Vertex re-identification attack using neighbourhood-pair properties

There has been a growing interest in sharing and mining social network data for a wide variety of applications. In this paper, we address the problem of privacy disclosure risks that arise from publishing social network data. Specifically, we look at the vertex re-identification attack that aims to link specific vertex in social network data to specific individual in the real world. We show that even when identifiable attributes such as names are removed from released social network data, re-identification attack is still possible by manipulating abstract information. We present a new type of vertex re-identification attack model called neighbourhood-pair attack. This attack utilizes the information about the local communities of two connected vertices to identify the target individual. We show both theoretically and empirically that the proposed attack provides higher re-identification rate compared with the existing re-identification attacks that also manipulate network structure properties. The experiments conducted also show that the proposed attack is still possible even on anonymised social network data.

Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms

Big Data technologies are exciting cutting-edge technologies that generate, collect, store and analyse tremendous amount of data. Like any other IT revolution, Big Data technologies also have big challenges that are obstructing it to be adopted by wider community or perhaps impeding to extract value from Big Data with pace and accuracy it is promising. In this paper we first offer an alternative view of «Big Data Cloud» with the main aim to make this complex technology easy to understand for new researchers and identify gaps efficiently. In our lab experiment, we have successfully implemented cyber-attacks on Apache Hadoop's management interface «Ambari». On our thought about «attackers only need one way in», we have attacked the Apache Hadoop's management interface, successfully turned down all communication between Ambari and Hadoop's ecosystem and collected performance data from Ambari Virtual Machine (VM) and Big Data Cloud hypervisor. We have also detected these cyber-attacks with 94.0187% accurateness using modern machine learning algorithms. From the existing researchs, no one has ever attempted similar experimentation in detection of cyber-attacks on Hadoop using performance data.

Detection of anomalous crowd behaviour using hyperspherical clustering

Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect anomalous events and objects, two types of feature coding has been proposed: spatial features and spatio-temporal features. Spatial features comprises of contrast, correlation, energy and homogeneity, which are derived from Gray Level Co-occurrence Matrix (GLCM). Spatio-temporal feature includes the time spent by an object at different locations in the scene. Hyperspherical clustering has been employed to detect the anomalies. Spatial features revealed the anomalous frames by using contrast and homogeneity measures. Loitering behaviour of the people were detected as anomalous objects using the spatio-temporal coding.

Taming transitive permission attack via bytecode rewriting on Android application

Google Android is popular for mobile devices in recent years. The openness and popularity of Android make it a primary target for malware. Even though Android's security mechanisms could defend most malware, its permission model is vulnerable to transitive permission attack, a type of privilege escalation attacks. Many approaches have been proposed to detect this attack by modifying the Android OS. However, the Android's fragmentation problem and requiring rooting Android device hinder those approaches large-scale adoption. In this paper, we present an instrumentation framework, called SEAPP, for Android applications (or “apps”) to detect the transitive permission attack on unmodified Android. SEAPP automatically rewrites an app without requiring its source codes and produces a security-harden app. At runtime, call-chains are built among these apps and detection process is executed before a privileged API is invoked. Our experimental results show that SEAPP could work on a large number of benign apps from the official Android market and malicious apps, with a repackaged success rate of over 99.8%. We also show that our framework effectively tracks call-chains among apps and detects known transitive permission attack with low overhead. Copyright © 2016 John Wiley & Sons, Ltd.

GIS-based dynamic modelling and analysis of flash floods considering land-use planning

Flood inundation is a common natural disaster and a growing development challenge for many cities and thousands of small towns around the world. Soil features have frequently altered with the rapid development of urbanised regions, which has led to more frequent and longer duration of flooding in urban flood-prone regions. Thus, this paper presents a geographic information system (GIS)-based methodology for measuring and visualising the effects on urban flash floods generated by land-use changes over time. The measurement is formulated with a time series in order to perform a dynamic analysis. A catchment mesh is introduced into a hydrological model for reflecting the spatial layouts of infrastructure and structures over different construction periods. The Geelong Waurn Ponds campus of Deakin University is then selected as a case study. Based on GIS simulation and mapping technologies, this research illustrates the evolutionary process of flash floods. The paper then describes flood inundation for different built environments and presents a comparison by quantifying the flooding extents for infrastructure and structures. The results reveal that the GIS-based estimation model can examine urban flash floods in different development phases and identify the change of flooding extents in terms of land-use planning. This study will bring benefits to urban planners in raising awareness of flood impact and the approach proposed here could be used for flood mitigation through future urban planning.

Measuring eco-roof mitigation on flash floods via GIS simulation

Purpose: The rapid and ongoing expansion of urbanised impervious areas could lead to more frequent flood inundation in urban flood-prone regions. Nowadays, urban flood inundation induced by rainstorm is an expensive natural disaster in many countries. In order to reduce the flooding risk, eco-roof systems (or green roof systems) could be considered as an effective mechanism of mitigating flooding disasters through their rainwater retention capability. However, there is still a lack of examining the stormwater management tool. The purpose of this paper is to evaluate the effects on flooding disaster from extensive green roofs. Design/methodology/approach: Based on geographical information system (GIS) simulation, this research presents a frame of assessing eco-roof impacts on urban flash floods. The approach addresses both urban rainfall-runoff and underground hydrologic models for traditional impervious and green roofs. Deakin University’s Geelong Waurn Ponds campus is chosen as a study case. GIS technologies are then utilised to visualise and analyse the effects on flood inundation from surface properties of building roofs. Findings: The results reveal that the eco-roof systems generate varying degrees of mitigation of urban flood inundation with different return period storms. Originality/value: Although the eco-roof technology is considered as an effective stormwater management tool, it is not commonly adopted and examined in urban floods. This study will bring benefits to urban planners for raising awareness of hazard impacts and to construction technicians for considering disaster mitigation via roof technologies. The approach proposed here could be used for the disaster mitigation in future urban planning.

Modified moment-based image watermarking method robust to cropping attack

Developing a watermarking method that is robust to cropping attack is a challenging task in image watermarking. The moment-based watermarking schemes show good robustness to common signal processing attacks and some geometric attacks but are sensitive to cropping attack. In this paper, we modify the moment-based approach to deal with cropping attack. Firstly, we find the probability density function (PDF) of the pixel value distribution from the original image. Secondly, we reshape and normalize the pdf of the pixel value distribution (PPVD) to form a two dimensional image. Then, the moment invariants are calculated from the PPVD image. Since PPVD is insensitive to cropping, the proposed method is robust to cropping attack. Besides, it also has high robustness against other common attacks. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.