Security monitoring of heterogeneous networks for big data based on distributed association algorithm Abstract   With the continuous development of heterogeneous networks, the structure and scale of the network are becoming more and more complex, and the difficulty of processing network alarm information is also increasing. Therefore, an efficient and flexible alarm handling scheme will become especially important in the alarm management of heterogeneous networks. In the aspect of alarm analysis of heterogeneous networks, this paper proposes a corresponding distributed alarm analysis model and corresponding distributed association analysis algorithm. Combined with the structural characteristics of heterogeneous networks, the proposed distributed analysis model has many potential advantages. One of them is the significant reduction in the candidate set in the alarm database, which can greatly help us improve the efficiency of alarm correlation analysis in heterogeneous networks, thereby further reducing the time required for our alarm correlation analysis. In many respects, such improvement provides good technical support for the management of alarm information in heterogeneous networks. Finally, the simulation verification of the corresponding algorithm is given. The results show that under different support thresholds, the distributed association analysis algorithm has shorter running time.

DCSA: Using DensityBased Clustering and Sequential Association Analysis to Predict Alarms in Telecommunication Networks Traditional alarm prediction in telecommunication networks mainly relies on expert knowledge. However, with the increasing complexity of telecommunication network, the traditional methods may not work well. Itu0027s necessary to study new automatic association rules extraction methods. In this paper, we proposed a method called DCSA (Densitybased Clustering and Sequential Association Analysis) for alarm association rules mining. We use time densitybased clustering and FPGrowth algorithm to mine the association rules in alarm data, which overcomes the drawbacks of sliding windows method. In addition, we design a sequential rules filtering module to eliminate the items that do not meet the sequential conditions in the original rules. Experiments on 7.5 million real alarm items from a telecommunication company of China show the sequential rules filtering module can greatly reduce the redundancy of association rules. We also demonstrate that the proposed DCSA method could effectively predict the occurrence of alarms.