating blockchain technology with intrusion detection, inspired by the ability to apply blockchain in all fields. The proposed model outperforms the conventional systems with respect to accuracy in detecting the security attacks.

网络攻击变得越来越复杂和微妙。入侵检测系统 (IDS) 通常用于各种网络中，以帮助及时检测入侵。近年来，区块链技术作为一种无需可信第三方即可共享数据的方式受到了广泛关注。特别是，记录在单个块中的数据不能在不影响所有后续块的情况下进行修改。为了有效更新，攻击者需要监控大多数网络节点，鉴于当前网络规模，这是不可行的。这项工作旨在创建一个基于深度学习的 IDS 模型，该模型具有将区块链技术与入侵检测相结合的潜力，其灵感来自于在所有领域应用区块链的能力。