The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber-physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this paper, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against softwarized industrial CPSs. Specifically, we first design a new deep learning based intrusion detection model for industrial CPSs, by making use of a convolutional neur

Android malware and its variants are a major challenge for mobile platforms. However, there are two main problems in the existing detection methods: a ) The detection method lacks the evolution ability for Android malware, which leads to the low detection rate of the detection model for malware and its variants. b ) Traditional detection methods require centralized data for model training, however, the aggregation of training samples is limited due to the infectivity of malware and growing data privacy concerns, centralized detection methods are difficult to be applied in actual detection scenarios. In this paper, we propose FEDriod, a comprehensive Android malware detection method based on federated learning architecture that protects against growing Android malware or emerging Android malware variants. Specifically, we employ genetic evolution strategy to simulate the evolution of Android malware and develop potential malware variants from typical Android malware. Then, we customize the Android malware detection model based on residual neural network to achieve high detection accuracy. Finally, to achieve the protection sensitive data, we develope a federated learning framework to allows multiple Android malware detection agencies to jointly build a comprehensive Android malware detection model. We comprehensively evaluate the performance of FEDriod on the CIC, Drebin, and Contagio authoritative datasets. Experimental results show that our local model outperforms all baseline classifiers. In the federal scenario, our proposed method is superior to the state-of-the-art detection methods, especially in the cross-dataset evaluation, the F1 of FEDriod is 98.53%. More important, we performed genetic evolution experiments on the Drebin dataset, and the results showed that our proposed method has the ability to detect Android malware variants.

Biometric image hashing techniques have been widely studied and seen progressive advancements. However, only a handful of available solutions provide two-factor cancelability while simultaneously satisfying the tradeoff among all criteria of template protection mechanisms. In this paper, we propose a novel scheme for generating a secure and robust hash from a fingerprint image using Fourier-Mellin transform and fractal coding. First, due to its invariance property, Fourier-Mellin transform is incorporated into the domain fingerprint minutiae blocks to provide feature alignment, therein generating a fixed-length minutiae representation for comparison. Then, dimensionality reduction and texture compression are exploited using fractal coding to generate a robust and compact hash for improved security and recognition. The experimental results demonstrate a favorable recognition performance on benchmarked state-of-the-art schemes from FVC2002 and FVC2004 fingerprint databases. The analyses prove our method’s robustness and resiliency to security and privacy attacks. Our method also satisfies the revocability and unlinkability criteria of cancelable biometrics.

Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative learning tasks, such as steering angle prediction, trajectory prediction, drivable road detection, etc., among integrated vehicles. However, since VANETs are characterized by ad hoc cooperating vehicles with non-independent and identically distributed (Non-IID) data, directly employing existing FL frameworks to VANETs may cause extensive communication overhead and compromised model performance. Further, most of the existing deep learning models incorporated in FL frameworks rely heavily on data with manual annotations, leading to a huge labor cost. To address these issues, in this paper we propose an efficient and effective Federated End-to-End Learning framework for cooperative learning tasks in VANETs, named FEEL. Specifically, we first formulate a distributed optimization problem for cooperative deep learning tasks with Non-IID data in multi-hop cluster VANETs. Second, two algorithms for inter-cluster learning and inner-cluster learning are respectively designed, to reduce the communication overhead and fit Non-IID data. Third, a Paillier-based communication protocol is crafted, allowing secure model parameter updates at the central server without knowing the real updates at each cooperating base station. Extensive experiments on two real-world datasets are conducted by considering various data distributions and VANET topologies, demonstrating the high efficiency and effectiveness of the proposed FEEL framework in both regression and classification tasks.

The integration of phasor measurement units (PMUs) and phasor data concentrators (PDCs) in smart grids may be exploited by attackers to initiate new and sophisticated false data injection (FDI) attacks. Existing FDI attack mitigation approaches are generally less effective against sophisticated FDI attacks, such as collusive false data injection (CFDI) attacks launched by compromised PDCs (and PMUs) as we demonstrate in this paper. Thus, we propose a secure and resilience-enhanced scheme (SeCDM) to detect and mitigate such cyber threats in smart grids. Specifically, we design a decentralized homomorphic computation paradigm along with a hierarchical knowledge sharing algorithm to facilitate secure ciphertext calculation of state estimation residuals. Following this, a centralized FDI detector is implemented to detect FDI attacks. Findings from the security analysis demonstrate our approach achieves enhanced conventional FDI and CFDI attack resilience, and findings from our performance evaluations on the standard IEEE 14-, 24-, and 39-bus power systems also show that the communication overheads and computational complexity are reasonably “low”.