Although there exist very accurate hardware systems for measuring traffic on the internet, their widespread use for analysis tasks is limited by their high cost. On the other hand, less expensive, software-based systems exist that are widely available and can be used to perform a number of simple analysis tasks. The caveat with using such software systems is that application of standard analysis methods cannot proceed blindly because inherent distortions exist in the measurements obtained from software systems. The goal of this paper is to analyze common Internet measurement systems to discover the effect of these distortions on common analysis tasks. Then by selecting one specific task, periodic signal detection, a more in-depth an alysis is conducted which derives a signal representation to capture the salient features of the measurement and develops a periodic detection mechanism designed for the measurement system which outperforms an existing detection method not optimized for the measurement system. Finally, through experiments the importance of understanding the relationship between the input traffic, measurement system configuration and detection method performance is em phasized.
Prasad looked at how interrupt coalescence altered standard network tomography methods, and discussed some methods to detect the presence of coalescence in measured signals in @cite_8 . Our work is different because we do not simply show the effect of the measurement system, but instead we use our knowledge of the measurement system and its inherent effect to design strategies for a specific networking problem, namely DOS attack detection. We chose DOS attack detection because recent work @cite_17 @cite_19 has shown malware and specifically DOS attacks can produce periodic traffic. Thus DOS attack detection becomes a problem of periodic signal detection, which is a common signal processing task. For comparison we use an attack detection method, called the periodic attack detector (PAD), proposed by He in @cite_10 , which is was shown to produce good detection performance in @cite_13 . The PAD is different from our method because it is not designed for a specific measurement system.
@cite_8 @cite_10 @cite_19 @cite_13 @cite_17
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Launching a denial of service (DoS) attack is trivial, but detection and response is a painfully slow and often a manual process. Automatic classification of attacks as single- or multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, and novel techniques such as transient ramp-up behavior and spectral analysis. Although headers are easily forged, we show that characteristics of attack ramp-up and attack spectrum are more difficult to spoof. To evaluate our framework we monitored access links of a regional ISP detecting 80 live attacks. Header analysis identified the number of attackers in 67 attacks, while the remaining 13 attacks were classified based on ramp-up and spectral analysis. We validate our results through monitoring at a second site, controlled experiments, and simulation. We use experiments and simulation to understand the underlying reasons for the characteristics observed. In addition to helping understand attack dynamics, classification mechanisms such as ours are important for the development of realistic models of DoS traffic, can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet. This paper develops two parametric methods to detect low-rate denial-of-service attacks and other similar near-periodic traffic, without the need for flow separation. The first method, the periodic attack detector, is based on a previous approach that exploits the near-periodic nature of attack traffic in aggregate traffic by modeling the peak frequency in the traffic spectrum. The new method adopts simple statistical models for attack and background traffic in the time-domain. Both approaches use sequential probability ratio tests (SPRTs), allowing control over false alarm rate while examining the trade-off between detection time and attack strength. We evaluate these methods with real and synthetic traces, observing that the new Poisson- based scheme uniformly detects attacks more rapidly, often in less than 200 ms, and with lower complexity than the periodic attack detector. Current entropy-based detection methods provide an equivalent time to detection but require flow-separation since they utilize source destination IP addresses. We evaluate sensitivity to attack strength (compared to the rate of background traffic) with synthetic traces, finding that the new approach can detect attacks that represent only 10 of the total traffic bitrate in fractions of a second. Denial-of-service (DoS) detection techniques - such as activity profiling, change-point detection, and wavelet-based signal analysis - face the considerable challenge of discriminating network-based flooding attacks from sudden increases in legitimate activity or flash events. This survey of techniques and testing results provides insight into our ability to successfully identify DoS flooding attacks. Although each detector shows promise in limited testing, none completely solve the detection problem. Combining various approaches with experienced network operators most likely produce the best results. Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.