The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Disaster-Resilient Control Plane Design and Mapping in Software-Defined Networks

Communication networks, such as core optical networks, heavily depend on their physical infrastructure, and hence they are vulnerable to man-made disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction (WMD) attacks, as well as to natural disasters. Large-scale disasters may cause huge data loss and connectivity disruption in these networks. As our dependence on network services increases, the need for novel survivability methods to mitigate the effects of disasters on communication networks becomes a major concern. Software-Defined Networking (SDN), by centralizing control logic and separating it from physical equipment, facilitates network programmability and opens up new ways to design disaster-resilient networks. On the other hand, to fully exploit the potential of SDN, along with data-plane survivability, we also need to design the control plane to be resilient enough to survive network failures caused by disasters. Several distributed SDN controller architectures have been proposed to mitigate the risks of overload and failure, but they are optimized for limited faults without addressing the extent of large-scale disaster failures. For disaster resiliency of the control plane, we propose to design it as a virtual network, which can be solved using Virtual Network Mapping techniques. We select appropriate mapping of the controllers over the physical network such that the connectivity among the controllers (controller-to-controller) and between the switches to the controllers (switch-to-controllers) is not compromised by physical infrastructure failures caused by disasters. We formally model this disaster-aware control-plane design and mapping problem, and demonstrate a significant reduction in the disruption of controller-to-controller and switch-to-controller communication channels using our approach.Comment: 6 page

Outsmarting Network Security with SDN Teleportation

Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), introduces a new vulnerability which we call \emph{teleportation}. An attacker (e.g., a malicious switch in the data plane or a host connected to the network) can use teleportation to transmit information via the control plane and bypass critical network functions in the data plane (e.g., a firewall), and to violate security policies as well as logical and even physical separations. This paper characterizes the design space for teleportation attacks theoretically, and then identifies four different teleportation techniques. We demonstrate and discuss how these techniques can be exploited for different attacks (e.g., exfiltrating confidential data at high rates), and also initiate the discussion of possible countermeasures. Generally, and given today's trend toward more intent-based networking, we believe that our findings are relevant beyond the use cases considered in this paper.Comment: Accepted in EuroSP'1

Comparison of Empirical Data from Two Honeynets and a Distributed Honeypot Network

In this paper we present empirical results and speculative analysis based on observations collected over a two month period from studies with two high interaction honeynets, deployed in a corporate and an SME (small to medium enterprise) environment, and a distributed honeypots deployment. All three networks contain a mixture of Windows and Linux hosts. We detail the architecture of the deployment and results of comparing the observations from the three environments. We analyze in detail the times between attacks on different hosts, operating systems, networks or geographical location. Even though results from honeynet deployments are reported often in the literature, this paper provides novel results analyzing traffic from three different types of networks and some initial exploratory models. This research aims to contribute to endeavours in the wider security research community to build methods, grounded on strong empirical work, for assessment of the robustness of computer-based systems in hostile environments

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

Botnet Detection using Social Graph Analysis

Signature-based botnet detection methods identify botnets by recognizing Command and Control (C\&C) traffic and can be ineffective for botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel botnet detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world botnet traffic and compare its performance with other community detection methods. The results show that our approach works effectively and the refined modularity measure improves the detection accuracy.Comment: 7 pages. Allerton Conferenc

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201