With the unprecedented demand for cloud computing, ensuring trust in the underlying environment is challenging. Applications executing in the cloud are prone to attacks of different types including malware, network and data manipulation. These attacks may remain undetected for a significant length of time thus causing a lack of trust. Untrusted cloud services can also lead to business losses in many cases and therefore need urgent attention. In this paper, we presentTrusted Public Cloud(TPC), a generic framework ensuring theZero-trustsecurity of client machine. It tracks the system state, alerting the user of unexpected changes in the machine’s state, thus increasing the run-time detection of security vulnerabilities. We validatedTPCon Microsoft Azure with Local, Software Trusted Platform Module (SWTPM) and Software Guard Extension (SGX)-enabled SWTPM security providers. We also evaluated the scalability ofTPCon Amazon Web Services (AWS) with a varying number of client machines executing in a concurrent environment. The execution results show the effectiveness ofTPCas it takes a maximum of 35.6 seconds to recognise the system state when there are 128 client machines attached.

This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability.

The authors describe a study of the social dynamics of new media in Scottish households. The evolving project drew on dialogues with multiple household members elicited in group conversations. This approach to interviews captured different and conflicting points of view, a feature shared with certain social approaches to systems design. Analysis of the interview transcripts revealed that there are recurrent narratives and behavioral genres across households (and across sample groups), and that these reflect tactics, stratagems, and plans by means of which respondents navigate social space. The authors' approach contrasts with prevailing “needs and uses” models in information science, in offering a methodological framework based on group narrative and genre analysis that contributes to a theory of social informatics in the household.