6LoWPAN also known as “IPV6 over Low Power Wireless Personal Area Network” is a standard created to make IPv6 Protocol work on even the smallest devices with limited processing capabilities. RPL also known as Routing Protocol for Low power and lossy networks is a network layer routing protocol specifically designed for 6LoPWAN. RPL is subjected to various attacks in which sniffing attack is a type of traffic packet eavesdropping attack. There is a research gap when it comes to detecting sniffing attacks on 6LoWPAN RPL networks or any detailed information about it. So, the aim of this thesis is to detect the sniffing attack in 6LoPWAN RPL networks and find which machine learning algorithm is best in detecting this type of attack. A poorly implemented sniffer can cause latency due to resource constraints which can further cause changes in data traffic pattern. Since sniffing attack can cause unauthorized authentication and further attacks like spoofing attacks, sinkhole attacks and DoS attacks, it is important to detect it. This thesis starts by trying to detect sniffing attack on small scale networks. Instantcontiki3.0 (Cooja Simulator) is used here generate network traffic for both normal scenarios and attack scenarios. The data obtained from the simulation is pre-processed to feed the machine learning algorithms. The features are extracted using chi2 feature selection method. In this thesis the ML algorithms used are Random Forest classifier, Naive Bayes classifier, Support Vector Machine, Logistic regression, K- nearest neighbor and Decision tree. From the analysis of the results, it is concluded that the Random Forest classifier is the best algorithm to detect sniffing attack. The prefix value in RPL options which are carried by ICMPv6 control messages can point the DAG partition the sniffer is located by mapping to a DODAG ID.