DYNAMIC DATA EXFILTRATION OVER COMMON PROTOCOLS VIA SOCKET LAYER PROTOCOL CUSTOMIZATION

Obfuscated data exfiltration perpetrated by malicious actors presents a significant threat to organizations looking to protect sensitive data. Socket layer protocol customization presents the potential to enhance obfuscated data exfiltration by providing a protocol-agnostic means of embedding targeted data within application payloads of established socket connections. Fully evaluating and characterizing this technique will serve as an important step in the development of suitable mitigations. This thesis evaluated the performance of this method of data exfiltration through experimentation to determine its viability and identify its limitations. The evaluation assessed the effectiveness of exfiltration via socket layer customization with various application protocols and characterized its use to determine the most suitable protocols. Basic host-based and network-based security controls were introduced to test the exfiltration method’s ability to bypass typical security controls implemented to prevent data exfiltration. The experimentation results indicate that this exfiltration method is both viable and applicable across multiple application protocols. It proved flexible enough in its design and configuration to bypass basic host-based access controls and general network intrusion prevention system packet inspection. Deep packet inspection was identified as a potential solution; however, the required inspection and filtering granularity might make implementation infeasible.Office of Naval Research, Arlington, VA 22203-1995Outstanding ThesisPetty Officer First Class, United States NavyApproved for public release. Distribution is unlimited

Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things

In the past, industrial control systems were ‘air gapped’ and isolated from more conventional networks. They used specialist protocols, such as Modbus, that are very different from TCP/IP. Individual devices used proprietary operating systems rather than the more familiar Linux or Windows. However, things are changing. There is a move for greater connectivity – for instance so that higher-level enterprise management systems can exchange information that helps optimise production processes. At the same time, industrial systems have been influenced by concepts from the Internet of Things; where the information derived from sensors and actuators in domestic and industrial components can be addressed through network interfaces. This paper identifies a range of cyber security and safety concerns that arise from these developments. The closing sections introduce potential solutions and identify areas for future research

Deteção de propagação de ameaças e exfiltração de dados em redes empresariais

Modern corporations face nowadays multiple threats within their networks. In an era where companies are tightly dependent on information, these threats can seriously compromise the safety and integrity of sensitive data. Unauthorized access and illicit programs comprise a way of penetrating the corporate networks, able to traversing and propagating to other terminals across the private network, in search of confidential data and business secrets. The efficiency of traditional security defenses are being questioned with the number of data breaches occurred nowadays, being essential the development of new active monitoring systems with artificial intelligence capable to achieve almost perfect detection in very short time frames. However, network monitoring and storage of network activity records are restricted and limited by legal laws and privacy strategies, like encryption, aiming to protect the confidentiality of private parties. This dissertation proposes methodologies to infer behavior patterns and disclose anomalies from network traffic analysis, detecting slight variations compared with the normal profile. Bounded by network OSI layers 1 to 4, raw data are modeled in features, representing network observations, and posteriorly, processed by machine learning algorithms to classify network activity. Assuming the inevitability of a network terminal to be compromised, this work comprises two scenarios: a self-spreading force that propagates over internal network and a data exfiltration charge which dispatch confidential info to the public network. Although features and modeling processes have been tested for these two cases, it is a generic operation that can be used in more complex scenarios as well as in different domains. The last chapter describes the proof of concept scenario and how data was generated, along with some evaluation metrics to perceive the model’s performance. The tests manifested promising results, ranging from 96% to 99% for the propagation case and 86% to 97% regarding data exfiltration.Nos dias de hoje, várias organizações enfrentam múltiplas ameaças no interior da sua rede. Numa época onde as empresas dependem cada vez mais da informação, estas ameaças podem compremeter seriamente a segurança e a integridade de dados confidenciais. O acesso não autorizado e o uso de programas ilícitos constituem uma forma de penetrar e ultrapassar as barreiras organizacionais, sendo capazes de propagarem-se para outros terminais presentes no interior da rede privada com o intuito de atingir dados confidenciais e segredos comerciais. A eficiência da segurança oferecida pelos sistemas de defesa tradicionais está a ser posta em causa devido ao elevado número de ataques de divulgação de dados sofridos pelas empresas. Desta forma, o desenvolvimento de novos sistemas de monitorização ativos usando inteligência artificial é crucial na medida de atingir uma deteção mais precisa em curtos períodos de tempo. No entanto, a monitorização e o armazenamento dos registos da atividade da rede são restritos e limitados por questões legais e estratégias de privacidade, como a cifra dos dados, visando proteger a confidencialidade das entidades. Esta dissertação propõe metodologias para inferir padrões de comportamento e revelar anomalias através da análise de tráfego que passa na rede, detetando pequenas variações em comparação com o perfil normal de atividade. Delimitado pelas camadas de rede OSI 1 a 4, os dados em bruto são modelados em features, representando observações de rede e, posteriormente, processados por algoritmos de machine learning para classificar a atividade de rede. Assumindo a inevitabilidade de um terminal ser comprometido, este trabalho compreende dois cenários: um ataque que se auto-propaga sobre a rede interna e uma tentativa de exfiltração de dados que envia informações para a rede pública. Embora os processos de criação de features e de modelação tenham sido testados para estes dois casos, é uma operação genérica que pode ser utilizada em cenários mais complexos, bem como em domínios diferentes. O último capítulo inclui uma prova de conceito e descreve o método de criação dos dados, com a utilização de algumas métricas de avaliação de forma a espelhar a performance do modelo. Os testes mostraram resultados promissores, variando entre 96% e 99% para o caso da propagação e entre 86% e 97% relativamente ao roubo de dados.Mestrado em Engenharia de Computadores e Telemátic

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm

Towards a robust, effective and resource-efficient machine learning technique for IoT security monitoring.

Internet of Things (IoT) devices are becoming increasingly popular and an integral part of our everyday lives, making them a lucrative target for attackers. These devices require suitable security mechanisms that enable robust and effective detection of attacks. Machine learning (ML) and its subdivision Deep Learning (DL) methods offer a promise, but they can be computationally expensive in providing better detection for resource-constrained IoT devices. Therefore, this research proposes an optimization method to train ML and DL methods for effective and efficient security monitoring of IoT devices. It first investigates the feasibility of the Light Gradient Boosting Machine (LGBM) for attack detection in IoT environments, proposing an optimization procedure to obtain its effective counterparts. The trained LGBM can successfully discern attacks and regular traffic in various IoT benchmark datasets used in this research. As LGBM is a traditional ML technique, it may be difficult to learn complex network traffic patterns present in IoT datasets. Therefore, we further examine Deep Neural Networks (DNNs), proposing an effective and efficient DNN-based security solution for IoT security monitoring to leverage more resource savings and accurate attack detection. Investigation results are promising, as the proposed optimization method exploits the mini-batch gradient descent with simulated micro-batching in building effective and efficient DNN-based IoT security solutions. Following the success of DNN for effective and efficient attack detection, we further exploit it in the context of adversarial attack resistance. The resulting DNN is more resistant to adversarial samples than its benchmark counterparts and other conventional ML methods. To evaluate the effectiveness of our proposal, we considered on-device learning in federated learning settings, using decentralized edge devices to augment data privacy in resource-constrained environments. To this end, the performance of the method was evaluated against various realistic IoT datasets (e.g. NBaIoT, MNIST) on virtual and realistic testbed set-ups with GB-BXBT-2807 edge-computing-like devices. The experimental results show that the proposed method can reduce memory and time usage by 81% and 22% in the simulated environment of virtual workers compared to its benchmark counterpart. In the realistic testbed scenario, it saves 6% of memory footprints with a reduction of execution time by 15%, while maintaining a better and state-of-the-art accuracy

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

Three Decades of Deception Techniques in Active Cyber Defense -- Retrospect and Outlook

Deception techniques have been widely seen as a game changer in cyber defense. In this paper, we review representative techniques in honeypots, honeytokens, and moving target defense, spanning from the late 1980s to the year 2021. Techniques from these three domains complement with each other and may be leveraged to build a holistic deception based defense. However, to the best of our knowledge, there has not been a work that provides a systematic retrospect of these three domains all together and investigates their integrated usage for orchestrated deceptions. Our paper aims to fill this gap. By utilizing a tailored cyber kill chain model which can reflect the current threat landscape and a four-layer deception stack, a two-dimensional taxonomy is developed, based on which the deception techniques are classified. The taxonomy literally answers which phases of a cyber attack campaign the techniques can disrupt and which layers of the deception stack they belong to. Cyber defenders may use the taxonomy as a reference to design an organized and comprehensive deception plan, or to prioritize deception efforts for a budget conscious solution. We also discuss two important points for achieving active and resilient cyber defense, namely deception in depth and deception lifecycle, where several notable proposals are illustrated. Finally, some outlooks on future research directions are presented, including dynamic integration of different deception techniques, quantified deception effects and deception operation cost, hardware-supported deception techniques, as well as techniques developed based on better understanding of the human element.Comment: 19 page