Evaluating Federated Learning-Based Intrusion Detection Scheme for Next Generation Networks

The proliferation of billions of heterogeneous Internet of Things (IoT) devices at a rapid pace has resulted in a marked expansion of attack surfaces. Numerous new attacks are constantly emerging to undermine the network's availability, data confidentiality, and systems' integrity due to inadequate security measures and resource limitations. Intrusion detection systems (IDSs) are used as the first line of defense to identify early instances of cyber-attacks targeting critical points. However, Next-Generation Networks (NGNs) with dense connectivity pose a challenge for traditional IDS approaches, as they raise concerns about users' data privacy. Federated learning-based IDSs (Fed-IDSs) are an emerging and promising solution, as they permit the training of machine learning models on decentralized data stored on devices without compromising privacy. However, Fed-IDSs also have some unique issues. We identified that the existing Fed-IDSs have poor performance since the datasets used for evaluation, or the data in the real world, are highly imbalanced, and classes are not uniformly distributed. Motivated by this, we developed a novel IDS to effectively address the problem of class imbalance in federated learning at both the local and global levels. Following this, we evaluated the performance of our Fed-IDS under both independent and identically distributed (IID) and non-IID data settings and observed its generalizability to detect various attacks improved greatly. Extensive experiments are conducted to illustrate the effectiveness and benefits of this proposal.