Geo-location based QR-Code authentication scheme to defeat active real-time phishing attack

Kim, Seung Hyun; Choi, Daeseon; Jin, Seunghun; Lee, Sung-Hoon

doi:10.1145/2517881.2517889

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…According to the comprehensice throretical and results from experiments, ActiveTrust could effectively against the black hole attacks. Kim et al mainly research MITM (Man-In-The-Middle) phishing attacks, which means attackers that hide between servers and users [ 8 ]. MITM may hide for long and substitute phishing URLs by operating authentication and trading information.…”

Section: Related Workmentioning

confidence: 99%

Combating QR-Code-Based Compromised Accounts in Mobile Social Networks

Guo

Cao

Wang

et al. 2016

Sensors

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Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two types of methods to identify compromised accounts in MSNs: one type is to analyze the potential threats on wireless access points and the potential threats on handheld devices’ operation systems so as to stop compromised accounts from spreading malicious messages; the other type is to apply the method of detecting compromised accounts in online social networks to MSNs. The above types of methods above focus neither on the problems of MSNs themselves nor on the interaction of sensors’ messages, which leads to the restrictiveness of platforms and the simplification of methods. In order to stop the spreading of compromised accounts in MSNs effectively, the attacks have to be traced to their sources first. Through sensors, users exchange information in MSNs and acquire information by scanning QR codes. Therefore, analyzing the traces of sensor-related information helps to identify the compromised accounts in MSNs. This paper analyzes the diversity of information sending modes of compromised accounts and normal accounts, analyzes the regularity of GPS (Global Positioning System)-based location information, and introduces the concepts of entropy and conditional entropy so as to construct an entropy-based model based on machine learning strategies. To achieve the goal, about 500,000 accounts of Sina Weibo and about 100 million corresponding messages are collected. Through the validation, the accuracy rate of the model is proved to be as high as 87.6%, and the false positive rate is only 3.7%. Meanwhile, the comparative experiments of the feature sets prove that sensor-based location information can be applied to detect the compromised accounts in MSNs.

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Section: Related Workmentioning

confidence: 99%

Combating QR-Code-Based Compromised Accounts in Mobile Social Networks

Guo

Cao

Wang

et al. 2016

Sensors

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“…This security advance gives a great defense against various types of attacks as brute force, man-in-the-middle, and keyboard hacking attacks (Y. G. Kim & Jun, 2011). The location services offered by mobile phones have also contributed in the authentication process (S.-H. Kim, Choi, Jin, & Lee, 2013;Zhang, Kondoro, & Muftic, 2012). It could provide the continuous identification and authentication and forces the remote threats to be connected to physical locations as claimed in (Choi & www.ccsenet.org/cis Computer and Information Science Vol.…”

Section: Token Based Authenticationmentioning

confidence: 99%

Authentication systems: principles and threats

Abdulkader¹,

Atia²,

Mostafa³

2015

CIS

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Identity manipulation is considered a serious security issue that has been enlarged with the spread of automated systems that could be accessed either locally or remotely. Availability, integrity, and confidentiality represent the basic requirements that should be granted for successful authentication systems. Personality verification has taken multiple forms depending on different possession types. They are divided into knowledge based, token based, and biometric based authentication. The permanent ownership to the human being has increased the chances of deploying biometrics based authentication in highly secure systems. It includes capturing the biological traits, which are physiological or behavioral, extracting the important features and comparing them to the previously stored features that belong to the claimed user. Various kinds of attacks aim to take down the basic requirements at multiple points. This paper describes different types of authentication along with their vulnerable points and threatening attacks. Then it provides more details about the biometric system structure as well as examples of distinguishing biological characteristics, organized by their locations. It shows the performance results of various biometric systems along with the deployed algorithms for different components.

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