Fast Internet-wide network measurement plays an important role in cybersecurity analysis and network asset detection. The vast address space of IPv6, however, makes it infeasible to apply a brute-force approach for scanning the entire network. Even worse, the extremely uneven distribution of IPv6 active addresses results in a low hit rate for active scanning. To address the problem, we propose 6Hit, a reinforcement learning-based target generation method for active address discovery in the IPv6 address space. It first divides the IPv6 address space into different regions according to the structural information of a set of known seed addresses. Then, it allocates exploration resources according to the reward of the scanning on each region. Based on the evaluative feedback from existing scanning results, 6Hit optimizes the subsequent search direction to regions that have a higher density of activity addresses. Compared with other state-of-the-art target generation methods, 6Hit achieves better performance on hit rate. Our experiments over real-world networks show that 6Hit achieves 3.5% - 11.5% hit rate for the eight candidate datasets, which is 7.7% - 630% improvement over the state-of-the-art methods.

Recently, smart grid is considered as the next generation of power grid by introducing information and communication technologies. Line-loss is an important synthetic indicator which can directly reflect the energy efficiency and power management level of smart grid enterprises. In order to obtain all residential areas, line-loss requires obtaining electricity consumption of each user. However, data about users’ electricity consumption could reveal sensitive information; a sophisticated adversary can use some data analysis methods to deduce economic situation, habits, lifestyles, etc. In order to solve the problem, we propose an Efficient Privacy-preserving scheme for Line-loss Calculation, named EPLC. In our scheme, a data item is reading from one smart meter which implies the energy consumption in a time period of the user who owns it, and each user lives in a residential area. For each user, we encrypt user’s data based on Paillier cryptosystem by using two Horner parameters, by leveraging homomorphism, and each residential area gateway calculates relevant data about corresponding line-loss and control center hides the area-level polynomial into the final output for representing line-loss of all residential areas which are both in the form of ciphertext. Finally, we can still recover each residential area line-loss with possessing private keys and Horner parameters. Moreover, EPLC adopts the batch verification technique to lower authentication cost. Finally, our analysis indicates that EPLC is not only efficient but also can protect individual user’s electricity consumption privacy, and the flexibility and expansibility of EPLC are very suitable for smart grid.