Intrusion detection system (IDS) provides an important basis for the network defense. Due to the development of the cloud computing and social network, massive amounts of data are generated, which inevitably brings much pressure to IDS. And therefore, it becomes crucial to efficiently divide the data into different classes over big data according to data features. Moreover, we can further determine whether one is normal behavior or not based on the classes information. Although the clustering approach based on K-means for IDS has been well studied, unfortunately directly using it in big data environment may suffer from inappropriateness. On the one hand, the efficiency of data clustering needs to be improved. On the other hand, differ from the classification, there is no unified evaluation indicator for clustering issue, and thus, it is necessary to study which indicator is more suitable for evaluating the clustering results of IDS. In this paper, we propose a clustering method for IDS based on Mini Batch K-means combined with principal component analysis. First, a preprocessing method is proposed to digitize the strings and then the data set is normalized so as to improve the clustering efficiency. Second, the principal component analysis method is used to reduce the dimension of the processed data set aiming to further improve the clustering efficiency, and then mini batch K-means method is used for data clustering. More specifically, we use K-means++ to initialize the centers of cluster in order to avoid the algorithm getting into the local optimum, in addition, we choose the Calsski Harabasz indicator so that the clustering result is more easily determined. Compared with the other methods, the experimental results and the time complexity analysis show that our proposed method is effective and efficient. Above all, our proposed clustering method can be used for IDS over big data environment.

Fog computing, as the supplement of cloud computing, can provide low‐latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty‐two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs‐oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi‐MU offloading scheme. Then we survey edge server‐ (ES‐) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved Affinity Propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.