Now showing 1 - 10 of 98
  • Publication
    Clustering Approaches for Financial Data Analysis: a Survey
    (CSREA Press, 2012-07-19) ; ;
    Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutes have applied different data mining techniques to enhance their business performance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. However, there are not many studies on clustering approaches for financial data analysis. In this paper, we evaluate different clustering algorithms for analysing different financial datasets varied from time series to transactions. We also discuss the advantages and disadvantages of each method to enhance the understanding of inner structure of financial datasets as well as the capability of each clustering method in this context.
  • Publication
    Network Forensics Readiness and Security Awareness Framework
    The goal of reaching a high level of security in wirelessand wired communication networks is continuously provendifficult to achieve. The speed at which both keepers and violatorsof secure networks is evolving is relatively close. Nowadaysnetwork infrastructures contain a large number of event logscaptured by Firewalls and Domain Controllers (DCs). However,these logs are increasingly becoming an obstacle for networkadministrators in analyzing networks for malicious activities.Forensic investigators mission to detect malicious activities andreconstruct incident scenarios is very complex considering thenumber as well as the quality of these event logs. In this paper,we present the building blocks of a framework for automatednetwork readiness and awareness. The idea of this frameworkis to utilize the current network security outputs to constructforensically comprehensive evidence. In the proposed framework,we cover the three vital phases of the cybercrime managementchain, which are: 1) Forensics Readiness, 2) Active Forensics, and3) Forensics Awareness. Keywords: Network Forensics, ForensicsReadiness, Network Security,Active Forensics, Reactive Forensics,Forensics Awareness and Network Security Framework.
  • Publication
    Digital Forensic Investigations in the Cloud: A Proposed Approach for Irish Law Enforcement
    Cloud computing offers utility oriented Information and Communications Technology (ICT) services to users all over the world. The evolution of Cloud computing is driving the design of data centres by architecting them as networks of virtual services; this enables users to access and run applications from anywhere in the world. Cloud computing offers significant advantages to organisations through the provision of fast and flexible ICT hardware and software infrastructures, thus enabling organisations to focus on creating innovative business values for the services they provide.As the prevalence and usage of networked Cloud computer systems increases, logically the likelihood of these systems being used for criminal behaviour also increases. Thus, this new computing evolution has a direct effect on, and creates challenges for, digital forensic practitioners working in Irish law enforcement.The field of digital forensics has grown rapidly over the last decade due to the rise of the internet and associated crimes; however while the theory is well established, the practical application of the discipline is still new and developing. Law enforcement agencies can no longer rely on traditional digital forensic methods of data acquisition through device seizure to gather relevant evidence pertaining to an investigation. Using traditional digital forensic methods will lead to the loss of valuable evidential material if employed during investigations which involve Cloud based infrastructures.Cloud computing and its impact on digital forensics will continue to grow. This paper analyses traditional digital forensics methods and explains why these are inadequate for Cloud forensic investigations with particular focus on Irish law enforcement agencies. In this paper, we do a survey on approaches to digital forensics of Irish Law Enforcement Agencies for cloud based investigations and we propose a digital forensic framework approach to acquiring data from Cloud environments. This proposed approach aims to overcome the limitations of traditional digital forensics and the challenges Cloud computing presents for digital forensic practitioners working in Irish law enforcement.
  • Publication
    Toward a new cloud-based approach to preserve the privacy for detecting suspicious cases of money laundering in an investment bank
    Today, money laundering poses a serious threat not only to financialinstitutions but also to the nations. This criminal activity is becoming more andmore sophisticated and seems to have moved from the clich of drug traffickingto financing terrorism and surely not forgetting personal gain. Mostinternational financial institutions have been implementing anti-moneylaundering solutions to fight investment fraud. On the other hand, cloud-basedapplications are merging daily and bringing to clients with lower cost ofplatforms and data storage, greater scalability and improved businesscontinuity. Hence, more financial instituitions aim to move their ITinfrastructure to the cloud. However, accessing directly to the customertransaction datasets by a third party could be a confidential issue. This approachis more severe when these solutions are built by collaborating partners.Traditional methods are based on data access agreement but there is still a riskof infringing privacy. In order to preserve the privacy of datasets, different datadisguising methods have been proposed. Nevertheless, analysing disguiseddatasets is a performance issue in the context of detecting suspicious moneylaundering cases where the real value of data has an important impact. Indeed,the results of analysis could also be a privacy issue. Within the scope of acollaboration project for developing a new cloud-based solution for the Anti-Money Laundering Units in an international investment bank, in this paper, wepropose new cloud-based approach using data disguising methods applied inanalysing transaction datasets. We also show that the creating relevantdimensions from the current ones is efficient for analysing transaction datasetsin terms of both detecting suspicious case and privacy preserving.
  • Publication
    ADMIRE framework: Distributed Data Mining on Data Grid platforms
    In this paper, we present the ADMIRE architecture; a new framework for developing novel and innovative data mining techniques to deal with very large and distributed heterogeneous datasets in both commercial and academic applications. The main ADMIRE components are detailed as well as its interfaces allowing the user to efficiently develop and implement their data mining applications techniques on a Grid platform such as Globus ToolKit, DGET, etc.
  • Publication
    Distributed Knowledge Map for Mining Data on Grid Platforms
    Recently, huge datasets representing different applications domains are produced and stored on distributed platforms. These datasets are, generally, owned by different organizations. As a consequence, The scale and distribution nature of these datasets have created the problem of efficient mining and management on these platforms. Most of the existing knowledge management approaches are mainly for centralized data mining. Few of them propose solutions for mining and handling knowledge on Grid. However, the new knowledge is stored and managed as any other kinds of resources.
  • Publication
    An Efficient Data Warehouse for Crop Yield Prediction
    Nowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-processed agricultural data are usually collected using sensors, robots, satellites, weather stations, farm equipment, farmers and agribusinesses while the Internet of Things (IoT) should deliver the promise of wirelessly connecting objects and devices in the agricultural ecosystem. Agricultural data typically captures information about farming entities and operations. Every farming entity encapsulates an individual farming concept, such as field, crop, seed, soil, temperature, humidity, pest, and weed. Agricultural datasets are spatial, temporal, complex, heterogeneous, non-standardized, and very large. In particular, agricultural data is considered as Big Data in terms of volume, variety, velocity and veracity. Designing and developing a data warehouse for precision agriculture is a key foundation for establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. Some of the requirements for such an agricultural data warehouse are privacy, security, and real-time access among its stakeholders (e.g., farmers, farm equipment manufacturers, agribusinesses, co-operative societies, customers and possibly Government agencies). However, currently there are very few reports in the literature that focus on the design of efficient data warehouses with the view of enabling Agricultural Big Data analysis and data mining. In this paper, we propose a system architecture and a database schema for designing and implementing a continental level data warehouse. Besides, some major challenges and agriculture dimensions are also reviewed and analysed.
  • Publication
    Leveraging Decentralisation to Extend the Digital Evidence Acquisition Window: Case Study on BitTorrent Sync
    (Association of Digital Forensics, Security and Law, 2014) ; ; ;
    File synchronization services such as Dropbox, Google Drive, Microsoft OneDrive, Apple iCloud, etc., are becoming increasingly popular in today's always-connected world. A popular alternative to the aforementioned services is BitTorrent Sync. This is a decentralized/cloudless file synchronization service and is gaining significant popularity among Internet users with privacy concerns over where their data is stored and who has the ability to access it. The focus of this paper is the remote recovery of digital evidence pertaining to files identified as being accessed or stored on a suspect's computer or mobile device. A methodology for the identification, investigation, recovery and verification of such remote digital evidence is outlined. Finally, a proof-of-concept remote evidence recovery from BitTorrent Sync shared folder highlighting a number of potential scenarios for the recovery and verification of such evidence.
  • Publication
    Reference Architecture for a Cloud Forensic Readiness System
    The Digital Forensic science is participating to a brand new change represented by the management of incidents in the Cloud Computing Services. Due that the Cloud Computing architecture is uncontrollable because of some specific features,its use to commit crimes is becoming a very critical issue, too. Proactive Cloud Forensics becomes a matter of urgency, due to its capability of collecting critical data before crimes happen, thus saving time and money for the subsequent investigations. In this paper, a proposal for a Cloud Forensic Readiness System is presented. It is conceived as reference architecture, in order to be of general applicability, not technically constrained by any Cloud architecture. The principal aim of this work is to extend our initial proposed Cloud Forensic Readiness System reference architecture, by providing more details and an example of its application by exploiting the Open Stack Cloud Platform.
  • Publication
    An interactive exercise biofeedback Android application utilizing a single inertial measurement unit to support joint replacement rehabilitation
    Boomerang Ortho is an Android application developed with the aim to better support patients in their exercise rehabilitation program following total knee replacement. The use of a single inertial measurement unit (IMU) attached to the lower leg allows for classification of exercise technique, real-time biofeedback, and both self and remote monitoring of patient data. The prototype application for demonstration is currently undergoing pilot testing prior to an assessment of impact on clinical outcome.