Identity theft

About: Identity theft is a research topic. Over the lifetime, 2284 publications have been published within this topic receiving 31700 citations.

A Framework for Secure VoIP

Abstract: VoIP stands for Voice over Internet Protocol and is a way to carry voice traffic over computer networks like the Internet. Over the last decade VoIP has become increasingly popular, gaining millions of subscribers every year (e.g. LINE and WECHAT provide voicecall facilities) and has certainly caught the eye of telecommunication service providers all over the world. The driving factor for the success of VoIP is cost reduction, both for users and providers. But VoIP doesn’t only bring reduced costs it also brings threats and vulnerabilities since it is IP based it’s susceptible to large number of threats. The threats include spoofing or identity theft and call redirection, making data integrity a major risk. Therefore authentication and encryption techniques can be used to design a framework which can survive the possible threats. In this security framework authentication is implemented first to authenticate the true user and then cryptography techniques is used to safely transmit the information stream over the network. The authentication part will be implemented using biometrics because it is not possible to theft anyone’s physical features.

Session hijacking vulnerabilities and prevention algorithms in the use of internet.

Abstract: The concept of Internet security is studied by computer science as a safe medium for exchanging data while minimizing the likelihood of online threats. The extensive use of advanced web-based software in different industries such as education, retail, medical care, and payment systems, represents a security challenge for the programmers and an opportunity for the hackers to attack through session hijacking. This paper aims to present vulnerability with the respective control mechanisms and to propose an approach for avoiding hijacking threats by using one-time cookies along with other prevention strategies. The study uses a la review of literature, by analyzing resources from existing literature. Based on recent OWASP guidelines, session hijacking of attack is indeed one of the most frequent attacks that happens lately. Session hijacking happens as a result of poorly designed websites and a lack of security mechanisms, where the user's identity and session data are exposed. Keywords: Cookies; internet; security; session hijacking, vulnerability.

Using AI to combat fraud in the voice channel

Abstract: Whether it's an attempt to defraud individuals by accessing their bank accounts, or a large-scale attack on an organisation targeting huge volumes of data, identity is at the heart of most sophisticated criminal activity today. And while most online channels have been continually strengthened to stay ahead of evolving criminal tactics, the voice channel – another critical customer connection point – has increasingly come to be seen as a vulnerable target.

Malicious URL Detection Using Machine Learning

Abstract: AbstractIn recent years cyberattacks have become destructive and targeted. With technological advancements, diverse threats are launching in a sophisticated way that targets people to defraud them. Many web applications have been struggling to improve the reliability and security of their platforms to protect users from fraud, revenue, or malware. These attacks use malicious uniform resource locators (URLs) to attack web users. These URLs host unwanted content in the form of junk emails, phishing, or unauthorized drive-by downloads. Unsuspecting people click these phishing URLs and become victims of unethical anonymous activities like identity theft (personal or financial details) and installation of viruses. Therefore, it is necessary to detect malicious URLs accurately for resolving security issues. Traditional protection method, such as blacklisting, remains a classical technique for the detection of malicious URLs due to its simplicity but cannot detect unknown malicious URLs; hence, machine learning approaches are being used for achieving better results. This chapter aims to provide a structural understanding of popular feature extraction techniques and machine learning algorithms.KeywordsMalicious URLFeature extractionBlacklistingMachine learning

Lawful Trojan Horse

Abstract: News outlets don't usually report on training methods in counter-cyberterrorism, particularly lawful trojan attacks. Instead they describe recent cyberterrorist attacks, or threats, or laws and regulations concerning internet privacy or identity theft. Yet Europe is looking to do just that to head-off the next major cyberattack by creating rules for how member states should react and respond. Several news outlets, for example, reported that Germany's Federal Criminal Police Office (BKA) were using a Trojan Horse to access the smartphone data of suspected individuals before the information was encrypted. Although the urge to strike back may be palpable, hacking-back can put power back into the hands of the suspect. The consensus now is that government action is preferable to hacking-back at attackers.