The telephony channel has become an attractive target for cyber criminals, who are using it to craft a variety of attacks. In addition to delivering voice and messaging spam, this channel is also being used to lure victims into calling phone numbers that are controlled by the attackers. One way this is done is by aggressively advertising phone numbers on social media (e.g., Twitter). This form of spam is then monetized over the telephony channel, via messages/calls made by victims. We refer to this type of attacks as outgoing phone communication (OPC) attacks.

Telephone networks first appeared more than a hundred years ago, long before transistors were invented. They, therefore, form the oldest large scale network that has grown to touch over 7 billion people. Telephony is now merging many complex technologies and because numerous services enabled by these technologies can be monetized, telephony attracts a lot of fraud. In 2015, a telecom fraud association study estimated that the loss of revenue due to global telecom fraud was worth 38 billion US dollars per year. Because of the convergence of telephony with the Internet, fraud in telephony networks can also have a negative impact on security of online services. However, there is little academic work on this topic, in part because of the complexity of such networks and their closed nature. This paper aims to systematically explore fraud in telephony networks. Our taxonomy differentiates the root causes, the vulnerabilities, the exploitation techniques, the fraud types and finally the way fraud benefits fraudsters. We present an overview of each of these and use CAller NAMe (CNAM) revenue share fraud as a concrete example to illustrate how our taxonomy helps in better understanding this fraud and to mitigate it.

Over the past decade, the number of mobile phones has increased dramatically, overtaking the world population in October 2014. In developing countries like India and China, mobile subscribers outnumber traditional landline users and account for over 90% of the active population. At the same time, convergence of telephony with the Internet with technologies like VoIP makes it possible to reach a large number of telephone users at a low or no cost via voice calls or SMS (short message service) messages. As a consequence, cybercriminals are abusing the telephony channel to launch attacks, e.g., scams that offer fraudulent services and voice-based phishing or vishing, that have previously relied on the Internet. In this paper, we introduce and deploy the first mobile phone honeypot called MobiPot that allow us to collect fraudulent calls and SMS messages. We implement multiple ways of advertising mobile numbers (honeycards) on MobiPot to investigate how fraudsters collect phone numbers that are targeted by them. During a period of over seven months, MobiPot collected over two thousand voice calls and SMS messages, and we confirmed that over half of them were unsolicited. We found that seeding honeycards enables us to discover attacks on the mobile phone numbers which were not known before.

Recent convergence of telephony with the Internet offers malicious actors the ability to craft cross-channel attacks that leverage both telephony and Internet resources. Bulk messaging services can be used to send unsolicited SMS messages to phone numbers. While the long-term properties of email spam tactics have been extensively studied, such behavior for SMS spam is not well understood. In this paper, we discuss a novel SMS abuse attribution system called CHURN. The proposed system is able to collect data about large SMS abuse campaigns and analyze their passive DNS records and supporting website properties. We used CHURN to systematically conduct attribution around the domain names and IP addresses used in such SMS spam operations over a five year time period. Using CHURN, we were able to make the following observations about SMS spam campaigns: (1) only 1 % of SMS abuse domains ever appeared in public domain blacklists and more than 94 % of the blacklisted domain names did not appear in such public blacklists for several weeks or even months after they were first reported in abuse complaints, (2) more than 40 % of the SMS spam domains were active for over 100 days, and (3) the infrastructure that supports the abuse is surprisingly stable. That is, the same SMS spam domain names were used for several weeks and the IP infrastructure that supports these campaigns can be identified in a few networks and a small number of IPs, for several months of abusive activities. Through this study, we aim to increase the situational awareness around SMS spam abuse, by studying this phenomenon over a period of five years. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Usability is an important aspect of security, because poor usability motivates users to find shortcuts that bypass the system. Existing studies on keystroke biometrics evaluate the usability issue in terms of the average false rejection rate (FRR). We show in this paper that such an approach underestimates the user impact in two ways. First, the FRR of keystroke biometrics changes for the worse under a range of common conditions such as background music, exercise and even game playing. In a user study involving 111 participants, the average penalties (increases) in FRR are 0.0360 and 0.0498, respectively, for two different classifiers. Second, presenting the FRR as an average obscures the fact that not everyone is suitable for keystroke biometrics deployment. For example, using a Monte Carlo simulation, we found that 30% of users would encounter an account lockout before their 50th authentication session (given a lockout policy of 3 attempts) if they are affected by external influences 50% of the time when authenticating.

Most social networking platforms protect user's private information by limiting access to it to a small group of members, typically friends of the user, while allowing (virtually) everyone's access to the user's public data. In this paper, we exploit public data available on Facebook to infer users' undisclosed interests on their profile pages. In particular, we infer their undisclosed interests from the public data fetched using Graph APIs provided by Facebook. We demonstrate that simply liking a Facebook page does not corroborate that the user is interested in the page. Instead, we perform sentiment-oriented mining on various attributes of a Facebook page to determine the user's real interests. Our experiments conducted on over 34,000 public pages collected from Facebook and data from volunteers show that our inference technique can infer interests that are often hidden by users on their personal profile with moderate accuracy. We are able to disclose 22 interests of a user and find more than 80,097 users with at least 2 interests. We also show how this inferred information can be used to break a preference based backup authentication system.

To meet the demand of scalability and usability, many real-world authentication systems have adopted the idea of responsibility shifting, explicitly or implicitly, where a user's responsibility of authentication is shifted to another entity, usually in case of failure of the primary authentication method. One example of responsibility shifting is in the fourth-factor authentication [1] whereby a user gets the crucial authentication assistance from a helper who takes over the responsibility. In the fourth-factor authentication system [1], subverting/coercing the helper (trustee) allows the adversary to log in without capturing the password of the user.

Malware continues to thrive on the Internet. Besides automated mechanisms for detecting malware, we provide users with trust evidence information to enable them to make informed trust decisions. To scope the problem, we study the challenge of assisting users with judging the trustworthiness of software downloaded from the Internet.

Security of web servers has become a sensitive subject today. Prediction of normal and abnormal request is problematic due to large number of false alarms in many anomaly based Intrusion Detection Systems (IDS). SS-IDS derives automatically the parameter profiles from the analyzed data thereby generating the Statistical Signatures. Statistical Signatures are based on modeling of normal requests and their distribution value without explicit intervention. Several attributes are used to calculate the behavior of the legitimate request on the web server. SS-IDS is best suited for the newly installed web servers which doesn’t have large number of requests in the data set to train the IDS and can be used on top of currently used signature based IDS like SNORT. Experiments conducted on real data sets have shown high accuracy up to 99.98% for predicting valid request as valid and false positive rate ranges from 3.82-7.84%.