Biblio

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

Community question answering (CQA) websites have become very popular platforms attracting numerous participants to share and acquire knowledge and information in Internet However, with the rapid growth of crowdsourcing systems, many malicious users organize collusive attacks against the CQA platforms for promoting a target (product or service) via posting suggestive questions and deceptive answers. These manipulate deceptive contents, aggregating into multiple collusive questions and answers (Q&As) spam groups, can fully control the sentiment of a target and distort the decision of users, which pollute the CQA environment and make it less credible. In this paper, we propose a Pattern and Burstiness based Collusive Q&A Spam Detection method (PBCSD) to identify the deceptive questions and answers. Specifically, we intensively study the campaign process of crowdsourcing tasks and summarize the clues in the Q&As’ vocabulary usage level when collusive attacks are launched. Based on the clues, we extract the Q&A groups using frequent pattern mining and further purify them by the burstiness on posting time of Q&As. By designing several discriminative features at the Q&A group level, multiple machine learning based classifiers can be used to judge the groups as deceptive or ordinary, and the Q&As in deceptive groups are finally identified as collusive Q&A spam. We evaluate the proposed PBCSD method in a real-world dataset collected from Baidu Zhidao, a famous CQA platform in China, and the experimental results demonstrate the PBCSD is effective for collusive Q&A spam detection and outperforms a number of state-of-art methods.

With the widespread deployment of Internet of Things (IoT) devices, hackers can use IoT devices to launch large-scale distributed denial of service (DDoS) attacks, which bring great harm to the Internet. However, how to defend against these attacks remains to be an open challenge. In this paper, we propose a novel prevention method for IoT DDoS attacks based on blockchain and obfuscation of IP addresses. Our observation is that IoT devices are usually resource-constrained and cannot support complicated cryptographic algorithms such as RSA. Based on the observation, we employ a novel authentication then communication mechanism for IoT DDoS attack prevention. In this mechanism, the attack targets' IP addresses are encrypted by a random security parameter. Clients need to be authenticated to obtain the random security parameter and decrypt the IP addresses. In particular, we propose to authenticate clients with public-key cryptography and a blockchain system. The complex authentication and IP address decryption operations disable IoT devices and thus block IoT DDoS attacks. The effectiveness of the proposed method is analyzed and validated by theoretical analysis and simulation experiments.