{'fraction_non_alphanumeric': 0.012948793407886992, 'fraction_numerical': 0.0011771630370806356, 'mean_word_length': 5.093189964157706, '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}}
A perfect QoS routing algorithm for finding the best path for dynamic networks

It is highly desirable to protect data traffic from unexpected changes as well as provide effective network utilization in the internetworking era. In this paper the QoS management issue that utilizing the active network technology is discussed. Such algorithm is based on the proposed work presented in [15]. Active networks seem to be particularly useful in the context of QoS support. The Active QoS Routing (AQR) algorithm which is based on On-demand routing is implemented incorporating the product of available bit rate and delay for finding the best path for dynamic networks using the active network test bed ANTS. It is inferred that with background traffic, the AQR finds alternative paths very quickly and the delay and subsequently the jitter involved are reduced significantly. In this paper the variant of AQR implemented is demonstrated to be more useful in reducing the jitter when the overall traffic in the network is heavy and has useful application in finding effective QoS routing in ad-hoc networks as well as defending DDoS attacks by identifying the attack traffic path using QoS regulations. The main achievement of this paper is the fast attack detection algorithm. Such algorithm based on performing cross correlation in the frequency domain between data traffic and the input weights of fast time delay neural networks (FTDNNs). It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.