Biblio

The 6G wireless communication networks are being studied to build a powerful networking system with global coverage, enhanced spectral/energy/cost efficiency, better intelligent level and security. This paper presents a four-in-one networking paradigm named 3CL-Net that would broaden and strengthen the capabilities of current networking by introducing ubiquitous computing, caching, and intelligence over the communication connection to build 6G-required capabilities. To evaluate the practicability of 3CL-Net, this paper designs a platform based on the 3CL-Net architecture. The platform adopts leader-followers structure that could support all functions of 3CL-Net, but separate missions of 3CL-Net into two parts. Moreover, this paper has implemented part of functions as a prototype, on which some experiments are carried out. The results demonstrate that 3CL-Net is potential to be a practical and effective network paradigm to meet future requirements, meanwhile, 3CL-Net could motivate designs of related platforms as well.

This paper studies the technology of generating DDPG (deep deterministic policy gradient) by using the deep dual network and experience pool network structure, and puts forward the sampling strategy gradient algorithm to randomly select actions according to the learned strategies (action distribution) in the continuous action space, based on the dispatching control system of the power dispatching control center of a super city power grid, According to the actual characteristics and operation needs of urban power grid, The developed refined artificial intelligence on-line security analysis and emergency response plan intelligent generation function realize the emergency response auxiliary decision-making intelligent generation function. According to the hidden danger of overload and overload found in the online safety analysis, the relevant load lines of the equipment are searched automatically. Through the topology automatic analysis, the load transfer mode is searched to eliminate or reduce the overload or overload of the equipment. For a variety of load transfer modes, the evaluation index of the scheme is established, and the optimal load transfer mode is intelligently selected. Based on the D5000 system of Metropolitan power grid, a multi-objective and multi resource coordinated security risk decision-making assistant system is implemented, which provides integrated security early warning and decision support for the main network and distribution network of city power grid. The intelligent level of power grid dispatching management and dispatching operation is improved. The state reality network can analyze the joint state observations from the action reality network, and the state estimation network uses the actor action as the input. In the continuous action space task, DDPG is better than dqn and its convergence speed is faster.

Recommender systems, which aim to suggest personalized lists of items for users, have drawn a lot of attention. In fact, many of these state-of-the-art recommender systems have been built on deep neural networks (DNNs). Recent studies have shown that these deep neural networks are vulnerable to attacks, such as data poisoning, which generate fake users to promote a selected set of items. Correspondingly, effective defense strategies have been developed to detect these generated users with fake profiles. Thus, new strategies of creating more ‘realistic’ user profiles to promote a set of items should be investigated to further understand the vulnerability of DNNs based recommender systems. In this work, we present a novel framework CopyAttack. It is a reinforcement learning based black-box attacking method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items. CopyAttack is constructed to both efficiently and effectively learn policy gradient networks that first select, then further refine/craft user profiles from the source domain, and ultimately copy them into the target domain. CopyAttack’s goal is to maximize the hit ratio of the targeted items in the Top-k recommendation list of the users in the target domain. We conducted experiments on two real-world datasets and empirically verified the effectiveness of the proposed framework. The implementation of CopyAttack is available at https://github.com/wenqifan03/CopyAttack.

Android malware family classification is an advanced task in Android malware analysis, detection and forensics. Existing methods and models have achieved a certain success for Android malware detection, but the accuracy and the efficiency are still not up to the expectation, especially in the context of multiple class classification with imbalanced training data. To address those challenges, we propose an Android malware family classification model by analyzing the code's specific semantic information based on sensitive opcode sequence. In this work, we construct a sensitive semantic feature-sensitive opcode sequence using opcodes, sensitive APIs, STRs and actions, and propose to analyze the code's specific semantic information, generate a semantic related vector for Android malware family classification based on this feature. Besides, aiming at the families with minority, we adopt an oversampling technique based on the sensitive opcode sequence. Finally, we evaluate our method on Drebin dataset, and select the top 40 malware families for experiments. The experimental results show that the Total Accuracy and Average AUC (Area Under Curve, AUC) reach 99.50% and 98.86% with 45. 17s per Android malware, and even if the number of malware families increases, these results remain good.