Online auctions have become an increasingly popular and convenient way for conducting ecommerce transactions on the Web. However, the rapid surge of users participating in online auctions has led to auction fraud. Among the types of auction fraud, the most prominent is Shill bidding. Shill bidding is intentionally fake bidding by a seller on his/her own auction to inflate the final price. This can be accomplished either by the seller himself/herself or by someone colluding with the seller to place fake bids on his/her behalf. Therefore, it is difficult to manually investigate the large amount of auctions and bidders for shill bidding activities. Detecting shill bidding in real-time is the most effective way to reduce the loss result of the auction fraud. Researchers have proposed multiple approaches and experimented to control the losses incurred due to shill bidding. This paper investigates the real-time detection techniques of shill bidding. It also provides a brief overview of major work that has been conducted in shill bidding detection including both offline and real-time approaches. Furthermore, this paper identifies research gaps in the detection and prevention of shill bidding behaviours. It also provides future research issues and challenges to detect shill bidding in real-time.