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Wang, W., Tang, B., Zhu, C., Liu, B., Li, A., Ding, Z..  2020.  Clustering Using a Similarity Measure Approach Based on Semantic Analysis of Adversary Behaviors. 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). :1—7.

Rapidly growing shared information for threat intelligence not only helps security analysts reduce time on tracking attacks, but also bring possibilities to research on adversaries' thinking and decisions, which is important for the further analysis of attackers' habits and preferences. In this paper, we analyze current models and frameworks used in threat intelligence that suited to different modeling goals, and propose a three-layer model (Goal, Behavior, Capability) to study the statistical characteristics of APT groups. Based on the proposed model, we construct a knowledge network composed of adversary behaviors, and introduce a similarity measure approach to capture similarity degree by considering different semantic links between groups. After calculating similarity degrees, we take advantage of Girvan-Newman algorithm to discover community groups, clustering result shows that community structures and boundaries do exist by analyzing the behavior of APT groups.

Cominelli, M., Gringoli, F., Patras, P., Lind, M., Noubir, G..  2020.  Even Black Cats Cannot Stay Hidden in the Dark: Full-band De-anonymization of Bluetooth Classic Devices. 2020 IEEE Symposium on Security and Privacy (SP). :534—548.

Bluetooth Classic (BT) remains the de facto connectivity technology in car stereo systems, wireless headsets, laptops, and a plethora of wearables, especially for applications that require high data rates, such as audio streaming, voice calling, tethering, etc. Unlike in Bluetooth Low Energy (BLE), where address randomization is a feature available to manufactures, BT addresses are not randomized because they are largely believed to be immune to tracking attacks. We analyze the design of BT and devise a robust de-anonymization technique that hinges on the apparently benign information leaking from frame encoding, to infer a piconet's clock, hopping sequence, and ultimately the Upper Address Part (UAP) of the master device's physical address, which are never exchanged in clear. Used together with the Lower Address Part (LAP), which is present in all frames transmitted, this enables tracking of the piconet master, thereby debunking the privacy guarantees of BT. We validate this attack by developing the first Software-defined Radio (SDR) based sniffer that allows full BT spectrum analysis (79 MHz) and implements the proposed de-anonymization technique. We study the feasibility of privacy attacks with multiple testbeds, considering different numbers of devices, traffic regimes, and communication ranges. We demonstrate that it is possible to track BT devices up to 85 meters from the sniffer, and achieve more than 80% device identification accuracy within less than 1 second of sniffing and 100% detection within less than 4 seconds. Lastly, we study the identified privacy attack in the wild, capturing BT traffic at a road junction over 5 days, demonstrating that our system can re-identify hundreds of users and infer their commuting patterns.

Zheng, L., Xue, Y., Zhang, L., Zhang, R..  2017.  Mutual Authentication Protocol for RFID Based on ECC. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). 2:320–323.

In this paper, a mutual authentication protocol based on ECC is designed for RFID systems. This protocol is described in detail and the performance of this protocol is analyzed. The results show that the protocol has many advantages, such as mutual authentication, confidentiality, anonymity, availability, forward security, scalability and so on, which can resist camouflage attacks, tracking attacks, denial of service attacks, system internal attack.