A Decentralized Patient Agent Controlled Blockchain for Remote Patient Monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 15th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019 Vol. 2019-October, p. 207-214
- Full Text: false
- Reviewed:
- Description: Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system. © 2019 IEEE.
- Description: E1
A framework for a QoS based adaptive topology control system for wireless ad hoc networks with multibeam smart antennas
- Authors: Rokonuzzaman, S. K. , Pose, Ronald , Gondal, Iqbal
- Date: 2008
- Type: Text , Conference proceedings
- Full Text: false
- 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.
A novel color image fusion QoS measure for multi-sensor night vision applications
- Authors: Anwaar, Ul-Haq , Gondal, Iqbal , Murshed, Manzur
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: Color image fusion of visible and infra-red imagery can play an important role in multi-sensor night vision systems that are an integral part of modern warfare. Image fusion minimizes the amount of required bandwidth by transmitting the fused image rather than multiple sensor images. Color image fusion can be achieved by combining inputs from original colored sensors or by employing pseudo colorization and color transfer to grayscale images. Various quality measures have been proposed for multi-sensor grayscale image fusion techniques; but no appropriate quality measure has been devised for the quality evaluation of multi-sensor color image fusion. In this paper, we propose a novel color image fusion quality measure, Color Fusion Objective Index (CFOI) based on colorfulness, gradient similarity and mutual information techniques. Experimental results show the effectiveness of CFOI to evaluate the color and salient feature extraction introduced by color fusion techniques into the final fused imagery as well as its consistency with subjective evaluation.
A patient agent to manage blockchains for remote patient monitoring
- 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
- Full Text: false
- Reviewed:
- 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 server side solution for detecting webInject : A machine learning approach
- Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11154 LNAI, p. 162-167
- Full Text: false
- Reviewed:
- Description: With the advancement of client-side on the fly web content generation techniques, it becomes easier for attackers to modify the content of a website dynamically and gain access to valuable information. A majority portion of online attacks is now done by WebInject. The end users are not always skilled enough to differentiate between injected content and actual contents of a webpage. Some of the existing solutions are designed for client side and all the users have to install it in their system, which is a challenging task. In addition, various platforms and tools are used by individuals, so different solutions needed to be designed. Existing server side solution often focuses on sanitizing and filtering the inputs. It will fail to detect obfuscated and hidden scripts. In this paper, we propose a server side solution using a machine learning approach to detect WebInject in banking websites. Unlike other techniques, our method collects features of a Document Object Model (DOM) and classifies it with the help of a pre-trained model.
Action recognition using spatio-temporal distance classifier correlation filter
- Authors: Anwaar-Ul Haq , Gondal, Iqbal , Murshed, Manzur
- Date: 2011
- Type: Text , Conference proceedings
- Relation: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, 6th-8th Dec, 2011
- Full Text: false
- Reviewed:
- Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
- Description: The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like \emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a \emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions
- 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
- Full Text: false
- Reviewed:
- 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)
AFES: An advanced forensic evidence system
- Authors: Black, Paul , Gondal, Iqbal , Brooks, Richard , Yu, Lu
- Date: 2021
- Type: Text , Conference proceedings
- Relation: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), Gold Coast, Australia, 25-29th October, 2021 p. 67-74
- Full Text: false
- Reviewed:
- Description: News media often contain reports that raise doubt related to policing operations. We examine the question of how to improve policing integrity during the execution of search warrants and provide an outline for law enforcement search warrants and digital forensic analysis procedures. Existing techniques for improving the integrity of search warrants are reviewed, limitations are noted, and we propose an Advanced Forensic Evidence System (AFES) to address these limitations.AFES provides an immutable record and biometric authentication of the officers present during the execution of a search warrant, time and location, video recording, seizure record, contemporaneous notes, and photographs. AFES records digital evidence items, imaging details, evidence hashes, provides an access control system, and an immutable record of access to all stored items. AFES uses a permissioned distributed ledger prototype, called Scrybe, developed under NSF aegis, to ensure evidence seizure integrity. Scrybe is run as multiple blockchain instances at law enforcement, prosecution, judicial, and defence organisations to ensure that an immutable record is maintained.
An anomaly intrusion detection system using C5 decision tree classifier
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11154 LNAI, p. 149-155
- Full Text: false
- Reviewed:
- Description: Due to increase in intrusion activities over internet, many intrusion detection systems are proposed to detect abnormal activities, but most of these detection systems suffer a common problem which is producing a high number of alerts and a huge number of false positives. As a result, normal activities could be classified as intrusion activities. This paper examines different data mining techniques that could minimize both the number of false negatives and false positives. C5 classifier’s effectiveness is examined and compared with other classifiers. Results should that false negatives are reduced and intrusion detection has been improved significantly. A consequence of minimizing the false positives has resulted in reduction in the amount of the false alerts as well. In this study, multiple classifiers have been compared with C5 decision tree classifier using NSL_KDD dataset and results have shown that C5 has achieved high accuracy and low false alarms as an intrusion detection system.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring
- 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
- Full Text:
- Reviewed:
- 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
Automated multi-sensor color video fusion for nighttime video surveillance
- Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- 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.
Blockchain leveraged task migration in body area sensor networks
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
- Full Text:
- Reviewed:
- Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
- Description: E1
Carry me if you can : A utility based forwarding scheme for content sharing in tourist destinations
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 22nd Asia-Pacific Conference on Communications, APCC 2016; Yogyakarta, Indonesia; 25th-27th August 2016 p. 261-267
- Full Text:
- Reviewed:
- Description: Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships. © 2016 IEEE.
Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment
- Authors: Ul Haq, Ikram , Gondal, Iqbal , Vamplew, Peter , Brown, Simon
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 16th Australasian Conference on Data Mining, AusDM 2018; Bathurst, NSW; 28 November 2018 through 30 November 2018 Vol. 996, p. 69-80
- Full Text: false
- Reviewed:
- Description: Fraud detection for online banking is an important research area, but one of the challenges is the heterogeneous nature of transactions data i.e. a combination of numeric as well as mixed attributes. Usually, numeric format data gives better performance for classification, regression and clustering algorithms. However, many machine learning problems have categorical, or nominal features, rather than numeric features only. In addition, some machine learning platforms such as Apache Spark accept numeric data only. One-hot Encoding (OHE) is a widely used approach for transforming categorical features to numerical features in traditional data mining tasks. The one-hot approach has some challenges as well: the sparseness of the transformed data and that the distinct values of an attribute are not always known in advance. Other than the model accuracy, compactness of machine learning models is equally important due to growing memory and storage needs. This paper presents an innovative technique to transform categorical features to numeric features by compacting sparse data even if all the distinct values are not known. The transformed data can be used for the development of fraud detection systems. The accuracy of the results has been validated on synthetic and real bank fraud data and a publicly available anomaly detection (KDD-99) dataset on a multi-node data cluster. © Springer Nature Singapore Pte Ltd. 2019.
Complex anomaly for enhanced machine independent condition monitoring
- Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 9th International Conference on Open Source Systems and Technologies, ICOSST 2015; Lahore, Pakistan; 17th-19th December 2015
- Full Text: false
- Description: Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework. © 2015 IEEE.
- Description: ICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings
Cybersecurity indexes for eHealth
- Authors: Burke, Wendy , Oseni, Taiwo , Jolfaei, Alireza , Gondal, Iqbal
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-8
- Full Text: false
- Reviewed:
- Description: This study aimed to explore the cybersecurity landscape to identify cybersecurity indexes that may be relevant to the health industry. While the healthcare sector poses security concerns regarding patients' records, cybersecurity in the healthcare sector has not been given much consideration. Cybersecurity indexes are a survey that measures security preparedness and capabilities of a country or organisation. An index is made up of a series of questions, often broken into categories. These categories target areas such as law, technical responses, organisational threats, capacity building and social context. Some indexes provide ranking capabilities against other countries, while others directly evaluate what it means to be cyber-ready. In this paper, cybersecurity indexes were reviewed regarding the level of assessment (country level/organisation level), and their consideration of the wider community, the health sector, and their appearance in academic literature. Results from this study found that there was no consistency between the indexes investigated, with each index having a diverse number of categories and indicators. Some indexes resulted in a score; others did not rank their results in league tables. Evidence to calculate the level of adherence was often obtained from secondary sources, with four of the country indexes using both primary and secondary sources. Eight (out of fourteen) indexes measured wider community indicators and only one index specifically measured eHealth services. Findings from the initial systematic review suggest that hardly any peer-reviewed journal articles exist on the topic of cybersecurity indexes. The paper concludes that most of the indexes studied are broad and do not consider the eHealth sector specifically. Each index relies on a different process to gauge cybersecurity, with little to no academic rigour. It is expected that this research will contribute to the current (limited) literature addressing cybersecurity indexes.
- Description: ACM International Conference Proceeding Series
Diversified adaptive frequency rolling to mitigate self and static interferences
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: Increase in the number of coexisting networks in Industrial, Scientific and Medical (ISM) band cause interferences and demands for intelligent interference avoidance schemes. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets which has the tendency to mitigate both the self and static interferences and ensures sufficient frequency diversity. Simulation studies validate the prospects for the proposed scheme to be used for frequency hopping networks against already existing techniques, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR).
Dynamic content distribution for decentralized sharing in tourist spots using demand and supply
- Authors: Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal , Kaisar, Shahriar
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017; Valencia, Spain; 26th-30th June 2016 p. 2121-2126
- Full Text: false
- Reviewed:
- Description: Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach. © 2017 IEEE.
- Description: 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
Dynamically recommending repositories for health data : a machine learning model
- Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Full Text: false
- Reviewed:
- Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
- Description: E1
Evolved similarity techniques in malware analysis
- 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
- Full Text: false
- Reviewed:
- 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.