{'fraction_non_alphanumeric': 0.018556701030927835, 'fraction_numerical': 0.0, 'mean_word_length': 5.966507177033493, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
NISA: Node Identification and Spoofing Attack Detection Based on Clock Features and Radio Information for Wireless Sensor Networks

Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensor networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broadcasting. To defend against Spoofing attacks, we propose a new node identification scheme called node identification against Spoofing attack (NISA). It utilizes the reverse time synchronization framework, where sensor nodes’ clock skews are estimated at the head of a WSN, and the spatially-correlated radio link information to achieve simultaneous node identification and attack detection. We further provide centralized and distributed NISA for covering both single-hop and multi-hop scenarios, the former of which employs a single-input and multiple-output convolutional neural network. With a real WSN testbed consisting of TelosB sensor nodes running TinyOS, we investigate the identifiability of clock skews under temperature and voltage variations and evaluate the performance of both centralized and distributed NISA. Experimental results demonstrate that both centralized and distributed NISA could provide accurate node identification and Spoofing attack detection.