6 research outputs found
Efficient detection of spam over internet telephony by machine learning algorithms
Recent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment to the user's experience. A number of techniques have been produced to detect SPIT calls. We reviewed these techniques and have proposed a new approach for quick, efficient and highly accurate detection of SPIT calls using neural networks and novel call parameters. The performance of this system was compared to other state-of-art machine learning algorithms on a real-world dataset, which has been published online and is publicly available. The results of the study demonstrated that new parameters may help improve the effectiveness and accuracy of applied machine learning algorithms. The study explored the entire process of designing a SPIT detection algorithm, including data collection and processing, defining suitable parameters, and final evaluation of machine learning models.Web of Science1013342613341
Building a Collaborative Phone Blacklisting System with Local Differential Privacy
Spam phone calls have been rapidly growing from nuisance to an increasingly
effective scam delivery tool. To counter this increasingly successful attack
vector, a number of commercial smartphone apps that promise to block spam phone
calls have appeared on app stores, and are now used by hundreds of thousands or
even millions of users. However, following a business model similar to some
online social network services, these apps often collect call records or other
potentially sensitive information from users' phones with little or no formal
privacy guarantees.
In this paper, we study whether it is possible to build a practical
collaborative phone blacklisting system that makes use of local differential
privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the
challenges and trade-offs related to using LDP, evaluate our LDP-based system
on real-world user-reported call records collected by the FTC, and show that it
is possible to learn a phone blacklist using a reasonable overall privacy
budget and at the same time preserve users' privacy while maintaining utility
for the learned blacklist.Comment: 15 pages, 10 figures, 7 algorithm
Consumer-facing technology fraud : economics, attack methods and potential solutions
The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks
Consumer-facing technology fraud: Economics, attack methods and potential solutions
The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks.N/
Cyber Threats and NATO 2030: Horizon Scanning and Analysis
The book includes 13 chapters that look ahead to how NATO can best address the cyber threats, as well as opportunities and challenges from emerging and disruptive technologies in the cyber domain over the next decade.
The present volume addresses these conceptual and practical requirements and contributes constructively to the NATO 2030 discussions. The book is arranged in five short parts...All the chapters in this book have undergone double-blind peer review by at least two external experts.https://scholarworks.wm.edu/asbook/1038/thumbnail.jp