Security analysis of network neighbors

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010O presente trabalho aborda um problema comum a muitos dos actuais fornecedores de serviços Internet (ISPs): mitigação eficiente de tráfego malicioso na sua rede. Este tráfego indesejado impõe um desperdício de recursos de rede o que leva a uma consequente degradação da qualidade de serviço. Cria também um ambiente inseguro para os clientes, minando o potencial oferecido pela Internet e abrindo caminho para actividades criminosas graves. Algumas das principais condicionantes na criação de sistemas capazes de resolver estes problemas são: a enorme quantidade de tráfego a ser analisado, o facto da Internet ser inerentemente anónima e a falta de incentivo para os operadores de redes de trânsito em bloquear este tipo de tráfego. No âmbito de um ISP de média escala, este trabalho concentra-se em três áreas principais: origens de tráfego malicioso, classificação de segurança de redes vizinhas ao ISP e políticas de intervenção. Foram colectados dados de rede considerando, determinados tipos de tráfego malicioso: varrimento de endereços e inundação de fluxos de ligações; assim como informação de acessibilidades rede: mensagens de actualização de BGP disponibilizadas pelo RIPE Routing Information Service. Analisámos o tráfego malicioso em busca de padrões de rede, o que nos permitiu compreender que é maioritariamente originário de um subconjunto muito pequeno de ASes na Internet. No âmbito de um ISP e de acordo com um conjunto de métricas de segurança, definimos uma expressão de correlação para quantificar os riscos de segurança associados a conexões com redes vizinhas, a qual denominámos Risk Score. Finalmente, propusemos técnicas para concretização das tarefas de rede necessárias à redução de tráfego malicioso de forma eficiente, se possível em cooperação com redes vizinhas / ASes. Não temos conhecimento de qualquer publicação existente que correlacione as características de tráfego malicioso de varrimento de endereços e inundação de fluxos de ligações, com informação de acessibilidades de rede no âmbito de um ISP, de forma a classificar a segurança das vizinhanças de rede, com o propósito de decidir filtrar o tráfego de prefixos específicos de um AS ou bloquear todo o tráfego proveniente de um AS. Acreditamos que os resultados apresentados neste trabalho podem ser aplicados imediatamente em cenários reais, permitindo criar ambientes de rede mais seguros e escaláveis, desta forma melhorando as condições de rede necessárias ao desenvolvimento de novos serviços.This thesis addresses a common issue to many of current Internet Service Providers (ISPs): efficient mitigation of malicious traffic flowing through their network. This unwanted traffic imposes a waste of network resources, leading to a degradation of quality of service. It also creates an unsafe environment for users, therefore mining the Internet potential and opening way for severe criminal activity. Some of the main constraints of creating systems that may tackle these problems are the enormous amount of traffic to be analyzed, the fact that the Internet is inherently untraceable and the lack of incentive for transit networks to block this type of traffic. Under the scope of a mid scale ISP, this thesis focuses on three main areas: the origins of malicious traffic, security classification of ISP neighbors and intervention policies. We collected network data from particular types of malicious traffic: address scans and flow floods; and network reachability information: BGP update messages from RIPE Routing Information Service (RIS). We analyzed the malicious traffic looking for network patterns, which allowed us to understand that most of it originates from a very small subset of Internet ASes. We defined a correlation expression to quantify the security risks of neighbor connections within an ISP scope according to a set of security metrics that we named Risk Score. We finally proposed techniques to implement the network tasks required to mitigate malicious traffic efficiently, if possible in cooperation with other neighbors/ASes. We are not aware of any work been done that correlates the malicious traffic characteristics of address scans and flow flood attacks, with network reachability information of an ISP network, to classify the security of neighbor connections in order to decide to filter traffic from specific prefixes of an AS, or to block all traffic from an AS. It is our belief, the findings presented in this thesis can be immediately applied to real world scenarios, enabling more secure and scalable network environments, therefore opening way for better deployment environments of new services

SIIMCO: A forensic investigation tool for identifying the influential members of a criminal organization

Members of a criminal organization, who hold central positions in the organization, are usually targeted by criminal investigators for removal or surveillance. This is because they play key and influential roles by acting as commanders, who issue instructions or serve as gatekeepers. Removing these central members (i.e., influential members) is most likely to disrupt the organization and put it out of business. Most often, criminal investigators are even more interested in knowing the portion of these influential members, who are the immediate leaders of lower level criminals. These lower level criminals are the ones who usually carry out the criminal works; therefore, they are easier to identify. The ultimate goal of investigators is to identify the immediate leaders of these lower level criminals in order to disrupt future crimes. We propose, in this paper, a forensic analysis system called SIIMCO that can identify the influential members of a criminal organization. Given a list of lower level criminals in a criminal organization, SIIMCO can also identify the immediate leaders of these criminals. SIIMCO first constructs a network representing a criminal organization from either mobile communication data that belongs to the organization or crime incident reports. It adopts the concept space approach to automatically construct a network from crime incident reports. In such a network, a vertex represents an individual criminal, and a link represents the relationship between two criminals. SIIMCO employs formulas that quantify the degree of influence/importance of each vertex in the network relative to all other vertices. We present these formulas through a series of refinements. All the formulas incorporate novelweighting schemes for the edges of networks. We evaluated the quality of SIIMCO by comparing it experimentally with two other systems. Results showed marked improvement

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users in a graph, while graph de-anonymization aims to re-identify users. The effectiveness of anonymization and de-anonymization algorithms is usually evaluated with privacy metrics. However, it is unclear how strong existing privacy metrics are when they are used in graph privacy. In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether metrics use a consistent value range across scenarios. Our extensive experiments indicate that no single metric fulfills all three criteria perfectly. We therefore use methods from multi-criteria decision analysis to aggregate multiple metrics in a metrics suite, and we show that these metrics suites improve monotonicity compared to the best individual metric. This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system