| Title | Exponential Smoothing based Approach for Detection of Blackhole Attacks in IoT |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Sahay, Rashmi, Geethakumari, G., Mitra, Barsha, Thejas, V. |
| Conference Name | 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) |
| Date Published | dec |
| Keywords | 6LoWPAN, Blackhole attack, blackhole attack detection approach, blackhole attacks, exponential smoothing, exponential smoothing time series data, exponential window function, Forecasting, high performance computing environment, Internet, Internet of Things, Internet of things environment, IoT, IoT environment, IP networks, IPv6 over low power personal area network, IPv6 routing protocol over LLN, low power and lossy network, personal area networks, pubcrawl, Resiliency, RFID, Routing, Routing protocols, RPL, Scalability, Sensors, sink node, smoothing methods, telecommunication network topology, time series, topological isolation |
| Abstract | Low power and lossy network (LLN) comprising of constrained devices like sensors and RFIDs, is a major component in the Internet of Things (IoT) environment as these devices provide global connectivity to physical devices or "Things". LLNs are tied to the Internet or any High Performance Computing environment via an adaptation layer called 6LoWPAN (IPv6 over Low power Personal Area Network). The routing protocol used by 6LoWPAN is RPL (IPv6 Routing Protocol over LLN). Like many other routing protocols, RPL is susceptible to blackhole attacks which cause topological isolation for a subset of nodes in the LLN. A malicious node instigating the blackhole attack drops received packets from nodes in its subtree which it is supposed to forward. Thus, the malicious node successfully isolates nodes in its subtree from the rest of the network. In this paper, we propose an algorithm based on the concept of exponential smoothing to detect the topological isolation of nodes due to blackhole attack. Exponential smoothing is a technique for smoothing time series data using the exponential window function and is used for short, medium and long term forecasting. In our proposed algorithm, exponential smoothing is used to estimate the next arrival time of packets at the sink node from every other node in the LLN. Using this estimation, the algorithm is designed to identify the malicious nodes instigating blackhole attack in real time. |
| DOI | 10.1109/ANTS.2018.8710073 |
| Citation Key | sahay_exponential_2018 |
Exponential Smoothing based Approach for Detection of Blackhole Attacks in IoT