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دسته بندی:
حقوق خصوصی - Private law
سال انتشار:
2020
عنوان انگلیسی مقاله:
Socioscope: I know who you are, a robo, human caller or service number
ترجمه فارسی عنوان مقاله:
جامعه شناسی: من می دانم شما چه کسی هستید ، یک روبو ، تماس گیرنده انسانی یا شماره خدمات
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 105 (2020) 297-307. doi:10.1016/j.future.2019.11.007
نویسنده:
Muahammad Ajmal Azad a,∗, Mamoun Alazab b, Farhan Riaz c, Junaid Arshad d, Tariq Abullah a
چکیده انگلیسی:
Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate
in our daily life and have been widely adopted for business and personal communications. At the
same time, scammers, criminals, and fraudsters have also find the telephony network an attractive
and affordable medium to target end-users with the advertisement, marketing of legal and illegal
products, and bombard them with the huge volume of unwanted calls. These calls would not only
trick call recipients into disclosing their private information such as credit card numbers, PIN code
which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing.
The fraudsters, political campaigners can also use telephony systems to spread malicious information
(hate political or religious messages) in real-time through audio or text messages, which have serious
political and social consequences if malicious callers are not mitigated in a quick time. In this context,
the identification of malicious callers would not only minimize telephony fraud but would also bring
peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get
feedback from the call recipients about their recent interactions with the caller, but these systems
not only bring inconvenience to callees but also require changes in the system design. The call detail
records extensively log the activities of users and can be used to categorize them as the spammer and
non-spammer. In this paper, we utilize the information from the call detailed records and proposed a
spam detection framework for the telephone network that identifies malicious callers by utilizing the
social behavioral features of users within the network. To this extent, we first model the behavior of
the users as the directed social graph and then analyze different features of the social graph i.e. the
Relationship Network and Call patterns of users towards their peers. We then used these features along
with the decision tree to classify callers into three classes i.e. human, spammer and call center. We
analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed
evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection
accuracy and extremely low false-positive rate. The performance results show that the spammers and
call center numbers not only have a large number of non-repetitive calls but also have a large number
of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of
repetitive calls and most of them interacted for a relatively long duration.
Keywords: Social network analysis | Telephone spam detection | Robocalls | Telephone call records | Telemarketers | User characterization
قیمت: رایگان
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