Visible to the public Biblio

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2020-07-10
Mi, Xianghang, Feng, Xuan, Liao, Xiaojing, Liu, Baojun, Wang, XiaoFeng, Qian, Feng, Li, Zhou, Alrwais, Sumayah, Sun, Limin, Liu, Ying.  2019.  Resident Evil: Understanding Residential IP Proxy as a Dark Service. 2019 IEEE Symposium on Security and Privacy (SP). :1185—1201.

An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them. In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.

2018-12-03
Zhou, Zhe, Li, Zhou, Zhang, Kehuan.  2017.  All Your VMs Are Disconnected: Attacking Hardware Virtualized Network. Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy. :249–260.
Single Root I/O Virtualization (SRIOV) allows one physical device to be used by multiple virtual machines simultaneously without the mediation from the hypervisor. Such technique significantly decreases the overhead of I/O virtualization. But according to our latest findings, in the meantime, it introduces a high-risk security issue that enables an adversary-controlled VM to cut off the connectivity of the host machine, given the limited filtering capabilities provided by the SRIOV devices. As showcase, we demonstrate two attacks against SRIOV NIC by exploiting a vulnerability in the standard network management protocol, OAM. The vulnerability surfaces because SRIOV NICs treat the packets passing through OAM as data-plane packets and allow untrusted VMs to send and receive these packets on behalf of the host. By examining several off-the-shelf SRIOV NICs and switches, we show such attack can easily turn off the network connection within a short period of time. In the end, we propose a defense mechanism which runs on the existing hardware and can be readily deployed.
2017-09-15
Liao, Xiaojing, Yuan, Kan, Wang, XiaoFeng, Li, Zhou, Xing, Luyi, Beyah, Raheem.  2016.  Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :755–766.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

2017-07-24
Liao, Xiaojing, Yuan, Kan, Wang, XiaoFeng, Li, Zhou, Xing, Luyi, Beyah, Raheem.  2016.  Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :755–766.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

2017-05-22
Alrwais, Sumayah, Yuan, Kan, Alowaisheq, Eihal, Liao, Xiaojing, Oprea, Alina, Wang, XiaoFeng, Li, Zhou.  2016.  Catching Predators at Watering Holes: Finding and Understanding Strategically Compromised Websites. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :153–166.

Unlike a random, run-of-the-mill website infection, in a strategic web attack, the adversary carefully chooses the target frequently visited by an organization or a group of individuals to compromise, for the purpose of gaining a step closer to the organization or collecting information from the group. This type of attacks, called "watering hole", have been increasingly utilized by APT actors to get into the internal networks of big companies and government agencies or monitor politically oriented groups. With its importance, little has been done so far to understand how the attack works, not to mention any concrete step to counter this threat. In this paper, we report our first step toward better understanding this emerging threat, through systematically discovering and analyzing new watering hole instances and attack campaigns. This was made possible by a carefully designed methodology, which repeatedly monitors a large number potential watering hole targets to detect unusual changes that could be indicative of strategic compromises. Running this system on the HTTP traffic generated from visits to 61K websites for over 5 years, we are able to discover and confirm 17 watering holes and 6 campaigns never reported before. Given so far there are merely 29 watering holes reported by blogs and technical reports, the findings we made contribute to the research on this attack vector, by adding 59% more attack instances and information about how they work to the public knowledge. Analyzing the new watering holes allows us to gain deeper understanding of these attacks, such as repeated compromises of political websites, their long lifetimes, unique evasion strategy (leveraging other compromised sites to serve attack payloads) and new exploit techniques (no malware delivery, web only information gathering). Also, our study brings to light interesting new observations, including the discovery of a recent JSONP attack on an NGO website that has been widely reported and apparently forced the attack to stop.