Biblio

Cloud computing has been envisioned as a next generation information technology (IT) paradigm. The risk of losing data stored with any untrustworthy service provider is the key barrier to widespread uptake of cloud computing. This paper proposes an algebraic signature based remote data possession checking (RDPC) scheme to verify the integrity of the data stored in the cloud. This scheme integrates forward error-correcting codes to enhance the data possession guarantee, which can recover the data when a small amount of file has been deleted. The scheme allows verification without the need for the auditor to compare against the original data, which reduces the communication complexity dramatically. The storage complexity of cloud user is reduced to several bytes' information. Extensive security analysis and simulation show that the proposed scheme is highly provably secure. Finally, experiment results reveal that the computation performance is effective, and bounded by disk I/O.

Supervisory Control and Data Acquisition (SCADA)networks are widely deployed in modern industrial control systems (ICSs)such as energy-delivery systems. As an increasing number of field devices and computing nodes get interconnected, network-based cyber attacks have become major cyber threats to ICS network infrastructure. Field devices and computing nodes in ICSs are subjected to both conventional network attacks and specialized attacks purposely crafted for SCADA network protocols. In this paper, we propose a deep-learning-based network intrusion detection system for SCADA networks to protect ICSs from both conventional and SCADA specific network-based attacks. Instead of relying on hand-crafted features for individual network packets or flows, our proposed approach employs a convolutional neural network (CNN)to characterize salient temporal patterns of SCADA traffic and identify time windows where network attacks are present. In addition, we design a re-training scheme to handle previously unseen network attack instances, enabling SCADA system operators to extend our neural network models with site-specific network attack traces. Our results using realistic SCADA traffic data sets show that the proposed deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerged threats.