With the rapid deployment of IOT devices, Bluetooth networks, which form Personal Area Networks(PAN), have become the wireless network of choice for small range/indoor communications networks. Bluetooth is widely used to deliver audio streams (e.g.: Bluetooth headphones, Music systems in cars), connecting peripherals devices to more powerful devices (e.g.: keyboards to computers), connecting wearable technology like smart watches, heart monitors and fitness trackers. It's imperative that Bluetooth networks (like other wireless networks) are secure against cyberattacks such as Man In The Middle Attacks(MITM), Denial of Service attacks(DoS), etc. Moreover, Bluetooth is used heavily in mobile devices/ sensors, and consequently they become sensitive to battery utilization attacks; this type of attacks requires the Bluetooth devices to be secure against different battery draining attacks. As a part of this paper we present an anomaly-based intrusion detection system for Bluetooth networks; Bluetooth IDS (BIDS). The BIDS use an n-gram based approach to characterize the normal behavior of the Bluetooth protocol. Smoothing techniques like Jelinek-Mercer smoothing was used to improve the machine learning algorithm used for detecting abnormal Bluetooth operations. Machine learning algorithms like C4.5, AdaBoostMl, SVM, Naïve Bayes, RIPPER, Bagging were used to build the behavior models for the Bluetooth protocol. The developed models had high accuracy with precision up to 99.6% and recall up to 99.6%.