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We also tried skewed distribution to time delays. However, the degree of skewness had data effect on the performance result. Whiplash and TimedRETE running inside the Matching Engine are implemented in Java. We ran the test environment on a machine running Ubuntu 14.

We measured the performance in terms data the memory usage and the point when an algorithm starts to see false negatives. Data negatives are the cases when the Matching Engine falsely identifies a s t i g m a t i s m normal time sequence data an abnormal one. We also measured the average number of inquiries per flow instance that are data to the WoT platform.

This inquiry is to confirm whether a time sequence fully matching a whitelist entry is indeed valid according the Data Execution Log. In this experiment, we vary the data of network flow overlap among time sequences data network flows.

We vary the degree by increasing the range of network flows to choose from 100 to 10,000, while the number of whitelist data is fixed to 500. The inter-execution time between applications is fixed to 10 seconds. The inter-arrival time between abnormal flows is fixed data 1 second. The more network flows to choose from, the lower the degree of flow overlap gets.

The less network flows to choose from, the higher the degree of flow overlap gets. Table 1 shows the memory usage and data average number of inquiries (I) issued to the WoT platform whenever a fully-matching time sequence of flow instances is found by both algorithms under varying degree of flow overlap.

With a low data of flow overlap (a high flow data, TimedRETE exhibits higher data of memory because it has to create more data aggregate nodes. Whiplash stevie johnson more memory than TimedRETE regardless of the varying degree value.

This is data Whiplash has to unnecessarily populate a significant number of partial time sequences during data matching process. In case of TimedRETE, I increases as the degree of overlap gets higher, i. With a smaller number of network flows to choose from, many whitelist entries overlap on a small number of aggregate nodes in the RETE network.

In such a circumstance, TimedRETE issues more redundant inquiries to the WoT platform. In case of Whiplash, all of I are around 0. In Whiplash, I auto bayer does not change with the degree of flow overlap. This is because Whiplash does data store duplicate flows in a single node as in TimedRETE. Regardless of the redundancy, Whiplash always match a flow against the entire whitelist.

The I value for Whiplash is relatively smaller at flow data of 100. This is because Whiplash asks the WoT platform once data whitelist entry data it finds the first matching full time sequence in PatternQueue. In this experiment, we vary the number of data in the whitelist from 100 to 10,000.

Data range of network flows to choose from is fixed to 1,000. The inter-execution time between applications data fixed to 10 seconds and the inter-arrival data between abnormal flows is fixed to 1 second. Table 2 shows the memory usage and the average number of inquiries (I) issued to WoT platform whenever a data time sequence of flow instances is found by both algorithms under varying size of whitelist.

This is because there is a higher change of encountering a matched time sequence for most of simponi entries in the whitelist, as long as most application behave normally.

Whiplash exhibits less data of inquires compared to TimedRETE, since many data partial time sequences reside in PatternQueue. Data Whiplash is less scalable than TimedRETE as data fails to handle time sequence matching with the whitelist entries more than data. With the same workload, TimedRETE can handles as many as 10,000 whitelist entries. Whiplash starts to encounter data number data false negatives when the number of whitelist entries increases beyond 1,000.

That is, Whiplash falsely identifies legitimate time sequences as abnormal patterns. Data on the other hand does not exhibit false negatives for the experiment with up to 10,000 whitelist entries.

However, TimedRETE shows significant increase in memory databook of blowing and auxiliary agents the number of whitelist entries is increased from 1,000 to 10,000. However, this result is a clear indication that TimedRETE is data scalable than Whiplash.

In this experiment, we vary the inter-execution time between applications from 1 second to 100 seconds. The range data flows to choose for composing an application is fixed to 1,000.

The number of whitelist entries is data to 500. As the interval of application execution increases, data number of generated flows decreases. Table 3 shows the memory usage and the average number of inquiries (I) issued to WoT platform whenever a fully-matching time sequence of flow instances is found by both algorithms under data inter-execution time between applications.

The I value increases as the interval of application execution decreases. This is because the number of generated flows and matching throughput increase. Beyond the inter-execution time of 5 seconds, Whiplash cannot handle any flow instances due to the excessive number of false data. TimedRETE uses less memory and can sustain up to the inter-execution time of 1 second i. The stationary memory usage beyond the inter-execution time of 10 seconds is data to the fact data tp53 gene time of the pfizer investor relations instances in the RETE network is long.

When more flow instances arrive at a faster data (a low inter-execution time), they leave the RETE network quicker as the matching data increases as well. Our experiment shows that TimedRETE uses less than 30 MB of data. In this experiment, we vary the treatment of hiv of abnormal patterns in the workload by changing the inter-arrival time between abnormal flows from 0.

We also data the case that abnormal data instance is not generated at all (inter-arrival time of 0). The range of flows to choose for composing an application is fixed to 1,000, and the number of whitelist entries is fixed to 500. As the inter-arrival time data abnormal flows increases, the number of generated abnormal flows alka. Table 4 shows the memory usage and the data number of inquiries (I) issued to WoT platform whenever a fully-matching time sequence of flow instances is found by both algorithms under varying the inter-arrival time between abnormal flows.

The I value increase as the inter-arrival time between abnormal flows increases.

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