Security is a major challenge to the IT sector these days. Attackers choose the network as a source to target and compromise the data of the organization or any end user. Data from network systems must be collected to identify and detect threats or vulnerabilities and ensure the network’s security. The network data collection is very easy but complex.
Big data analytics, machine learning, and other technologies make gathering and analyzing data over networks easy, but these technologies are very hard to implement and expensive. Only experts can use this technology to give effective results. This scholarly reviewed article explains network data collection, its purpose, and methods of data collection.
Table of Contents
1. What is Network Data Collection?
With the evolution and usage of technology, internet security for networks and data has become difficult. The network systems are targeted by the attackers to get access to data in the systems and applications of the organization. A lot of continental data is stored and generated by end users due to whom risks to data are increasing.
Network data is collected to find the abnormality in the network and detect the attacks over the network. This network data is difficult to gather because of a large volume of data sets. Network engineers and IT teams play an important role in collecting and analyzing the data of the network as they have experience in troubleshooting network issues.
Network data packets and flow processes must be checked first to get information about network threats. Many methods and techniques are present to collect and analyze network data, such as sampling, traffic analysis, detection of similarities, deep learning, machine learning, and other artificial intelligence techniques used today.
2. Purpose Of Collecting Network Data
Intrusion Detection:
Intrusion detection systems are used to monitor and detect malicious activities over the network to protect the system from future threats. It will monitor the behaviour of users over the network, data and the status of the host to ensure the security of the system. Some of the network data used in intrusion detection systems are several network packets, connections, network activities, and the content of packages.
Network Management:
Network management systems are used to diagnose the network, configure it correctly, and detect faults in the network. The network data collected from this system are used to ensure the efficiency of the network connection. Network protocol analyzer and traffic monitoring tools are used to collect the network-related data from this system and ensure security.
Traffic Accounting:
Internet service providers charge users or organizations to access the Internet. They use a subscription process to account for every user’s usage of the internet. They use traffic accounting systems to collect information, statistics, and configurations of users. They also use other techniques to account for the traffic of networks, such as router-oriented systems, LAN, proxy-server-based, firewall-oriented, and so on.
Network Forensics:
Network forensic tools are used to detect and remove attacks and vulnerabilities in the network. They use network data to find the intruder and his intentions of attacking.
Malware Detection:
Malware changes the network packets and behaviours to comprise the data and get access to the system. Network data and traffic analysis will help organizations detect and remove malware from their network and system and protect their company from losses.
There are many other reasons to collect and analyze network data, such as to test and evaluate machine learning algorithms, recognise normal and abnormal activities over the network, detect malicious domains, and be prepared for future attacks done by attackers.
3. Network Data Collection Tools
Collection Nodes:
The collection nodes of the network are routers, gateways, firewalls, honeypots, mobile terminals, servers, switches, and others that are used to connect different systems, mobile devices, and other electronic devices to the network. Router switches send delivery packets to devices. To ensure flexibility, these nodes collect network data. These data can be used to analyze and detect threats.
Collection Tools:
The network collection tools are divided into three categories. they are
- Software-based Data Collection Tools: Network cards, a stack of operating systems, packet-capturing applications, and device drivers are the subsystems of software tools to collect network data. If one of these tools is damaged, all the data will be lost. Libpcap is one of the packet-capturing libraries used mostly to capture network data. The other software tool used to collect network data is Simulation software.
- Network Protocol-based Tools: These tools give complete information about the network systems and help to manage them and diagnose problems effectively. Some of the tools are Simple Network Management Protocol, IPFIX, Telnet, Net Flow, and others.
- Hardware-based Data Collection Tools: These tools are used in intrusion detection systems to get high performance, but these are very expensive to implement. Some of the tools are DAG cards, sensors, hardware probes, port mirroring, inline taps, network interface cards, mobile terminals, firewalls, proxies, honeypots, routers, agents, and so on.
Collection Mechanisms:
Various methods are used to collect data, such as traffic predictions, with the help of artificial intelligence tools to predict the traffic of the network and use this data to find threats. The sampling method is used to collect and analyze only reasonable traffic packets to reduce the complexity of collecting network data.
Every traffic has some characteristics that are used to find the similarities between every traffic and collect the data. ARIMA model is used to find the similarities among traffic and collect necessary data over the network.
4. Conclusion
Network data collection is a trending topic that must be studied to find network vulnerabilities and threats and to ensure the security of the network from intruders. Network security is very important to protect the confidential data of an organization. There are many tools and techniques present to collect and analyze network data, out of which are listed above.
The network data helps to find the culprit, the technique used, and his intentions of attacking. It also helps to protect the organization from future threats and vulnerabilities. Network data ensures the privacy, security, and confidentiality of the sensitive data of an organization.