Intrusion Bit Detection in Data Packet Transmitted Over Covert channels using K-NN Machine Learning Algorithm
Raju Singh Kushwaha · International Journal for Research in Applied Science and Engineering Technology · 2022
bstract: K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning techniques. The algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well good category by using an algorithm. This algorithm can be used for Regression as well as for Classification but mostly it is used for Classification problems. If the encrypted data is transferred over the covert channel, the number of intruders, cryptanalysis attack and want to know data pattern by analysing the data bit pattern. If the intruder inserts any bit inside the data bit transferred to the receiver, then the receiver encryption system detects this bit and neglects this data for the decryption process.