Wireless sensor networks (WSNs) include a number of wireless sensor nodes distributed in a geographical area. Due to the intrinsic and functional nature of WSNs, these networks face many challenges such as limited energy resources of sensor nodes and the routing congestions. Clustering is the most common routing approach to control congestion and achieve energy efficiency in WSNs, which ultimately prolongs the network lifetime. In the cluster-based routing protocols, optimal selection of cluster heads (CHs) is an NP-hard problem, and consequently, heuristic and metaheuristic algorithms can be employed to obtain a near-optimal solution. In this paper, a fuzzy knowledgebased metaheuristic model based on multiobjective fuzzy inference system (moFIS) and bacterial foraging optimization (BFO), named moFIS-BFO, is proposed as an efficient routing protocol for clustered WSNs. In the moFIS-BFO model, the moFIS is utilized to calculate the chance of each node for becoming a CH based on different criteria including degree difference, residual energy, total distance to neighbors, and distance to the base station. Taking into account the calculated chances of nodes, the BFO is employed to select proper CHs at every round. To control the queue in cluster headings, a priority ranking method is used to control congestions and avoid packet wastages. Simulation results demonstrate the superiority of the moFIS-BFO protocol against the existing techniques to control congestion and prolong the network lifetime.

In today's world, many public and private services are provided virtually on the Internet. Due to the increasing dynamism and development of computer networks, intrusion detection systems, as one of the hottest topics in network security, has become an attractive area of research for researchers. The intrusion detection system tries to categorize the activity of the connections into two categories, normal and abnormal. In intrusion detection system, each connection is described based on a set of features, and decisions about whether that connection is normal or abnormal are made using those features. The act of determining the norm or abnormality of a connection is called classification. In this article, a method based on combined classification is proposed to detect zero-day attacks. One of the most important innovations in this method is using a new version of the GRASP feature selection algorithm, which is used to diversify the base classifiers. In this method, an attempt is made to produce a subset of different features that have high accuracy; and variety to be used in the assembly stage.Experimental results showed that the method used to create feature subsets has high quality.