Thisabove mentioned algorithm generates the dictionary that can be used to extractthe feature vectors for the long payload features.

Figure 2 shows the block diagramof dictionary construction. Now this dictionary which has been built withBigrams and Trigrams can be used by any feature extraction algorithm moreeffectively even from the long payload features. The generated Bigrams andTrigrams can also be handled by any machine learning algorithms. In the presentwork, experiments were carried with one of the most widely cited machinelearning algorithm, Support Vector Machine (SVM) to study the results out ofthe proposed methodology.   Thefurther step is the feature vector extraction for the long payload featureswhich is outlined in the following figure 3. The approach in 16 has beenfollowed in the present work for the task of feature vector extraction.

Best services for writing your paper according to Trustpilot

Premium Partner
From $18.00 per page
4,8 / 5
4,80
Writers Experience
4,80
Delivery
4,90
Support
4,70
Price
Recommended Service
From $13.90 per page
4,6 / 5
4,70
Writers Experience
4,70
Delivery
4,60
Support
4,60
Price
From $20.00 per page
4,5 / 5
4,80
Writers Experience
4,50
Delivery
4,40
Support
4,10
Price
* All Partners were chosen among 50+ writing services by our Customer Satisfaction Team

Thefeature vector extraction step is also explained in more detail in the belowAlgorithm 2. ______________________________________Algorithm 2:Feature Vector Extraction for Long Payload Features with Bigrams & TrigramsStep-1: Inputthe long payload features and              constructed Bigram / TrigramDictionaryStep-2:Initialize all feature vectors to zeroStep-3: Take onestring at a time  Step-4: Take oneBigram / Trigram  Step-5: Find theindex of Bigram / Trigram            from the dictionaryStep-6:Increment the location counter in the            feature vectorStep-7: Repeattill there is no possibility of            finding Bigram / Trigram from theinput            payload featureStep-8: Finishfeature vector i, and proceed to            feature vector i+1Step-9: Stop theprocess if all there no feature             vector is left to be processedStep-10: Outputthe feature vectors