Clustered federated learning (CFL) is an advanced technique in the field of federated learning (FL) that addresses the issue of catastrophic forgetting caused by non-independent and identically distributed (non-IID) datasets. CFL achieves this by clustering clients based on the similarity of their datasets and training a global model for each cluster. Despite the effectiveness of CFL in mitigating performance degradation resulting from non-IID datasets, the potential risk of privacy leakages in CFL has not been thoroughly studied. Previous work evaluated the risk of privacy leakages in FL using the property inference attack (PIA), which extracts information about unintended properties (i.e., attributes that differ from the target attribute of the global model’s main task). In this paper, we explore the potential risk of unintended property leakage in CFL by subjecting it to both passive and active PIAs. Our empirical analysis shows that the passive PIA performance on CFL is substantially better than that on FL in terms of the attack AUC score. Moreover, we propose an enhanced active PIA method tailored for CFL to improve the attack performance. Our method introduces a scale-up parameter that amplifies the impact of malicious local updates, resulting in better performance than the previous technique. Furthermore, we demonstrate that the vulnerability of CFL can be alleviated by applying differential privacy (DP) mechanisms at the client-level. Unlike previous works, which have shown that applying DP to FL can induce a high utility loss, our empirical results indicate that DP can be used as a defense mechanism in CFL, leading to a better trade-off between privacy and utility.

The network-based Intrusion detection system (NIDS) plays a key role in Internet of Things (IoT) as most IoT services are network-driven. However, the existing NIDSes for IoT systems are either too costly to scale or vulnerable against advanced attacks such as traffic mimicry. In this paper, we propose a novel IDS named Hawkware, a lightweight ANN-based distributed NIDS that runs on an IoT device and analyzes the device's runtime behavior in tandem with its network traffic. By analyzing device behavior, Hawkware is able to replace expensive, deep data analysis that has traditionally been used to detect advanced attacks. Our evaluations show that Hawkware is lightweight enough to be distributed and deployed on a Raspberry PI, and yet capable of detecting such attacks at a satisfactory level.

The OS kernel is typically the assumed trusted computing base in a system. Consequently, when they try to protect the kernel, developers often build their solutions in a separate secure execution environment externally located and protected by special hardware. Due to limited visibility into the host system, the external solutions basically all entail the semantic gap problem which can be easily exploited by an adversary to circumvent them. Thus, for complete kernel protection against such adversarial exploits, previous solutions resorted to aggressive techniques that usually come with various adverse side effects, such as high performance overhead, kernel code modifications and/or excessively complicated hardware designs. In this paper, we introduce RiskiM, our new hardware-based monitoring platform to ensure kernel integrity from outside the host system. To overcome the semantic gap problem, we have devised a hardware interface architecture, called PEMI, by which RiskiM is supplied with all internal states of the host system essential for fulfilling its monitoring task to protect the kernel even in the presence of attacks exploiting the semantic gap between the host and RiskiM. To empirically validate the security strength and performance of our monitoring platform in existing systems, we have fully implemented RiskiM in a RISC-V system. Our experiments show that RiskiM succeeds in the host kernel protection by detecting even the advanced attacks which could circumvent previous solutions, yet suffering from virtually no aforementioned side effects.