DoWitcher: Effective Worm Detection and Containment in the Internet Core

Enterprise networks are increasingly offloading the responsibility for worm detection and containment to the carrier networks. However, current approaches to the zero-day worm detection problem such as those based on content similarity of packet payloads are not scalable to the carrier link speeds (OC-48 and up-wards). In this paper, we introduce a new system, namely DoWitcher, which in contrast to previous approaches is scalable as well as able to detect the stealthiest worms that employ low-propagation rates or polymorphisms to evade detection. DoWitcher uses an incremental approach toward worm detection: First, it examines the layer-4 traffic features to discern the presence of a worm anomaly; Next, it determines a flow-filter mask that can be applied to isolate the suspect worm flows and; Finally, it enables full-packet capture of only those flows that match the mask, which are then processed by a longest common subsequence algorithm to extract the worm content signature. Via a proof-of-concept implementation on a commercially available network analyzer processing raw packets from an OC-48 link, we demonstrate the capability of DoWitcher to detect low-rate worms and extract signatures for even the polymorphic worm

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges

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

Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

Symmetry degree measurement and its applications to anomaly detection

IEEE Anomaly detection is an important technique used to identify patterns of unusual network behavior and keep the network under control. Today, network attacks are increasing in terms of both their number and sophistication. To avoid causing significant traffic patterns and being detected by existing techniques, many new attacks tend to involve gradual adjustment of behaviors, which always generate incomplete sessions due to their running mechanisms. Accordingly, in this work, we employ the behavior symmetry degree to profile the anomalies and further identify unusual behaviors. We first proposed a symmetry degree to identify the incomplete sessions generated by unusual behaviors; we then employ a sketch to calculate the symmetry degree of internal hosts to improve the identification efficiency for online applications. To reduce the memory cost and probability of collision, we divide the IP addresses into four segments that can be used as keys of the hash functions in the sketch. Moreover, to further improve detection accuracy, a threshold selection method is proposed for dynamic traffic pattern analysis. The hash functions in the sketch are then designed using Chinese remainder theory, which can analytically trace the IP addresses associated with the anomalies. We tested the proposed techniques based on traffic data collected from the northwest center of CERNET (China Education and Research Network); the results show that the proposed methods can effectively detect anomalies in large-scale networks

Holistic Network Defense: Fusing Host and Network Features for Attack Classification

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon information among others. In addition, the work compares three different machine learning algorithms and updates the script required to obtain network features. Hybrid method results outperformed host only classification by 31.7% and network only classification by 25%. The new approach also reduces the number of alerts while remaining accurate compared with the commercial IDS SNORT. These results make it such that even the most typical users could understand alert classification messages