Abstract-Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Several anti-cheating solutions have been developed by gaming companies. However, most of these companies use cheating detection measures that may involve breaches to users' privacy. In our paper, we provide a serverside anti-cheating solution that uses only game logs. Our method is based on defining an honest player's behavior and cheaters' behavior first. After that, using machine learning classifiers to train cheating models, then detect cheaters. We presented our results in different organizations to show different options for developers, and our methods' results gave a very high accuracy in most of the cases. Finally, we provided a detailed analysis of our results with some useful suggestions for online games developers.

In service-oriented computing, web services composition is the process of translating user requirements into a workflow. This workflow comprises many tasks, each of which includes an abstract definition for some of the user requirements. Web services can be aggregated to handle the workflow. Many of these services are available from various providers for each task; they are referred to, in aggregate, as the candidate list. The web service selection (WSS) problem centers on selecting the best service from these candidates based on the quality of service (QoS) features. In this paper, we propose an enhancement to the ant colony optimization (ACO) algorithm based on a swap concept for the QoS-aware WSS problem. The aim of the enhancement to the ACO is to avoid the trap of local optima and reduce the search duration. We believe that the integration of many potent solutions will help the ACO algorithm yield a better solution and avoid stagnation. Several experiments were conducted to compare the proposed algorithm with the ACO and flying ACO (FACO) algorithms. Two different types of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment's results shows that the proposed algorithm is better than ACO by 12% and FACO by 11% in terms of quality of solutions. The results in the second type of experiment show that the proposed algorithm continuously outperforms both algorithms in terms of quality of solutions.INDEX TERMS Service-oriented computing (SOC), web services composition (WSC), web service (WS), web service selection (WSS), ant colony optimization (ACO).