207 research outputs found

    Click Fraud Detection in Online and In-app Advertisements: A Learning Based Approach

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    Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness -- while existing feature selection techniques address this issue, they have their own performance limitations. By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches

    An efficient network intrusion detection and classification system

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    Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

    Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

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    An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

    Efficient Intrusion Detection Model Using Ensemble Methods

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    Ensemble method or any combination model train multiple learners to solve the classification or regression problems, not by simply ordinary learning approaches that can able to construct one learner from training data rather construct a set of learners and combine them. Boosting algorithm is one of the most important recent developments in the area of classification methodology. Boosting belongs to a family of algorithms that has the capability to convert a group of weak learners to strong learners. Boosting works in a sequential manner by adding a classification algorithm to the next updated weight of the training samples by doing the majority voting technique of the sequence of classifiers. The boosting method combines the weak models to produce a powerful one and reduces the bias of the combined model. AdaBoost algorithm is the most influential algorithm that efficiently combines the weak learners to generate a strong classifier that could be able to classify a training data with better accuracy. AdaBoost differs from the current existing boosting methods in detection accuracy, error cost minimization, computational time and detection rate. Detection accuracy and computational cost are the two main metrics used to analyze the performance of AdaBoost classification algorithm. From the simulation result, it is evident that AdaBoost algorithm could able to achieve high detection accuracy with less computational time, and minimum cost compared to a single classifier. We have proposed a predictive model to classify normal class and attack class and an online inference engine is being imposed, either to allow or deny access to a network

    An Overview on Application of Machine Learning Techniques in Optical Networks

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    Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

    Comparing a Hybrid Multi-layered Machine Learning Intrusion Detection System to Single-layered and Deep Learning Models

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    Advancements in computing technology have created additional network attack surface, allowed the development of new attack types, and increased the impact caused by an attack. Researchers agree, current intrusion detection systems (IDSs) are not able to adapt to detect these new attack forms, so alternative IDS methods have been proposed. Among these methods are machine learning-based intrusion detection systems. This research explores the current relevant studies related to intrusion detection systems and machine learning models and proposes a new hybrid machine learning IDS model consisting of the Principal Component Analysis (PCA) and Support Vector Machine (SVM) learning algorithms. The NSL-KDD Dataset, benchmark dataset for IDSs, is used for comparing the models’ performance. The performance accuracy and false-positive rate of the hybrid model are compared to the results of the model’s individual algorithmic components to determine which components most impact attack prediction performance. The performance metrics of the hybrid model are also compared to two deep learning Autoencoder Neuro Network models and the results found that the complexity of the model does not add to the performance accuracy. The research showed that pre-processing and feature selection impact the predictive accuracy across models. Future research recommendations were to implement the proposed hybrid IDS model into a live network for testing and analysis, and to focus research into the pre-processing algorithms that improve performance accuracy, and lower false-positive rate. This research indicated that pre-processing and feature selection/feature extraction can increase model performance accuracy and decrease false-positive rate helping businesses to improve network security
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