The understanding of password security highly relates to our knowledge of how adversaries guess passwords, and this makes the modeling of guessing attacks a pivotal task. To maximize guessing effectiveness, the adversary generally attempts to guess in descending order of likelihood, akin to the way generative retrieval learning-to-rank works in a recommendation system, which prioritizes information to targeted users based on predicted relevance. In this paper, we propose a password guessing framework based on adversarial ranking, named RankGuess. We regard the password creation process as sequential decision trajectories. In this context, the adversary is assumed to train an agent where the current state is represented by the password sequence generated up to that point. The action taken is to generate the next token, and the evaluation score assigned by the ranker serves as the reward signal received. Consequently, we frame the problem of password guessing as a Markov Decision Process and tackle it using adversarial ranking techniques. Due to the generality of our framework, RankGuess can be applicable to various guessing scenarios (i.e., trawling guessing, targeted password guessing based on personally identifiable information (PII), and conditional password guessing). By employing 12 large-scale password datasets and six PII datasets, we demonstrate that our models are effective: (1) RankGuess surpasses all current state-of-the-art models and outperforms GAN-based methods by 26.29%~43.69% (avg. 34.80%); (2) When the victim's PII at site$A$(namely PIIA) is known, RankGuess-PII for targeted password guessing based on PIIA, which guesses 58.21%~91.95% of common users within 1012guesses, outperforms its foremost counterparts by 6.32%~17.09%; (3) Within 107guesses, our RankGuess-Mask based on victims' partial passwords (e.g., d****1*02*), improves the password cracking success rates by 7.70%~14.85% (avg. 8.21%) compared to its state-of-the-art counterparts. The paper provides a new technical approach to a well-known challenge in the password-guessing field.

In comparison to common quadrotors, the structure deformation of morphing quadrotors endows them with better flight performance but also results in more complex flight dynamics. Generally, it is extremely difficult or impossible for these morphing quadrotors to develop an accurate mathematical model that describes their complex flight dynamics. This fact leads to a particularly challenging situation, as the existing mature model-based flight control theory fails to address the flight control design issue of morphing quadrotors. By resorting to a combination of model-free control techniques [e.g., deep reinforcement learning (DRL)] and convex combination (CC) technique, a convex-combined-DRL (cc-DRL) flight control algorithm is proposed for flight trajectory tracking and attitude stabilization of a class of morphing quadrotors with arm-length deformation. In the proposed cc-DRL flight control algorithm, a proximal policy optimization algorithm is utilized to offline train the corresponding optimal flight control laws for some selected representative arm length modes. Hereby, a cc-DRL flight control scheme is constructed by the CC technique. Finally, simulation results are presented to show the effectiveness and merit of the proposed DRL flight control algorithm.

Network intrusion detection system provides a better network security solution than other traditional network defense technologies. Aiming at the increasingly serious problem of Internet security in the big data environment, a network intrusion detection model based on autoencoder network model and improved genetic algorithm BP (IGA‐BP) network is constructed. In order to reduce the data dimension and eliminate redundant information, the autoencoder network model is firstly used to denoise and dedimension. A new population was formed by selecting some of the best parent individuals for cross mutation and replacing the worst parent individuals. The improved genetic algorithm and new population generation model will provide more reasonable initial parameters for BP network, namely, IGA‐BP network model. Based on IGA‐BP network model, the problems of slow detection rate and easy to get into local optimality in BP network are solved. The experiments were performed on KDD CUP99 dataset, which simulated different types of user organizations and different types of network intrusion. Compared with the existing intrusion detection methods, the experimental results show that the proposed method has a great effect on classification accuracy, false positives, and detection rate.