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In frequency-modulated continuous-wave (FMCW) radar interference suppression based on signal reconstruction, the pruned exact linear time (PELT) algorithm is used to detect the time positions of the interference. Due to the uncertain penalty factor of the PELT algorithm, the exactness of the position detection is reduced; thus, the suppression performance is degraded. We propose a PELT algorithm with a known change number (PELT-KCN), where a known change number is used to calculate the optimal penalty factor such that the high accuracy of the algorithm can be guaranteed. After interference recognition, the beat signal is separated into two parts: the undamaged signal and the damaged signal. The former is utilized to restore the latter through an autoregressive (AR) model. In simulations and field experiments, we applied our proposed PELT-KCN algorithm to the interference suppression method and verified its performance. Our method can accurately detect the time positions of interference and effectively improve the signal-to-noise ratio (SNR) of the detected targets.INDEX TERMS Change-point detection, frequency modulated continuous wave (FMCW) radar, mutual interference suppression, signal restoration.

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Application-layer distributed denial of service (AL-DDoS) attacks are becoming critical threats to websites because the stealth of AL-DDoS attacks makes many intrusion prevention systems ineffective. To detect AL-DDoS attacks aimed at websites, we propose a novel statistical model called the RM (rhythm matrix). Although the original features from the network layer are adopted, the access trajectory, including requested objects and corresponding dwell-time values, can be abstracted and accumulated into an RM. With an RM, we can almost losslessly compress complex features into a simple structure and characterize the user access behavior. We detect AL-DDoS attacks according to the increase of the abnormality degree in the RM and further identify malicious hosts based on change-rate outliers. In the experiments, we simulate three modes of AL-DDoS attacks with the latest popular DDoS attack tools: LOIC and HOIC. The results show that our method can detect these simulated attacks and identify the malicious hosts accurately and efficiently. For an AL-DDoS detection method, the ability to distinguish flash crowds is indispensable. We also demonstrate the excellent performance of our approach in distinguishing flash crowds from AL-DDoS attacks with two reconstructed public datasets.

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