The quality of hot-rolled steel strip is directly affected by the strip *** machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced *** limitat...
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The quality of hot-rolled steel strip is directly affected by the strip *** machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced *** limitation results in poor production quality and efficiency,leading to increased production ***,a novel strip crown prediction model that uses the Boruta and extremelyrandomizedtrees(Boruta-ERT)algorithms to address this issue was *** improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data *** Boruta-ERT prediction model was then used to select features and predict the strip *** the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 *** level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.
The Low -rate denial of service (LDoS) attack is a variant of denial of service (DoS) attack that exploits low overhead to render target services unreachable for legitimate users. Unlike legacy networks, software -def...
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The Low -rate denial of service (LDoS) attack is a variant of denial of service (DoS) attack that exploits low overhead to render target services unreachable for legitimate users. Unlike legacy networks, software -defined networking (SDN) separates the behavior of controlling and forwarding, providing excellent programmability to achieve real-time defense against LDoS attacks. Leveraging the potential of SDN's programmability, we propose a real-time anomaly defense framework tailored for SDN, named ERT-EDR. This framework is capable of detecting and mitigating TCP-targeted LDoS Attacks and comprises three modules: (1) The Information Collection Module. It periodically samples the traffic statistics for analysis. (2) The LDoS Attack Detection Module. It combines six features of traffic statistics and utilizes the extremelyrandomizedtrees (ERT) algorithm to detect. (3) LDoS Attack Mitigation Module. It locates attacked ports with the Edit Distance on Real sequence (EDR) algorithm and installs flow table entries to filter attack traffic. Extensive experiments are conducted under various background traffic scenarios to validate the effectiveness and scalability of ERT-EDR. The results demonstrate that ERT-EDR can effectively detect TCP-targeted LDoS attacks with 96.4667% accuracy and 96.4977% F 1 score . Furthermore, it accurately identifies the attacked port and successfully mitigates attacks.
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