A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

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

Role based behavior analysis

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso são trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de Informacão/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nível da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. Além disso, se não houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiências noutras. Finalmente, incentivar a proactividade não implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese é desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo é iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com características semelhantes são agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais são comprados com os perfis de grupo, e os que diferem significativamente são marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda métodos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisão comportamento semelhante. Além disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos são assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it

Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data

The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.Comment: Extended version of a publication in the 2018 IEEE International Conference on Data Mining Workshops (ICDMW

Distributed Network Anomaly Detection on an Event Processing Framework

Network Intrusion Detection Systems (NIDS) are an integral part of modern data centres to ensure high availability and compliance with Service Level Agreements (SLAs). Currently, NIDS are deployed on high-performance, high-cost middleboxes that are responsible for monitoring a limited section of the network. The fast increasing size and aggregate throughput of modern data centre networks have come to challenge the current approach to anomaly detection to satisfy the fast growing compute demand. In this paper, we propose a novel approach to distributed intrusion detection systems based on the architecture of recently proposed event processing frameworks. We have designed and implemented a prototype system using Apache Storm to show the benefits of the proposed approach as well as the architectural differences with traditional systems. Our system distributes modules across the available devices within the network fabric and uses a centralised controller for orchestration, management and correlation. Following the Software Defined Networking (SDN) paradigm, the controller maintains a complete view of the network but distributes the processing logic for quick event processing while performing complex event correlation centrally. We have evaluated the proposed system using publicly available data centre traces and demonstrated that the system can scale with the network topology while providing high performance and minimal impact on packet latency