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Since the evolution of the internet, many small and large companies have moved their businesses to the internet to provide services to customers worldwide. Cyber credit card fraud or no card present fraud is increasingly rampant in the recent years for the reason that the credit card is majorly used to request payments by these companies on the internet. Therefore the need to ensure secured transactions for credit-card owners when consuming their credit cards to make electronic payments for goods and services provided on the internet is a criterion. Data mining has popularly gained recognition in combating cyber credit-card fraud because of its effective artificial intelligence (AI) techniques and algorithms that can be implemented to detect or predict fraud through Knowledge Discovery from unusual patterns derived from gathered data. In this study, a system's model for cyber credit card fraud detection is discussed and designed. This system implements the supervised anomaly detection algorithm of Data mining to detect fraud in a real time transaction on the internet, and thereby classifying the transaction as legitimate, suspicious fraud and illegitimate transaction. The anomaly detection algorithm is designed on the Neural Networks which implements the working principal of the human brain (as we humans learns from past experience and then make our present day decisions on what we have learned from our past experience). To understand how cyber credit card fraud are being committed, in this study the different types of cyber fraudsters that commit cyber credit card fraud and the techniques used by these cyber fraudsters to commit fraud on the internet is discussed.

Botnet is an attack network composed of hundreds of millions of compromised computers. Botnet is emerging as the most serious threat against cyber-security and is used to launch Distributed Denial of Service (DDoS) attacks, malware dissemination, phishing, remote control, click fraud, and etc. Although botnet has posed serious security threat on Internet, the research of detecting and preventing botnet is still in its infancy. One effective technique for botnet detection is to identify botnet CC furthermore, we also illustrate the correlation methods within the same botnet’s C&C communications to decrease the false positive rate.

Click fraud is arguably the cyberworld’s biggest scam. How do click fraudsters frame their actions? What are the characteristics of click fraud victims? How do formal and informal institutions affect click fraudsters’ actions? We address these questions by examining the contexts, mechanisms, and processes associated with the click fraudsters’ profitability and performance. We also discuss some attempts to criminalize and stigmatize click fraudsters.

Web advertisers prefer the cost-per-action (CPA) advertisement model whereby an advertiser pays a web publisher according to the actual amount of transactions, rather than the volume of advertisement clicks. The main obstacle for a wide deployment of this model is the deflation fraud. Namely, a dishonest advertiser under-reports the transaction count in order to discharge less. In this paper, we present a mechanism to detect such a fraud using a hybrid of cryptography and probability tools. With the assistance from a small number of users, the publisher can detect deflation fraud with a success probability growing exponentially with the fraud amount, and can estimate the amount of frauds. Our scheme is amiable to both the advertiser and the users because the existing transaction model remains unchanged. It is also efficient and scalable as the incurred communication, computation and storage costs are independent of the number of transactions.

Thanks to its low product-promotion cost and its efficiency, targeted online advertising has become very popular. Unfortunately, being profile-based, online advertising methods violate consumers' privacy, which has engendered resistance to the ads. However, protecting privacy through anonymity seems to encourage click-fraud. In this paper, we define consumer's privacy and present a privacy-preserving, targeted ad system (PPOAd) which is resistant towards click fraud. Our scheme is structured to provide financial incentives to all entities involved.

In pay-per-click online advertising systems like Google, Overture, or MSN, advertisers are charged for their ads only when a user clicks on the ad. While these systems have many advantages over other methods of selling online ads, they suffer from one major drawback. They are highly susceptible to a particular style of fraudulent attack called click fraud. Click fraud happens when an advertiser or service provider generates clicks on an ad with the sole intent of increasing the payment of the advertiser. Leaders in the pay-per-click marketplace have identified click fraud as the most significant threat to their business model. We demonstrate that a particular class of learning algorithms, called click-based algorithms, are resistant to click fraud in some sense. We focus on a simple situation in which there is just one ad slot, and show that fraudulent clicks can not increase the expected payment per impression by more than o(1) in a click-based algorithm. Conversely, we show that other common learning algorithms are vulnerable to fraudulent attacks.