Visible to the public Biblio

Filters: Author is Liu, Q.  [Clear All Filters]
2019-05-01
Li, P., Liu, Q., Zhao, W., Wang, D., Wang, S..  2018.  Chronic Poisoning against Machine Learning Based IDSs Using Edge Pattern Detection. 2018 IEEE International Conference on Communications (ICC). :1-7.

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). Poisoning attack, which is one of the most recognized security threats towards machine learning- based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

2019-01-31
Zhang, H., Chen, L., Liu, Q..  2018.  Digital Forensic Analysis of Instant Messaging Applications on Android Smartphones. 2018 International Conference on Computing, Networking and Communications (ICNC). :647–651.

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

2019-01-16
Jia, Z., Cui, X., Liu, Q., Wang, X., Liu, C..  2018.  Micro-Honeypot: Using Browser Fingerprinting to Track Attackers. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :197–204.
Web attacks have proliferated across the whole Internet in recent years. To protect websites, security vendors and researchers collect attack information using web honeypots. However, web attackers can hide themselves by using stepping stones (e.g., VPN, encrypted proxy) or anonymous networks (e.g., Tor network). Conventional web honeypots lack an effective way to gather information about an attacker's identity, which raises a big obstacle for cybercrime traceability and forensics. Traditional forensics methods are based on traffic analysis; it requires that defenders gain access to the entire network. It is not suitable for honeypots. In this paper, we present the design, implementation, and deployment of the Micro-Honeypot, which aims to use the browser fingerprinting technique to track a web attacker. Traditional honeypot lure attackers and records attacker's activity. Micro-Honeypot is deployed in a honeypot. It will run and gather identity information when an attacker visits the honeypot. Our preliminary results show that Micro-Honeypot could collect more information and track attackers although they might have used proxies or anonymous networks to hide themselves.
2018-02-15
Han, Z., Yang, L., Liu, Q..  2017.  A Novel Multifactor Two-Server Authentication Scheme under the Mobile Cloud Computing. 2017 International Conference on Networking and Network Applications (NaNA). :341–346.
Because the authentication method based username-password has the disadvantage of easy disclosure and low reliability, and also the excess password management degrades the user experience tremendously, the user is eager to get rid of the bond of the password in order to seek a new way of authentication. Therefore, the multifactor biometrics-based user authentication wins the favor of people with advantages of simplicity, convenience and high reliability, especially in the mobile payment environment. Unfortunately, in the existing scheme, biometric information is stored on the server side. As thus, once the server is hacked by attackers to cause the leakage of the fingerprint information, it will take a deadly threat to the user privacy. Aim at the security problem due to the fingerprint information in the mobile payment environment, we propose a novel multifactor two-server authentication scheme under mobile computing (MTSAS). In the MTSAS, it divides the authentication method and authentication means, in the meanwhile, the user's biometric characteristics cannot leave the user device. And also, MTSAS chooses the different authentication factors depending on the privacy level of the authentication, and then provides the authentication based on the different security levels. BAN logic's result proves that MTSAS has achieved the purpose of authentication, and meets the security requirements. In comparison with other schemes, the analysis shows that the proposed scheme MTSAS not only has the reasonable computational efficiency, but also keeps the superior communication cost.
2017-12-20
Che, H., Liu, Q., Zou, L., Yang, H., Zhou, D., Yu, F..  2017.  A Content-Based Phishing Email Detection Method. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :415–422.
Phishing emails have affected users seriously due to the enormous increasing in numbers and exquisite camouflage. Users spend much more effort on distinguishing the email properties, therefore current phishing email detection system demands more creativity and consideration in filtering for users. The proposed research tries to adopt creative computing in detecting phishing emails for users through a combination of computing techniques and social engineering concepts. In order to achieve the proposed target, the fraud type is summarised in social engineering criteria through literature review; a semantic web database is established to extract and store information; a fuzzy logic control algorithm is constructed to allocate email categories. The proposed approach will help users to distinguish the categories of emails, furthermore, to give advice based on different categories allocation. For the purpose of illustrating the approach, a case study will be presented to simulate a phishing email receiving scenario.
2017-03-07
Liu, Q., Zhao, X. g, Hou, Z. g, Liu, H. g.  2015.  Multi-scale wavelet kernel extreme learning machine for EEG feature classification. 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). :1546–1551.

In this paper, the principle of the kernel extreme learning machine (ELM) is analyzed. Based on that, we introduce a kind of multi-scale wavelet kernel extreme learning machine classifier and apply it to electroencephalographic (EEG) signal feature classification. Experiments show that our classifier achieves excellent performance.