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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 yber attack detection. 2d Barcode In VB.NET Using Barcode printer for .NET .NET Control to generate, create Data Matrix ECC200 image in VS .NET applications. div>.Related: Print QR Code .NET Image, Print QR Code Excel Image, Create QR Code Word



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shows the ROC charts of the Markov chain model based anomaly detection technique for the Pascal data set with the window sizes of 10 events and 100 events Regardless of the window size, the Markov chain model-based anomaly detection technique produces a good performance for both Mill and Pascal data sets with all the four ROC curves in Figures 151 and 152 close to the top-left corner of the ROC chart representing the 100% hit rate and the 0% false alarm rate In the Mill and Pascal testing data sets, normal use sessions and attack sessions are separate In [3], the Markov chain model-based anomaly detection technique is tested on four data sets with various degrees in which attack events and normal use events are mixed The testing results show that the detection performance of the Markov chain model-based anomaly detection technique is highly sensitive to the degree of mixing attack events and normal use events The detection performance of the Markov chain model-based anomaly detection technique drops to hat of a random decision-maker that decides each event as attack or normal use by random, when the attack events and the normal use events are randomly mixed but maintain the sequential order of the attack events and the sequential order of the normal use events This. 128 Code Set C Generation In VS .NET Using . In Visual Basic .NET Using Barcode printer for .NET .Related: VB.NET Interleaved 2 of 5 Generation , .NET UPC-E Generating , Codabar Generator .NET

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in exactly the same order, before and after the elimination; hence, the execution time cannot change The only bene t of rescheduling is a better estimation of the execution time with a more accurate schedule length Note the difference from Theorem 51 for classic scheduling, which states that the rescheduling of any schedule with a list scheduling algorithm results in a schedule at least as short as the original schedule There, it suf ces to schedule the nodes in their start time order of the original schedule Under the involvement contention model, the edge order on the links (Sections 741 and 751) as well as on the processors (Section 821, see also later) is relevant for the schedule length Generally, it is therefore necessary to repeat the scheduling with the same heuristic employed for the original schedule to ensure the exact same scheduling order of the nodes and edges Otherwise, the schedule length might increase Inserting into Gaps The insertion technique can be applied to use the emerging gaps during the scheduling This has the advantage that scheduling decisions might be different when a node or an edge can be inserted into a gap and therefore start earlier Note that use of the insertion technique in a straightforward manner is only possible because the causality Condition 81 for involvement scheduling gives the freedom of the node and edge order (Section 821) Inserting a node or an edge into a gap is very likely to separate edges from their origin or destination nodes, as, for example, in Figure 88 Scheduling Order of Edges As in contention scheduling (Sections 741 and 751), the order in which the edges are scheduled is relevant and might lead to different schedule lengths Here, the scheduling of the edges is divided into the scheduling of the entering edges on the links and the destination processors and the leaving edges provisionally on the source processors Scheduling the entering edges is very similar to the scheduling under the contention model and the same considerations apply to their order (Sections 741 and 751) A natural order of the leaving edges is the scheduling order of their destination nodes, as it follows the same rational as the minimization of the node s start and nish times (Section 51) Of course, the ordering of the edges slightly increases the complexity of a scheduling algorithm It is very important for most algorithms that the scheduling order of the edges is deterministic (Section 751) In Section 841, a list scheduling algorithm will be introduced, employing the technique of reserving time intervals for the leaving edges With list scheduling, utilization of the end or insertion technique is no roblem at all.

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