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Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment

- Ul Haq, Ikram, Gondal, Iqbal, Vamplew, Peter, Brown, Simon

  • 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
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  • 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.

Proposed machine learning techniques for bridge structural health monitoring : a laboratory study

- Noori Hoshyar, Azadeh, Rashidi, Maria, Yu, Yang, Samali, Bijan

  • Authors: Noori Hoshyar, Azadeh , Rashidi, Maria , Yu, Yang , Samali, Bijan
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Remote Sensing Vol. 15, no. 8 (2023), p.
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  • Description: Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. © 2023 by the authors.

A tree-based stacking ensemble technique with feature selection for network intrusion detection

- Rashid, Mamanur, Kamruzzaman, Joarder, Imam, Tasadduq, Wibowo, Santoso, Gordon, Steven

  • Authors: Rashid, Mamanur , Kamruzzaman, Joarder , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Applied Intelligence Vol. 52, no. 9 (2022), p. 9768-9781
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  • Description: Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due to the ease of automated data collection and subsequently an increased size of collected data on network traffic and activities, the complexity of intrusion analysis is increasing exponentially. A particular issue, due to statistical and computation limitations, a single classifier may not perform well for large scale data as existent in modern IDS contexts. Ensemble methods have been explored in literature in such big data contexts. Although more complicated and requiring additional computation, literature has a note that ensemble methods can result in better accuracy than single classifiers in different large scale data classification contexts, and it is interesting to explore how ensemble approaches can perform in IDS. In this research, we introduce a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15). We further enhance incorporate feature selection techniques to select the best relevant features with the proposed SET. A comprehensive performance analysis shows that our proposed model can better identify the normal and anomaly traffic in network than other existing IDS models. This implies the potentials of our proposed system for cybersecurity in Internet of Things (IoT) and large scale networks. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Anomaly detection on health data

- Samariya, Durgesh, Ma, Jiangang

A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

- Khraisat, Ansam, Alazab, Ammar

  • Authors: Khraisat, Ansam , Alazab, Ammar
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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  • Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).

Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar

  • Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
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  • Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Educational anomaly analytics : features, methods, and challenges

- Guo, Teng, Bai, Xiaomei, Tian, Xue, Firmin, Sally, Xia, Feng

  • Authors: Guo, Teng , Bai, Xiaomei , Tian, Xue , Firmin, Sally , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Frontiers in Big Data Vol. 4, no. (2022), p.
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  • Description: Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field. Copyright © 2022 Guo, Bai, Tian, Firmin and Xia.

Survey of intrusion detection systems : techniques, datasets and challenges

- Khraisat, Ansam, Iqbal, Gondal, Vamplew, Peter, Kamruzzaman, Joarder

Cyberattacks detection in iot-based smart city applications using machine learning techniques

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Imam, Tasadduq, Gordon, Steven

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Imam, Tasadduq , Gordon, Steven
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Environmental Research and Public Health Vol. 17, no. 24 (2020), p. 1-21
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  • Description: In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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