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to the lack of handling the mixed attack and normal use data and signature patterns through subtle data features
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REFERENCES
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1 R J Schalkoff, Arti cial Neural Networks New York: McGraw-Hill, 1997 2 Statistica, wwwstatsoftcom
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Cyber Attack Detection: Anomaly Detection
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Anomaly detection is one of the two conventional methodologies for cyber attack detection The anomaly detection methodology takes two steps First, a norm pro le is de ned to represent normal use behavior for a computer or network subject of interest Then the norm pro le is used to detect the presence of an anomaly, which is a large deviation from the norm pro le and is linked to a possible attack Many existing anomaly detection techniques differ mainly in their ways of representing the norm pro le and detecting anomalies accordingly This part presents two norm pro ling techniques and associated anomaly detection methods 14 describes both univariate and multivariate statistical anomaly detection techniques based on the statistical modeling of the norm pro le and the detection of statistical anomalies 15 presents a stochastic modeling technique, speci cally the Markov chain model, to capture the sequential order feature of an event sequence which is omitted in the statistical data modeling methods in 14 The advantage of the anomaly detection methodology lies in its ability to detect novel cyber attacks if they induce large deviations from the norm pro le However, it should be noted that the anomaly detection methodology cannot detect novel attacks if they do not appear to be deviating largely from the norm pro le The anomaly detection methodology has not gained a wide use in practical intrusion detection systems due to high false alarms typically associated with it This drawback in performance accuracy is attributed to two factors: (1) the lack of power by most anomaly detection techniques in adequately modeling a wide variety of normal use behavior (including irregular but benign behavior) through a single modeling technique, and (2) lack of handling the data mixture of attack activities and normal use activities that occur simultaneously Through the description of the statistical and stochastic anomaly detection techniques in s 14 and 15, the shortcomings of the anomaly detection methodology are illustrated
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Statistical anomaly detection with univariate and multivariate data
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This chapter describes two statistical anomaly detection techniques for cyber attack detection: the EWMA (Exponentially Weighted Moving Average) control chart which is a univariate Statistical Process Control (SPC) technique, and the Chi-Square Distance Monitoring method (CSDM) which is a multivariate SPC technique Many SPC techniques [1 2] have traditionally been developed and applied to monitor the quality of manufacturing processes SPC techniques rst build the statistical model of the process data obtained from an in-control process to contain only random variations of the process data SPC considers the process out of control with an assignable cause other than random causes of data variations if the process data shows a statistically signi cant deviation from the statistical in-control data model Consider that a computer and network system is in control if there are only normal use activities, but is out-of-control if there are also attack activities This makes the anomaly detection methodology for cyber attack detection similar to SPC techniques [3] in rst building the norm or in-control pro le of the process data and then using the norm pro le to detect a large deviation as anomaly or out-ofcontrol caused by an attack This chapter presents the application of a univariate SPC technique, the EWMA control chart, and a multivariate SPC technique, CSDM, to cyber attack detection
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141 EWMA CONTROL CHARTS
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Many of existing univariate SPC techniques, such as Shewhart control charts and Cumulative Sum (CUSUM) control charts, assume that the in-control process data follows a normal probability distribution EWMA control charts have been shown to be robust to nonnormality and produce the robust performance for both normally and nonnormally distributed data [1, 2, 4] Not all data variables from computer and network systems have a normal distribution For example, many data variables from Windows performance objects have a skewed probability distribution or a multimodal distribution, rather than a normal distribution, as described in 9 Hence, EWMA control charts have been selected and applied to cyber attack detection
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