Quantum anonymous collision detection for quantum networks

Quantum mechanics offers new opportunities for diverse information processing tasks in communication and computational networks. In the last two decades, the notion of quantum anonymity has been introduced in several networking tasks that provide an unconditional secrecy of identity for the communicating parties. In this article, we propose a quantum anonymous collision detection (QACD) protocol which detects not only the collision but also guarantees the anonymity in the case of multiple senders. We show that the QACD protocol serves as an important primitive for a quantum anonymous network that features tracelessness and resource efficiency. Furthermore, the security analysis shows that this protocol is robust against the adversary and malicious participants.

Website Fingerprinting Attacks Based on Homology Analysis

Website fingerprinting attacks allow attackers to determine the websites that users are linked to, by examining the encrypted traffic between the users and the anonymous network portals. Recent research demonstrated the feasibility of website fingerprinting attacks on Tor anonymous networks with only a few samples. Thus, this paper proposes a novel small-sample website fingerprinting attack method for SSH and Shadowsocks single-agent anonymity network systems, which focuses on analyzing homology relationships between website fingerprinting. Based on the latter, we design a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) attack classification model that achieves 94.8% and 98.1% accuracy in classifying SSH and Shadowsocks anonymous encrypted traffic, respectively, when only 20 samples per site are available. We also highlight that the CNN-BiLSTM model has significantly better migration capabilities than traditional methods, achieving over 90% accuracy when applied on a new set of monitored sites with only five samples per site. Overall, our experiments demonstrate that CNN-BiLSTM is an efficient, flexible, and robust model for website fingerprinting attack classification.

An Ensemble Deep Neural Network Model for Onion-Routed Traffic Detection to Boost Cloud Security

Anonymous network communication using onion routing networks such as Tor are used to guard the privacy of sender by encrypting all messages in the overlapped network. These days most of the onion routed communications are not only used for decent cause but also cyber offenders are ill-using onion routings for scanning the ports, hacking, exfiltration of theft data, and other types of online frauds. These cyber-crime attempts are very vulnerable for cloud security. Deep learning is highly effective machine learning method for prediction and classification. Ensembling multiple models is an influential approach to increase the efficiency of learning models. In this work, an ensemble deep learning-based classification model is proposed to detect communication through Tor and non-Tor network. Three different deep learning models are combined to achieve the ensemble model. The proposed model is also compared with other machine learning models. Classification results shows the superiority of the proposed model than other models.

Mental distress and its associations with behavioral outcomes during the COVID-19 pandemic: A national survey of Chinese adults

Background This study primarily aimed to evaluate the associations between mental distress and COVID-19-related changes in behavioral outcomes, and potential modifiers (age, gender, and educational attainment) of such associations.Methods An online survey using anonymous network sampling was conducted in China during April-May, 2020 using a 74-item questionnaire distributed through social media. A national sample of 10,545 adults in 31 provinces provided data on socio-demographic characteristics, COVID-19-related mental distress, and changes in behavioral outcomes. Structural equation models were used for data analyses.Results About 13% of adults reported experiencing at least one symptom of mental distress. After adjusting for covariates, greater mental distress was associated with increased smoking and alcohol consumption (among current smokers and drinkers) and with both increased and decreased physical activity. Underweight adults were more likely to lose body weight (≥ 1 kg) whereas overweight adults were more likely to gain weight by the same amount. Association between mental distress and change in physical activity was stronger in adults aged 40 and above and those with high education.Conclusions Mental distress was associated with increased smoking in males but not females. These findings inform the design of tailored public health interventions aimed to mitigate long-term negative consequences of mental distress on outcomes.

Detection and identification method of the tor bundle use

This paper presents the result of author’s research aimed at developing a detecting and identifying method of the Tor Bundle use in data transmission networks, in particular, on the Internet. Based on these characteristics, an algorithm has been developed that allows legitimate blocking of user access to a global network by a popular anonymizer. The subject of the study was an SSL/TLS encryption certificate, which is transmitted by the Tor network server to the user of the Tor Bundle and which contains the set of data necessary for its identification during the implementation of the TLS “handshake”. In the course of the study of the certificates features, several distinguishing features were identified, namely: the name of the subject and issuer of the certificate, which is a random set of letters and numbers; port used when connecting to an anonymous network; certificate size. Based on the data received, a method is proposed that allows the provider’s server to block the connection during which a certificate with certain characteristics is transmitted.