Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200

Distributed Denial of Service Attack Detection

Distributed Denial of Service (DDoS) attacks on web applications has been a persistent threat. Successful attacks can lead to inaccessible service to legitimate users in time and loss of business reputation. Most research effort on DDoS focused on network layer attacks. Existing approaches on application layer DDoS attack mitigation have limitations such as the lack of detection ability for low rate DDoS and not being able to detect attacks targeting resource files. In this work, we propose DDoS attack detection using concepts from information retrieval and machine learning. We include two popular concepts from information retrieval: Term Frequency (TF)-Inverse Document Frequency (IDF) and Latent Semantic Indexing (LSI). We analyzed web server log data generated in a distributed environment. Our evaluation results indicate that while all the approaches can detect various ranges of attacks, information retrieval approaches can identify attacks ongoing in a given session. All the approaches can detect three well known application level DDoS attacks (trivial, intermediate, advanced). Further, these approaches can enable an administrator identifying new pattern of DDoS attacks

Detection of network anomalies and novel attacks in the internet via statistical network traffic separation and normality prediction

With the advent and the explosive growth of the global Internet and the electronic commerce environment, adaptive/automatic network and service anomaly detection is fast gaining critical research and practical importance. If the next generation of network technology is to operate beyond the levels of current networks, it will require a set of well-designed tools for its management that will provide the capability of dynamically and reliably identifying network anomalies. Early detection of network anomalies and performance degradations is a key to rapid fault recovery and robust networking, and has been receiving increasing attention lately. In this dissertation we present a network anomaly detection methodology, which relies on the analysis of network traffic and the characterization of the dynamic statistical properties of traffic normality, in order to accurately and timely detect network anomalies. Anomaly detection is based on the concept that perturbations of normal behavior suggest the presence of anomalies, faults, attacks etc. This methodology can be uniformly applied in order to detect network attacks, especially in cases where novel attacks are present and the nature of the intrusion is unknown. Specifically, in order to provide an accurate identification of the normal network traffic behavior, we first develop an anomaly-tolerant non-stationary traffic prediction technique, which is capable of removing both pulse and continuous anomalies. Furthermore we introduce and design dynamic thresholds, and based on them we define adaptive anomaly violation conditions, as a combined function of both the magnitude and duration of the traffic deviations. Numerical results are presented that demonstrate the operational effectiveness and efficiency of the proposed approach, under different anomaly traffic scenarios and attacks, such as mail-bombing and UDP flooding attacks. In order to improve the prediction accuracy of the statistical network traffic normality, especially in cases where high burstiness is present, we propose, study and analyze a new network traffic prediction methodology, based on the frequency domain traffic analysis and filtering, with the objective_of enhancing the network anomaly detection capabilities. Our approach is based on the observation that the various network traffic components, are better identified, represented and isolated in the frequency domain. As a result, the traffic can be effectively separated into a baseline component, that includes most of the low frequency traffic and presents low burstiness, and the short-term traffic that includes the most dynamic part. The baseline traffic is a mean non-stationary periodic time series, and the Extended Resource-Allocating Network (BRAN) methodology is used for its accurate prediction. The short-term traffic is shown to be a time-dependent series, and the Autoregressive Moving Average (ARMA) model is proposed to be used for the accurate prediction of this component. Furthermore, it is demonstrated that the proposed enhanced traffic prediction strategy can be combined with the use of dynamic thresholds and adaptive anomaly violation conditions, in order to improve the network anomaly detection effectiveness. The performance evaluation of the proposed overall strategy, in terms of the achievable network traffic prediction accuracy and anomaly detection capability, and the corresponding numerical results demonstrate and quantify the significant improvements that can be achieved

Fake View Analytics in Online Video Services

Online video-on-demand(VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect the fake views? Can we detect them (and stop them) using online algorithms as they occur? What is the extent of fake views with current VoD service providers? These are the questions we study in the paper. We develop some algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Real time DDoS detection using fuzzy estimators

We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window. © 2012 Elsevier Ltd. All rights reserved

On the Detection of Cyber-Attacks in the Communication Network of IEC 61850 Electrical Substations

The availability of the data within the network communication remains one of the most critical requirement when compared to integrity and confidentiality. Several threats such as Denial of Service (DoS) or flooding attacks caused by Generic Object Oriented Substation Event (GOOSE) poisoning attacks, for instance, might hinder the availability of the communication within IEC 61850 substations. To tackle such threats, a novel method for the Early Detection of Attacks for the GOOSE Network Traffic (EDA4GNeT) is developed in the present work. Few of previously available intrusion detection systems take into account the specific features of IEC 61850 substations and offer a good trade-off between the detection performance and the detection time. Moreover, to the best of our knowledge, none of the existing works proposes an early anomaly detection method of GOOSE attacks in the network traffic of IEC 61850 substations that account for the specific characteristics of the network data in electrical substations. The EDA4GNeT method considers the dynamic behavior of network traffic in electrical substations. The mathematical modeling of the GOOSE network traffic first enables the development of the proposed method for anomaly detection. In addition, the developed model can also support the management of the network architecture in IEC 61850 substations based on appropriate performance studies. To test the novel anomaly detection method and compare the obtained results with available techniques, two use cases are used

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real