Modeling the efficiency of a port community system as an agent-based process

We present an agent-based method which makes use of reinforcement learning in order to estimate the efficiency of a Port Community System. We have evaluated the method using two weeks of observations of import containers at the Port of Brisbane as a case study. Three scenarios are examined. The first scenario evaluates the observed container delivery by individual shipping lines and estimates the consignments allocated to the various road carriers based on optimizing the individual shipper's total logistics cost. The second scenario implies that, in the optimum case, all agents (shipping lines and road carriers) communicate and cooperate through a single portal. The objective of cooperation is in sharing vehicles and creating tours to deliver shipments to several importers in order to reduce total logistics costs, while physical and time window constraints are also considered. The third scenario allows for some agents to occasionally decide to act based on individual costs instead of total combined logistics costs. The results of this study indicate an increase in the efficiency of the whole logistics process through cooperation, and the study provides a prototype of a Port Community System to support logistics decisions

Exploring the adaptive capacity of emergency management using agent based modelling

This project aimed to explore the suitability of Agent Based Modelling and Simulation (ABMS) technology in assisting planners and policy makers to better understand complex situations with multiple interacting aspects. The technology supports exploration of the impact of different factors on potential outcomes of a scenario, thus building understanding to inform decision making. To concretise this exploration a specific simulation tool was developed to explore response capacity around flash flooding in an inner Melbourne suburb, with a focus on sandbag depots as an option to be considered.The three types of activities delivered by this project to achieve its objectives were the development of an agent-based simulation, data collection to inform the development of the simulation and communication and engagement activities to progress the work. Climate change is an area full of uncertainties, and yet sectors such as Emergency Management and many others need to develop plans and policy responses regarding adaptation to these uncertain futures. Agent Based Modelling and Simulation is a technology which supports modelling of a complex situation from the bottom up, by modelling the behaviours of individual agents (often representing humans) in various scenarios. By running simulations with different configurations it is possible to explore and analyse a very broad range of potential options, providing a detailed understanding of potential risks and outcomes, given particular alternatives. This project explored the suitability of this technology for use in assessing and developing the capacity of the emergency response sector, as it adapts to climate change. A simulation system was developed to explore a particular issue regarding protection of property in a suburb prone to flash flooding. In particular the option of providing sandbag depots was explored. Simulations indicated that sandbag depots provided by CoPP or VicSES were at this time not a viable option. The simulation tool was deemed to be very useful for demonstrating this to community members as well as to decision makers. An interactive game was also developed to assist in raising awareness of community members about how to sandbag their property using on-site sandbags. The technology was deemed to be of great potential benefit to the sector and areas for further work inorder to realise this benefit were identified. In addition to developing awareness of useful technology, this project also demonstrated the critical importance of interdisciplinary team work, and close engagement with stakeholders and end users, if valuable technology uptake is to be realised. &nbsp

Environments to support collaborative software engineering

With increasing globalisation of software production, widespread use of software components, and the need to maintain software systems over long periods of time, there has been a recognition that better support for collaborative working is needed by software engineers. In this paper, two approaches to developing improved system support for collaborative software engineering are described: GENESIS and OPHELIA. As both projects are moving towards industrial trials and eventual publicreleases of their systems, this exercise of comparing and contrasting our approaches has provided the basis for future collaboration between our projects particularly in carrying out comparative studies of our approaches in practical use

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

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