Governing in the Anthropocene: are there cyber-systemic antidotes to the malaise of modern governance?

The Anthropocene imposes new challenges for governments, demanding capabilities for dealing with complexity and uncertainty. In this paper we examine how effective governing of social-biophysical dynamics is constrained by current processes and systems of government. Framing choices and structural determinants combine to create governance deficits in multiple domains, particularly in relation to the governing of complex larger-scale social – biophysical systems. Attempts to build capability for governing ‘wicked problems’ are relevant to sustainability science and Anthropocene governance, but these have mostly failed to become institutionalised. Two cases studies are reported to elucidate how the systemic dynamics of governing operate and fail in relation to espoused purpose. In the UK attempts to enact ‘joined-up’ government’ during the years of New Labour government reveal systemic flaws and consistent praxis failures. From Australia we report on water governance reforms with implications for a wide range of complex policy issues. We conclude that innovations are needed to build capacity for governing the unfolding surprises and inherent uncertainties of the Anthropocene. These include institutionalising, or structural incorporation, of cyber-systemic thinking/practices that can also enhance empowerment and creativity that underpins sustainability science

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Do cyber-birds flock together? Comparing deviance among social network members of cyber-dependent offenders and traditional offenders

The distinct setting in which cyber-dependent crime takes place may reduce the similarity in the deviance of social network members. We test this assumption by analysing the deviance of the most important social contacts of cyber-dependent offenders and traditional offenders in the Netherlands (N = 344 offenders; N = 1131 social contacts). As expected, similarity in deviance is weaker for cyber-dependent crime. Because this is a strong predictor of traditional offending, this has important implications for criminological research and practice. Additionally, for both crime types the offending behaviour of a person is more strongly linked to the deviance of social ties if those ties are of the same gender and age, and if the offender has daily contact with them. Implications and future criminological research suggestions are discussed