Traffic Monitoring and analysis for source identification

Ph.DDOCTOR OF PHILOSOPH

CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

Inspecting packets to detect intrusions faces challenges when coping with a high volume of network traffic. Packet-based detection processes every payload on the wire, which degrades the performance of network intrusion detection system (NIDS). This issue requires an introduction of a flow-based NIDS that reduces the amount of data to be processed by examining aggregated information of related packets. However, flow-based detection still suffers from the generation of the false positive alerts due to incomplete data input. This study proposed a Conditional Hybrid Intrusion Detection (CHID) by combining the flow-based with packet-based detection. In addition, it is also aimed to improve the resource consumption of the packet-based detection approach. CHID applied attribute wrapper features evaluation algorithms that marked malicious flows for further analysis by the packet-based detection. Input Framework approach was employed for triggering packet flows between the packetbased and flow-based detections. A controlled testbed experiment was conducted to evaluate the performance of detection mechanism’s CHID using datasets obtained from on different traffic rates. The result of the evaluation showed that CHID gains a significant performance improvement in terms of resource consumption and packet drop rate, compared to the default packet-based detection implementation. At a 200 Mbps, CHID in IRC-bot scenario, can reduce 50.6% of memory usage and decreases 18.1% of the CPU utilization without packets drop. CHID approach can mitigate the false positive rate of flow-based detection and reduce the resource consumption of packet-based detection while preserving detection accuracy. CHID approach can be considered as generic system to be applied for monitoring of intrusion detection systems

Detection and Mitigation of Steganographic Malware

A new attack trend concerns the use of some form of steganography and information hiding to make malware stealthier and able to elude many standard security mechanisms. Therefore, this Thesis addresses the detection and the mitigation of this class of threats. In particular, it considers malware implementing covert communications within network traffic or cloaking malicious payloads within digital images. The first research contribution of this Thesis is in the detection of network covert channels. Unfortunately, the literature on the topic lacks of real traffic traces or attack samples to perform precise tests or security assessments. Thus, a propaedeutic research activity has been devoted to develop two ad-hoc tools. The first allows to create covert channels targeting the IPv6 protocol by eavesdropping flows, whereas the second allows to embed secret data within arbitrary traffic traces that can be replayed to perform investigations in realistic conditions. This Thesis then starts with a security assessment concerning the impact of hidden network communications in production-quality scenarios. Results have been obtained by considering channels cloaking data in the most popular protocols (e.g., TLS, IPv4/v6, and ICMPv4/v6) and showcased that de-facto standard intrusion detection systems and firewalls (i.e., Snort, Suricata, and Zeek) are unable to spot this class of hazards. Since malware can conceal information (e.g., commands and configuration files) in almost every protocol, traffic feature or network element, configuring or adapting pre-existent security solutions could be not straightforward. Moreover, inspecting multiple protocols, fields or conversations at the same time could lead to performance issues. Thus, a major effort has been devoted to develop a suite based on the extended Berkeley Packet Filter (eBPF) to gain visibility over different network protocols/components and to efficiently collect various performance indicators or statistics by using a unique technology. This part of research allowed to spot the presence of network covert channels targeting the header of the IPv6 protocol or the inter-packet time of generic network conversations. In addition, the approach based on eBPF turned out to be very flexible and also allowed to reveal hidden data transfers between two processes co-located within the same host. Another important contribution of this part of the Thesis concerns the deployment of the suite in realistic scenarios and its comparison with other similar tools. Specifically, a thorough performance evaluation demonstrated that eBPF can be used to inspect traffic and reveal the presence of covert communications also when in the presence of high loads, e.g., it can sustain rates up to 3 Gbit/s with commodity hardware. To further address the problem of revealing network covert channels in realistic environments, this Thesis also investigates malware targeting traffic generated by Internet of Things devices. In this case, an incremental ensemble of autoencoders has been considered to face the ''unknown'' location of the hidden data generated by a threat covertly exchanging commands towards a remote attacker. The second research contribution of this Thesis is in the detection of malicious payloads hidden within digital images. In fact, the majority of real-world malware exploits hiding methods based on Least Significant Bit steganography and some of its variants, such as the Invoke-PSImage mechanism. Therefore, a relevant amount of research has been done to detect the presence of hidden data and classify the payload (e.g., malicious PowerShell scripts or PHP fragments). To this aim, mechanisms leveraging Deep Neural Networks (DNNs) proved to be flexible and effective since they can learn by combining raw low-level data and can be updated or retrained to consider unseen payloads or images with different features. To take into account realistic threat models, this Thesis studies malware targeting different types of images (i.e., favicons and icons) and various payloads (e.g., URLs and Ethereum addresses, as well as webshells). Obtained results showcased that DNNs can be considered a valid tool for spotting the presence of hidden contents since their detection accuracy is always above 90% also when facing ''elusion'' mechanisms such as basic obfuscation techniques or alternative encoding schemes. Lastly, when detection or classification are not possible (e.g., due to resource constraints), approaches enforcing ''sanitization'' can be applied. Thus, this Thesis also considers autoencoders able to disrupt hidden malicious contents without degrading the quality of the image

Network Coding based Information Security in Multi-hop Wireless Networks

Multi-hop Wireless Networks (MWNs) represent a class of networks where messages are forwarded through multiple hops of wireless transmission. Applications of this newly emerging communication paradigm include asset monitoring wireless sensor networks (WSNs), command communication mobile ad hoc networks (MANETs), community- or campus-wide wireless mesh networks (WMNs), etc. Information security is one of the major barriers to the wide-scale deployment of MWNs but has received little attention so far. On the one hand, due to the open wireless channels and multi-hop wireless transmissions, MWNs are vulnerable to various information security threats such as eavesdropping, data injection/modification, node compromising, traffic analysis, and flow tracing. On the other hand, the characteristics of MWNs including the vulnerability of intermediate network nodes, multi-path packet forwarding, and limited computing capability and storage capacity make the existing information security schemes designed for the conventional wired networks or single-hop wireless networks unsuitable for MWNs. Therefore, newly designed schemes are highly desired to meet the stringent security and performance requirements for the information security of MWNs. In this research, we focus on three fundamental information security issues in MWNs: efficient privacy preservation for source anonymity, which is critical to the information security of MWNs; the traffic explosion issue, which targets at preventing denial of service (DoS) and enhancing system availability; and the cooperative peer-to-peer information exchange issue, which is critical to quickly achieve maximum data availability if the base station is temporarily unavailable or the service of the base station is intermittent. We have made the following three major contributions. Firstly, we identify the severe threats of traffic analysis/flow tracing attacks to the information security in network coding enabled MWNs. To prevent these attacks and achieve source anonymity in MWNs, we propose a network coding based privacy-preserving scheme. The unique “mixing” feature of network coding is exploited in the proposed scheme to confuse adversaries from conducting advanced privacy attacks, such as time correlation, size correlation, and message content correlation. With homomorphic encryption functions, the proposed scheme can achieve both privacy preservation and data confidentiality, which are two critical information security requirements. Secondly, to prevent traffic explosion and at the same time achieve source unobservability in MWNs, we propose a network coding based privacy-preserving scheme, called SUNC (Source Unobservability using Network Coding). Network coding is utilized in the scheme to automatically absorb dummy messages at intermediate network nodes, and thus, traffic explosion induced denial of service (DoS) can be naturally prevented to ensure the system availability. In addition to ensuring system availability and achieving source unobservability, SUNC can also thwart internal adversaries. Thirdly, to enhance the data availability when a base station is temporarily unavailable or the service of the base station is intermittent, we propose a cooperative peer-to-peer information exchange scheme based on network coding. The proposed scheme can quickly accomplish optimal information exchange in terms of throughput and transmission delay. For each research issue, detailed simulation results in terms of computational overhead, transmission efficiency, and communication overhead, are given to demonstrate the efficacy and efficiency of the proposed solutions

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

TORKAMELEON. IMPROVING TOR’S CENSORSHIP RESISTANCE WITH K-ANONYMIZATION MEDIA MORPHING COVERT INPUT CHANNELS

Anonymity networks such as Tor and other related tools are powerful means of increas- ing the anonymity and privacy of Internet users’ communications. Tor is currently the most widely used solution by whistleblowers to disclose confidential information and denounce censorship measures, including violations of civil rights, freedom of expres- sion, or guarantees of free access to information. However, recent research studies have shown that Tor is vulnerable to so-called powerful correlation attacks carried out by global adversaries or collaborative Internet censorship parties. In the Tor ”arms race” scenario, we can see that as new censorship, surveillance, and deep correlation tools have been researched, new, improved solutions for preserving anonymity have also emerged. In recent research proposals, unobservable encapsulation of IP packets in covert media channels is one of the most promising defenses against such threat models. They leverage WebRTC-based covert channels as a robust and practical approach against powerful traf- fic correlation analysis. At the same time, these solutions are difficult to combat through the traffic-blocking measures commonly used by censorship authorities. In this dissertation, we propose TorKameleon, a censorship evasion solution de- signed to protect Tor users with increased censorship resistance against powerful traffic correlation attacks executed by global adversaries. The system is based on flexible K- anonymization input circuits that can support TLS tunneling and WebRTC-based covert channels before forwarding users’ original input traffic to the Tor network. Our goal is to protect users from machine and deep learning correlation attacks between incom- ing user traffic and observed traffic at different Tor network relays, such as middle and egress relays. TorKameleon is the first system to implement a Tor pluggable transport based on parameterizable TLS tunneling and WebRTC-based covert channels. We have implemented the TorKameleon prototype and performed extensive validations to ob- serve the correctness and experimental performance of the proposed solution in the Tor environment. With these evaluations, we analyze the necessary tradeoffs between the performance of the standard Tor network and the achieved effectiveness and performance of TorKameleon, capable of preserving the required unobservability properties.Redes de anonimização como o Tor e soluções ou ferramentas semelhantes são meios poderosos de aumentar a anonimidade e a privacidade das comunicações de utilizadores da Internet . O Tor é atualmente a rede de anonimato mais utilizada por delatores para divulgar informações confidenciais e denunciar medidas de censura tais como violações de direitos civis e da liberdade de expressão, ou falhas nas garantias de livre acesso à informação. No entanto, estudos recentes mostram que o Tor é vulnerável a adversários globais ou a entidades que colaboram entre si para garantir a censura online. Neste cenário competitivo e de jogo do “gato e do rato”, é possível verificar que à medida que novas soluções de censura e vigilância são investigadas, novos sistemas melhorados para a preservação de anonimato são também apresentados e refinados. O encapsulamento de pacotes IP em túneis encapsulados em protocolos de media são uma das mais promissoras soluções contra os novos modelos de ataque à anonimidade. Estas soluções alavancam canais encobertos em protocolos de media baseados em WebRTC para resistir a poderosos ataques de correlação de tráfego e a medidas de bloqueios normalmente usadas pelos censores. Nesta dissertação propomos o TorKameleon, uma solução desenhada para protoger os utilizadores da rede Tor contra os mais recentes ataques de correlação feitos por um modelo de adversário global. O sistema é baseado em estratégias de anonimização e reencaminhamento do tráfego do utilizador através de K nós, utilizando também encap- sulamento do tráfego em canais encobertos em túneis TLS ou WebRTC. O nosso objetivo é proteger os utilizadores da rede Tor de ataques de correlação implementados através de modelos de aprendizagem automática feitos entre o tráfego do utilizador que entra na rede Tor e esse mesmo tráfego noutro segmento da rede, como por exemplo nos nós de saída da rede. O TorKameleon é o primeiro sistema a implementar um Tor pluggable transport parametrizável, baseado em túneis TLS ou em canais encobertos em protocolos media. Implementamos um protótipo do sistema e realizamos uma extensa avalição expe- rimental, inserindo a solução no ambiente da rede Tor. Com base nestas avaliações, anali- zamos o tradeoff necessário entre a performance da rede Tor e a eficácia e a performance obtida do TorKameleon, que garante as propriedades de preservação de anonimato

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

System and Methods for Detecting Unwanted Voice Calls

Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packet-based IP networks. However, this migration is successful only if the present problems in IP networks are addressed before deploying VoIP infrastructure on a large scale. One of the important issues that the present VoIP networks face is the problem of unwanted calls commonly referred to as SPIT (spam over Internet telephony). Mostly, these SPIT calls are from unknown callers who broadcast unwanted calls. There may be unwanted calls from legitimate and known people too. In this case, the unwantedness depends on social proximity of the communicating parties. For detecting these unwanted calls, I propose a framework that analyzes incoming calls for unwanted behavior. The framework includes a VoIP spam detector (VSD) that analyzes incoming VoIP calls for spam behavior using trust and reputation techniques. The framework also includes a nuisance detector (ND) that proactively infers the nuisance (or reluctance of the end user) to receive incoming calls. This inference is based on past mutual behavior between the calling and the called party (i.e., caller and callee), the callee's presence (mood or state of mind) and tolerance in receiving voice calls from the caller, and the social closeness between the caller and the callee. The VSD and ND learn the behavior of callers over time and estimate the possibility of the call to be unwanted based on predetermined thresholds configured by the callee (or the filter administrators). These threshold values have to be automatically updated for integrating dynamic behavioral changes of the communicating parties. For updating these threshold values, I propose an automatic calibration mechanism using receiver operating characteristics curves (ROC). The VSD and ND use this mechanism for dynamically updating thresholds for optimizing their accuracy of detection. In addition to unwanted calls to the callees in a VoIP network, there can be unwanted traffic coming into a VoIP network that attempts to compromise VoIP network devices. Intelligent hackers can create malicious VoIP traffic for disrupting network activities. Hence, there is a need to frequently monitor the risk levels of critical network infrastructure. Towards realizing this objective, I describe a network level risk management mechanism that prioritizes resources in a VoIP network. The prioritization scheme involves an adaptive re-computation model of risk levels using attack graphs and Bayesian inference techniques. All the above techniques collectively account for a domain-level VoIP security solution

Seamless virtual network for international business continuity in presence of intentional blocks

東京電機大学201