The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using su...The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.展开更多
Most secure database is the one you know the most. Tamper detection compares the past and present status of the system and produces digital evidence for forensic analysis. Our focus is on different methods or identifi...Most secure database is the one you know the most. Tamper detection compares the past and present status of the system and produces digital evidence for forensic analysis. Our focus is on different methods or identification of different locations in an oracle database for collecting the digital evidence for database tamper detection. Starting with the basics of oracle architecture, continuing with the basic steps of forensic analysis the paper elaborates the extraction of suspicious locations in oracle. As a forensic examiner, collecting digital evidence in a database is a key factor. Planned and a modelled way of examination will lead to a valid detection. Based on the literature survey conducted on different aspects of collecting digital evidence for database tamper detection, the paper proposes a block diagram which may guide a database forensic examiner to obtain the evidences.展开更多
文摘The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.
文摘Most secure database is the one you know the most. Tamper detection compares the past and present status of the system and produces digital evidence for forensic analysis. Our focus is on different methods or identification of different locations in an oracle database for collecting the digital evidence for database tamper detection. Starting with the basics of oracle architecture, continuing with the basic steps of forensic analysis the paper elaborates the extraction of suspicious locations in oracle. As a forensic examiner, collecting digital evidence in a database is a key factor. Planned and a modelled way of examination will lead to a valid detection. Based on the literature survey conducted on different aspects of collecting digital evidence for database tamper detection, the paper proposes a block diagram which may guide a database forensic examiner to obtain the evidences.
