As the prevalence of high-speed networks with augmented transmission capacities expands, the security technologies devised to shield these networks lag behind the rapid advancements. This disparity leads to many issues, with Distributed Denial of Service (DDoS) attacks representing the most harmful. In such attacks, hackers exploit numerous bots to flood the target with a torrent of vicious traffic or spurious requests, leading to the paralysis of the communication framework and the interruption of essential services. Existing defense mechanisms against DDoS attacks struggle to reconcile detection speed, lead time, and accuracy, revealing several deficiencies. Our method begins by employing a sketch-based algorithm characterized by minimal computational demands paired with considerable swiftness. This strategy significantly mitigates the intensity of DDoS attacks and reduces the imbalance rate of benign traffic and attacks. Subsequently, the deployment of the CatBoost classifier augments the detection's F1-score to$9 9. 6 4 \%$. The experimental findings corroborate the efficacy of DDoS-Dam in the expeditious detection and mitigation of DDoS attacks in high-speed networks.

Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between quality and diversity limits the performance of these methods. We introduce the Phasic Diversity Optimization (PDO) algorithm, a Population-Based Training framework that separates reward and diversity training into distinct phases instead of optimizing a multi-objective function. In the auxiliary phase, agents with poor performance diversified via determinants will not replace the better agents in the archive. The decoupling of reward and diversity allows us to use an aggressive diversity optimization in the auxiliary phase without performance degradation. Furthermore, we construct a dogfight scenario for aerial agents to demonstrate the practicality of the PDO algorithm. We introduce two implementations of PDO archive and conduct tests in the newly proposed adversarial dogfight and MuJoCo simulations. The results show that our proposed algorithm achieves better performance than baselines.

In recent years, semi-supervised clustering ensembles have gained significant attention in the field of clustering. These ensembles leverage limited prior knowledge to guide the clustering process. Nonetheless, throughout the integration process, owing to the employment of diverse fundamental clustering algorithms, disparate algorithms generate incongruent results. Consequently, ascertain a more reliable conclusion becomes a formidable challenge. In this paper, we introduce D-CoA (Density based Confidence of Assignment), a novel semi-supervised clustering ensemble algorithm, designed to navigate the intricacies of this issue. D-CoA incorporates sub-clusters using the Minimum Spanning Tree algorithm and evaluates the confidence of each amalgamated cluster to establish prior confidence. Subsequently, a Mixture Density Network is leveraged to calculate the probability density distribution for each merged cluster, yielding posterior confidence scores for resolving conflicts in sample assignments. By adopting this methodology, we amplify the reliability of conflicting sample assignments. Empirical results demonstrate the versatility of this approach, as it effectively accommodates probability distributions of varying shapes, thus demonstrating its feasibility and scalability for diverse data types.

Currently, feature selection faces a huge challenge that no single feature selection method can effectively deal with various data sets for all real cases. Ensemble learning is a potential promising solution to address this problem. We propose an ensemble feature selection method based on enhanced co-association matrix (ECM-EFS). Positive-co-association matrix (PCM), negative-co-association matrix (NCM), and relative-co-association matrix (RCM) are first introduced to discover the relationship among features by ensembling the results in multiple feature selection methods. To further produce a more stable feature selection result, “Feature Kernel” is also introduced and used as a starting point for feature selection. Comparative experiments with four state-of-the-art methods have confirmed that the ECM-EFS can provide more robust results. Moreover, compared with traditional ensemble feature selection methods, our method can compensate information loss and reduce computational cost significantly.