This work focuses on multi-objective scheduling problems of automated manufacturing systems. Such an automated manufacturing system has limited resources and flexibility of processing routes of jobs, and hence is prone to deadlock. Its scheduling problem includes both deadlock avoidance and performance optimization. A new Pareto-based genetic algorithm is proposed to solve multi-objective scheduling problems of automated manufacturing systems. In automated manufacturing systems, scheduling not only sets up a routing for each job but also provides a feasible sequence of job operations. Possible solutions are expressed as individuals containing information of processing routes and the operation sequence of all jobs. The feasibility of individuals is checked by the Petri net model of an automated manufacturing system and its deadlock controller, and infeasible individuals are amended into feasible ones. The proposed algorithm has been tested with different instances and compared to the modified non-dominated sorting genetic algorithm II. The experiment results show the feasibility and effectiveness of the proposed algorithm.

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Intrusion detection can be essentially regarded as a classification problem, namely, distinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of intrusion detection to learn attack signatures. Decision tree algorithm is used as simple base learner of boosting algorithm. Furthermore, this paper employs the Principle Component Analysis (PCA) approach, an effective data reduction approach, to extract the key attribute set from the original high-dimensional network traffic data. KDD CUP 99 data set is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak learners by combining a number of simple "weak learners". In our experiments, the error rate of training phase of boosting algorithm is reduced from 30.2% to 8% after 10 iterations. Besides, this paper also compares boosting algorithm with Support Vector Machine (SVM) algorithm and shows that the classification accuracy of boosting algorithm is little better than SVM algorithm's. However, the generalization ability of SVM algorithm is better than boosting algorithm.

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In this paper, we formulate intrusion response problem as a factored Partially Observed Markov Decision Process (POMDP) model. Furthermore, a hierarchical planning algorithm is presented to decompose overall POMDP into some small sub-POMDPs and compute global optimal response policy according to MLS heuristic criterion. Meanwhile, reachable attack intention is defined and used to identify false alerts and compress belief state space. Finally, some experiments were performed to compare proposed algorithm with previous approaches and the results show that our approach have a good performance in response accuracy to different attack scenarios and robustness against false alerts.

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