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Evolved similarity techniques in malware analysis

- Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun

  • Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 2019 18th IEEE International Conference On Trust, Security And Privacy; published in In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 5-8th Aug, 2019 p. 404-410
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  • Description: Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.

Vibration spectrum imaging : A novel bearing fault classification approach

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Electronics Vol. 62, no. 1 (2015), p. 494-502
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  • Description: Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.

Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples

- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur

  • Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
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  • Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.

Multi-size-window spectral augmentation: Neural network bearing fault classifier

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) p. 261-266
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  • Description: Features extraction has always been crucial in rotary machines for Condition based machine health monitoring. Time-domain-segmentation being among the preliminary steps for further classification process plays a momentous role. Vibration signals from bearing are quasistationary in nature therefore calculation of constituent frequencies amplitudes in the vibration signal is dependent upon time-segmentation-window size. The proposed research confers the effects of time-segmentation window size on spectral features amplitudes calculation and its impacts on classification accuracy of the Artificial Neural Network (ANN). Using multi-size time-segmentation-window, for comprehensive spectral features calculation, ANN pattern classifier has been trained for enhanced classification. ANN learning assigns importance based relative weights to the links using supervised learning. Experimental results have shown that multi-size-window spectral features for ANN fault classifier perform efficiently for quasi-stationary bearing vibrations.

A warning based preemptive routing scheme for QoS maintenance in wireless ad hoc networks

- Rokonuzzaman, S. K., Pose, Ronald, Gondal, Iqbal

  • Authors: Rokonuzzaman, S. K. , Pose, Ronald , Gondal, Iqbal
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: PE-WASUN '09 Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
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  • Description: Traditional QoS routing protocols find paths that meet the QoS requirements while discovering routes. The chosen route is used while the QoS is met. If the QoS is violated rerouting takes place. Violation of QoS may be unacceptable for delay sensitive traffic. We introduce a preemptive QoS re-routing scheme. The end-to-end delay of packets in each session is monitored and if it seems likely that QoS violation might occur, a preemptive QoS rerouting process is initiated. This helps maintain QoS in dynamic wireless ad hoc networks. Schemes for triggering QoS re-routing are investigated. We have reduced the number of late packets and improved the overall end-to-end delay of the communications.

CAM : Congestion avoidance and mitigation in wireless sensor networks

- Bhuiyan, Mohammad, Gondal, Iqbal, Kamruzzaman, Joarder

Dynamic resource allocation for improved QoS in WiMAX/WiFi integration

- Rabbani, Md, Kamruzzaman, Joarder, Gondal, Iqbal, Ahmad, Iftekhar, Hassan, Md Rafiul

  • Authors: Rabbani, Md , Kamruzzaman, Joarder , Gondal, Iqbal , Ahmad, Iftekhar , Hassan, Md Rafiul
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011 (Studies in Computational Intelligence series) Vol. 368, no. 2011 (2011), p. 141-156
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  • Description: Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by WiFi technology. WiFi enjoys a high penetration in the market.Most of the electronic gadgets such as laptop, notepad, mobile set, etc., boast the provision ofWiFi. Currently most WiFi hotspots are connected to the Internet via wired connections (e.g., Ethernet), and the deployment cost of wired connection is high. On the other hand, since WiMAX can provide a high coverage area and transmission bandwidth, it is very suitable for the backbone networks of WiFi. WiMAX can also provide the better QoS needed for many 4G applications. WiMAX devices, however, are not as common as WiFi devices and it is also expensive to deploy aWiMAX-only infrastructure. An integrated WiMAX/WiFi architecture (using WiMAX as backhaul connection for WiFi) can support 4G applications with QoS assurance and mobility, and provide high-speed broadband services in rural, regional and urban areas while reducing the backhaul cost. WiMAX and WiFi have different MAC mechanisms to handle QoS. WiMAX MAC architecture is connection-oriented providing the platform for strong QoS control. In contrast,WiFi MAC is not connection-oriented, hence can provide only best effort services. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. The paper depicts a converged architecture of WiMAX and WiFi, and then proposes an adaptive resource distribution model for the access points. The resource distribution model ultimately allocates more time slots to those connections that need more instantaneous resources to meet QoS requirements. A dynamic splitting technique is also presented that divides the total transmission period into downlink and uplink transmission by taking the minimum data rate requirements of the connections into account. This ultimately improves the utilization of the available resources, and the QoS of the connections. Simulation results show that the proposed schemes significantly outperform the other existing resource sharing schemes, in terms of maintaining QoS of different traffic classes in an integratedWiMAX/WiFi architecture

An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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  • Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

A patient agent to manage blockchains for remote patient monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
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  • Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
  • Description: Studies in Health Technology and Informatics

A framework for a QoS based adaptive topology control system for wireless ad hoc networks with multibeam smart antennas

- Rokonuzzaman, S. K., Pose, Ronald, Gondal, Iqbal

  • Authors: Rokonuzzaman, S. K. , Pose, Ronald , Gondal, Iqbal
  • Date: 2008
  • Type: Text , Conference proceedings
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  • Description: Wireless ad hoc networks are self-configurable distributed systems. One of the major problems in traditional wireless ad hoc networks is interference. The interference could be reduced using smart directional antennas. In this study, multibeam smart antennas have been used. When using this type of antenna, two nodes can communicate when both the sending and receiving beams are pointing towards each other. Also, a node can only communicate with a subset of nodes in its neighborhood depending on the number of beams and their beamwidth. Thus, the network topology needs to be dynamic in this case, and by controlling the topology network, performance can be increased. In this paper, we present a framework of a cross layer approach of topology control that interacts with the routing layer and MAC layer and meets the required QoS of different data streams. The approach is fully distributed. When the network is initialized, the algorithm builds an initial connected topology and the routing algorithm uses this topology to find paths for the current communications. Then, depending on the network scenario, current communications and the required QoS, the topology control layer changes the topology to optimize the network performance. This study concerns suburban ad hoc networks (SAHN) where nodes tend to be fixed and are aware of their locations.

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

- Ulhaq, Anwaar, Yin, Xiaoxia, Zhang, Yunchan, Gondal, Iqbal

  • Authors: Ulhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016; Lecce, Italy; 24th-27th October 2016; published in Advanced Conepts for Intelligent Vision Systems (Lecture Notes in Computer Science series) Vol. 10016 LNCS, p. 465-476
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  • Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Automated multi-sensor color video fusion for nighttime video surveillance

- Ul-Haq, Anwaar, Gondal, Iqbal, Murshed, Manzur

  • Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
  • Date: 2010
  • Type: Text , Conference proceedings
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  • Description: In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.

Optimally parameterized wavelet packet transform for machine residual life prediction

- Gondal, Iqbal, Yaqub, Muhammad, Kamruzzaman, Joarder

  • Authors: Gondal, Iqbal , Yaqub, Muhammad , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper , Journal article
  • Relation: Australian Acoustical SocietyConference 2011: Breaking New Ground, Acoustics 2011; Gold Coast, Australia; 2nd-4th November 2011; p.1-8
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  • Description: One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.

Mining associated sensor patterns for data stream of wireless sensor networks

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference proceedings
  • Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
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  • Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.

Mobile malware detection with imbalanced data using a novel synthetic oversampling strategy and deep learning

- Khoda, Mahbub, Kamruzzaman, Joarder, Gondal, Iqbal, Imam, Tasadduq, Rahman, Ashfaqur

LACAR : Location aided congestion aware routing in wireless sensor networks

- Bhuiyan, Mohammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Wireless Communications and Networking Conference (WCNC), p. 1-6
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  • Description: Trade-off between energy-efficiency and reliability in wireless sensor networks is application dependent. Without the reliability, the extended lifetime of a network is of limited use. Due to the inherent correlation between reliability and congestion, it is necessary to reduce congestion to improve reliability. Existing congestion control algorithms in wireless sensor networks are reactive. They attempt to reduce the congestion only after its detection. In this paper, we present Location Aided Congestion Aware Routing (LACAR) protocol that proactively avoids congestion formation and improves data delivery success rate in data gathering wireless sensor networks. Location, energy and congestion information of neighbours together with the location information of the base station determine appropriate routes. Simulation results show that LACAR achieves high packet success rate in an energy-efficient way.

Social-connectivity-aware vertical handover for heterogeneous wireless networks

- Haider, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal Of Network And Computer Applications Vol. 36, no. 4 (2013), p. 1131-1139
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  • Description: Vertical handover mechanism for a WLAN-cellular heterogeneous network could be made efficient with the use of context aware admission control strategy. Existing admission control methods aim to provide satisfactory quality of service, but rely solely on the availability of wireless resources in the target network. We propose that the admission control in WLAN should make use of social connectivity context of users in its coverage area to classify local and global traffic. In this paper, we introduce a novel Social-Connectivity-aware Vertical Handover (SCVH) scheme, which performs admission control using connectivity graph data from the online social networking services. A higher importance of visiting node for users resident in WLAN, advocates a higher priority for granting admission. We employ graph-theoretic concept of centrality to calculate the social importance of potential handing-over nodes. By giving handover precedence to higher-centrality nodes, we achieve an optimal allocation of wireless resources in addition to improved quality of service. The proposed handover strategy offers an additional advantage of reducing global social network traffic.

Autonomous behavior modeling approach for diverse anomaly detection application

- Amar, Muhammad, Wilson, Campbell, Gondal, Iqbal

  • Authors: Amar, Muhammad , Wilson, Campbell , Gondal, Iqbal
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: ICOSST 2014 - 2014 International Conference on Open Source Systems and Technologies, Lahore, Pakistan, 18-20th Dec 2014 p. 122-127
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  • Description: For absolute process safety in diverse machine applications, timely and reliable anomalous behavior detection is very crucial. Different machine applications have different normal behavior patterns and safety standards thus require adjustable and adaptive anomaly detection techniques. In this paper an autonomous behavior modeling approach for anomaly detection has been presented. In this approach time segmented vibration signals from the machines are transformed into spectral contents. After normalization, these frequency domain contents are divided into weighted frequency bins and then Gaussian models are achieved for these frequency bins over the entire training set. Using summation rule on the outputs of Gaussian models a single indicative measure of the machine health: normality score is obtained. The sensitivity of the normality score and anomaly detector towards potential anomalous signals can be controlled by using different number of bins and weights. Suitable parameters values, number of bins and weights profile, for anomaly detector model are selected autonomously using minimum value of the cost function. The increase of normality score of this model above a certain threshold is considered an alarm indicating anomalous behavior. Thus the proposed method enables us to achieve autonomously a suitable anomaly detection model with suitable parameters with controlled sensitivity during the test phase.

I-MAC: energy efficient intelligent MAC protocol for wireless sensor networks

- Bhuiyan, Mohammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 17th Asia Pacific Conference on Communications, APCC 2011; Sabah, Malaysia; 2nd-5th October 2011 p. 133-138
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  • Description: Energy efficiency is a vital aspect of resource constrained wireless sensor networks (WSNs). All protocols designed for WSNs must be energy aware in order to prolong the network lifetime. In this paper, we have designed a novel MAC layer protocol (I-MAC: Intelligent MAC) for WSNs. By exercising intelligent sleep and wake-up schedule, I-MAC saves energy of the resource constrained sensor nodes greatly. At the same time, I-MAC does not compromise its operational performances. Both analytical study and simulation prove that I-MAC is not only highly energy efficient but also its operational performances are better than similar protocols.

Child oral health in migrant families : A cross-sectional study of caries in 1-4 year old children from migrant backgrounds residing in Melbourne, Australia

- Gibbs, Lisa, De Silva, Andrea, Christian, Bradley, Gold, Lisa, Gussy, Mark, Moore, Laurence, Calache, Hanny, Young, Dana, Riggs, Elisha, Tadic, Maryanne, Watt, Richard, Gondal, Iqbal, Waters, Elizabeth

  • Authors: Gibbs, Lisa , De Silva, Andrea , Christian, Bradley , Gold, Lisa , Gussy, Mark , Moore, Laurence , Calache, Hanny , Young, Dana , Riggs, Elisha , Tadic, Maryanne , Watt, Richard , Gondal, Iqbal , Waters, Elizabeth
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Community Dental Health Vol. 33, no. 2 (2016), p. 100-106
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  • Description: Early Childhood Caries (ECC) is the most common, preventable disease of childhood. It can affect children’s health and wellbeing and children from migrant families may be at greater risk of developing ECC. Objective: To describe ECC in children from migrant families, and explore possible influences. Basic research design: Cross-sectional analysis of caries data collected as baseline data for an oral health promotion study. Participants: The analysis sample included 630 1-4 year-old children clustered within 481 Iraqi, Lebanese and Pakistani families in Melbourne, Australia. Method: Child participants received a community-based visual dental examination. Parents completed a self-administered questionnaire on demographics, ethnicity, and oral health knowledge, behaviour and attitudes. Main outcome measure: Child caries experience. Bivariate associations between oral health behaviours and ethnicity were tested for significance using chi-square. Multivariate logistic regression analyses were performed to identify associations with ECC, adjusting for demographic variables and accounting for clustering by family. Results: Overall, 34% of children in the sample experienced caries (both non-cavitated and cavitated). For all caries lesions, parent’ length of residence in Australia, consumption of sweet drinks and parental education remained as independent predictors of child caries experience. Adding sugar to drinks was an additional risk factor for cavitation. Ethnicity was associated with some individual oral health behaviours suggesting cultural influences on health, however the relationship was not independent of other predictors. Conclusion: Culturally competent oral health promotion interventions should aim to support migrant families with young children, and focus on reducing sweet drink consumption. © BASCD 2016.