Recently, machine learning techniques, especially supervised learning techniques, have been adopted in the Intrusion Detection System (IDS). Due to the limit of supervised learning, most state-of-the-art IDSs do not perform well on unknown attacks and incur high computational overhead in the Internet of Things (IoT). To overcome these challenges, we propose a novel IDS based on unsupervised techniques, namely, UTEN-IDS. UTEN-IDS uses the ensemble of autoencoders to handle the network data and performs the anomaly detection by an Isolation Forest algorithm. The effectiveness of the proposed method is verified using two benchmark datasets. The results show that our approach has significant advantages in classification performance and proves its utility in the IoT network when compared to other approaches.

Stealth address is a known technique to ensure the privacy (anonymity) of a recipient participating in a certain transaction in a distributed blockchain scenario. However, most existing stealth address schemes require linear judge time and search time $\mathcal{O}(n)$, where $n$ is the number of transactions of a certain block, so the only way to claim transactions for a recipient is to traverse the transaction list to find out whether an ever-arrived transaction belongs to him. To overcome this drawback, we proposed the notion of Fast Stealth Address (FSA), a novel approach that simultaneously preserves privacy and improves search efficiency of recipients. We give a generic construction of FSA scheme under subgroup membership assumption related to factoring and instantiate concrete schemes based on specific number-theoretic assumptions. Our framework mainly improves on two aspects: (i) allowing constant recognize time $\mathcal{O}(1)$ to judge whether a certain block contains recipient’s transactions and (ii) allowing logarithmic search time $\mathcal{O}(\log{n})$ to find out the precise transactions intended for a recipient. We formalize the security model of an FSA scheme and provide provable security analysis to ensure the security of our constructions. Besides, we implement our schemes to measure their real-world performance on several metrics and give comparison results to stealth address scheme utilized by Monero.

Public chains represented by Bitcoin and Ethereum do not require users to use their real names, and transaction data are open to the whole network. Analysed based on this, researchers have achieved the deanonymization of blockchain transactions to a certain extent. Based on the existing blockchain transaction privacy protection scheme, the true link relationship between the transaction sender and receiver is hidden, which brings difficulties to regulation. In this paper, we propose a cryptocurrency mixing service RBSmix, which allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. RBSmix, through blind signature to prevent attackers from linking input and output addresses, by the threshold secret sharing algorithm, encryption technology, and a regulation team, combined with the idea of voting, tracks the source of funds for illegal addresses. Experiments show that the scheme scales to large numbers of users and can provide users with better privacy protection.

The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods.