Evaluation of deep neural networks for reduction of credit card fraud alerts

Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection

Extending Structural Learning Paradigms for High-Dimensional Machine Learning and Analysis

Structure-based machine-learning techniques are frequently used in extensions of supervised learning, such as active, semi-supervised, multi-modal, and multi-task learning. A common step in many successful methods is a structure-discovery process that is made possible through the addition of new information, which can be user feedback, unlabeled data, data from similar tasks, alternate views of the problem, etc. Learning paradigms developed in the above-mentioned fields have led to some extremely flexible, scalable, and successful multivariate analysis approaches. This success and flexibility offer opportunities to expand the use of machine learning paradigms to more complex analyses. In particular, while information is often readily available concerning complex problems, the relationships among the information rarely follow the simple labeled-example-based setup that supervised learning is based upon. Even when it is possible to incorporate additional data in such forms, the result is often an explosion in the dimensionality of the input space, such that both sample complexity and computational complexity can limit real-world success. In this work, we review many of the latest structural learning approaches for dealing with sample complexity. We expand their use to generate new paradigms for combining some of these learning strategies to address more complex problem spaces. We overview extreme-scale data analysis problems where sample complexity is a much more limiting factor than computational complexity, and outline new structural-learning approaches for dealing jointly with both. We develop and demonstrate a method for dealing with sample complexity in complex systems that leads to a more scalable algorithm than other approaches to large-scale multi-variate analysis. This new approach reflects the underlying problem structure more accurately by using interdependence to address sample complexity, rather than ignoring it for the sake of tractability

Towards a Multi-Layered Phishing Detection.

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art

Graph-based feature enrichment for online intrusion detection in virtual networks

The increasing number of connected devices to provide the required ubiquitousness of Internet of Things paves the way for distributed network attacks at an unprecedented scale. Graph theory, strengthened by machine learning techniques, improves an automatic discovery of group behavior patterns of network threats often omitted by traditional security systems. Furthermore, Network Function Virtualization is an emergent technology that accelerates the provisioning of on-demand security function chains tailored to an application. Therefore, repeatable compliance tests and performance comparison of such function chains are mandatory. The contributions of this dissertation are divided in two parts. First, we propose an intrusion detection system for online threat detection enriched by a graph-learning analysis. We develop a feature enrichment algorithm that infers metrics from a graph analysis. By using different machine learning techniques, we evaluated our algorithm for three network traffic datasets. We show that the proposed graph-based enrichment improves the threat detection accuracy up to 15.7% and significantly reduces the false positives rate. Second, we aim to evaluate intrusion detection systems deployed as virtual network functions. Therefore, we propose and develop SFCPerf, a framework for an automatic performance evaluation of service function chaining. To demonstrate SFCPerf functionality, we design and implement a prototype of a security service function chain, composed of our intrusion detection system and a firewall. We show the results of a SFCPerf experiment that evaluates the chain prototype on top of the open platform for network function virtualization (OPNFV).O crescente número de dispositivos IoT conectados contribui para a ocorrência de ataques distribuídos de negação de serviço a uma escala sem precedentes. A Teoria de Grafos, reforçada por técnicas de aprendizado de máquina, melhora a descoberta automática de padrões de comportamento de grupos de ameaças de rede, muitas vezes omitidas pelos sistemas tradicionais de segurança. Nesse sentido, a virtualização da função de rede é uma tecnologia emergente que pode acelerar o provisionamento de cadeias de funções de segurança sob demanda para uma aplicação. Portanto, a repetição de testes de conformidade e a comparação de desempenho de tais cadeias de funções são obrigatórios. As contribuições desta dissertação são separadas em duas partes. Primeiro, é proposto um sistema de detecção de intrusão que utiliza um enriquecimento baseado em grafos para aprimorar a detecção de ameaças online. Um algoritmo de enriquecimento de características é desenvolvido e avaliado através de diferentes técnicas de aprendizado de máquina. Os resultados mostram que o enriquecimento baseado em grafos melhora a acurácia da detecção de ameaças até 15,7 % e reduz significativamente o número de falsos positivos. Em seguida, para avaliar sistemas de detecção de intrusões implantados como funções virtuais de rede, este trabalho propõe e desenvolve o SFCPerf, um framework para avaliação automática de desempenho do encadeamento de funções de rede. Para demonstrar a funcionalidade do SFCPerf, ´e implementado e avaliado um protótipo de uma cadeia de funções de rede de segurança, composta por um sistema de detecção de intrusão (IDS) e um firewall sobre a plataforma aberta para virtualização de função de rede (OPNFV)

Advanced persistent threats detection based on deep learning approach.

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APTDASAC" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and preprocessing, (ii) Data analysis layer; applies the core process of "APTDASAC" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy