Machine Learning techniques applied to computer cybersecurity

A lot is heard about Artificial Intelligence, especially one of its most prominent branches, such as “Machine Learning.” However, Artificial Intelligence is not new; It has been with us since the end of the 50s when scientists met in Darthmoud and coined the term in 1956. Today, its influence has reached multiple sectors and areas, among others: the automotive sector, energy, industry, the banking sector, health, cyber defense, and cyber security.

Machine Learning consists of creating models or algorithms to analyze data, learn from it, and predict its possible behavior in time or estimated situations. For these reasons, the cybersecurity industry has not been immune to the growth, dissemination, and installation of techniques to improve computer security, using Machine Learning models and techniques, which allow a more adequate response in line with current requirements. These practices improve and enable an analysis of threats and promise to be more effective in stopping or preventing security incidents. Currently, we find several applications of artificial intelligence, through Machine Learning, in computer cybersecurity, among them: the detection of bank card fraud, intrusion detection, malware classification, and detection of denial of service attacks, for example. List some of them.

It is undeniable that the appearance of the Internet has achieved many advantages and improvements in living conditions for many people. For example, teleworking and virtual education are two areas or sectors that have benefited from the tools and platforms to work at home or study without being immersed in the chaotic and constant problems of transportation and insecurity. Of our big cities.

Machine Learning and e-commerce

Another sector that has benefited from the development and massification of the Internet has undoubtedly been electronic commerce. Companies have been immersed in the need to create new media and communication strategies with their clients, which allow them to obtain the necessary sales volume to improve profits; For reasons like this, e-commerce is an invaluable tool for the sales department of companies. But, on the other hand, just as the benefits and advantages of using the Internet have increased in multiple tools, platforms, consultation sites, financial and banking portals, etc., it is also true that the risks, threats, and possibilities have increased of intrusions, by unscrupulous and ill-intentioned people.

The expansion and accelerated development in communications, the massification of mobile and intelligent devices, and the advancement in technologies such as the Internet of Things (IoT) have increased their importance and complexity; it is there where data science stands with an option to optimize the requirements analysis mechanisms in computer systems and generate a better option against the different types of security risks that exist today.

On the other hand, attacks and intrusions into computer systems, Web sites, and applications continue to increase more frequently, making it essential to use autonomous mechanisms to prevent damage or loss of information. The security of business data, personal data, and mission-critical applications are aspects that organizations must avoid at all costs that are compromised. This is where the constant evolution and improvement in machine learning techniques come into the picture since they take historical or current data into consideration, intending to make predictions or projections of a certain range of data, or in certain periods of time, to be able to establish similarities, in relation to patterns or characteristics of behavior.

It must be taken into account that, thanks to machine learning, a computer system can locate strange behaviors and anomalous situations in large amounts of data, known as patterns. Machine Learning detects unusual situations that want to infiltrate a system network. We can find two possible solutions: Heuristic IDS and rule-based IDS.

Heuristic IDS

The IDS is the intrusion detection system responsible for monitoring a website’s incoming and featured traffic and recording its behavior. It allows supervision that detects suspicious activities and generates alerts upon detection. Based on these alerts, a security operations center (SOC) analyst or incident responder can investigate the problem and take appropriate action to correct the threat. IDS are designed to be deployed in different environments. And like many cybersecurity solutions, an IDS can be host-based or network-based. Now, let’s learn a little more about the different types of IDS.

Host-Based IDS (HIDS): A HIDS is deployed on a particular endpoint designed to protect against internal and external threats. This type of IDS may be able to monitor the computer’s incoming and outgoing network traffic, observe running processes, and inspect system logs. The visibility of a HIDS is limited to its host computer, which decreases the context for decision-making. Still, it has deep visibility into the internal components of the host computer.

Network-Based IDS (NIDS): A NIDS is designed to monitor an entire protected network. It has visibility into all traffic flowing through the network and makes determinations based on the metadata and contents of the packets. This broader view provides greater context and the ability to detect pervasive threats. However, these systems lack visibility into the internal components of the endpoints they protect. A unified threat management solution is recommended, integrating technologies into a single system to provide more comprehensive security. Due to the different levels of visibility, implementing an isolated HIDS or NIDS provides incomplete protection of the threat system for an organization.

IDS detection methods

IDS solutions differ in the way they identify potential intrusions:

Signature detection – Signature-based intrusion detection system solutions use fingerprints of known cyber threats to identify them. Once malware or other malicious content is identified, a signature is generated and added to the list used by the IDS solution to scan incoming content. This allows an IDS to achieve a high threat detection rate without false positives since all alerts are generated based on detecting known malicious content. However, a signature-based IDS is limited to detecting known cyber threats and not detecting vulnerabilities.

Anomaly Detection – Anomaly-based intrusion detection system solutions create a model of the “normal” behavior of the protected system. All future behavior is checked against this model, and any anomalies are tagged as potential cyberthreat and trigger alerts. Although this approach can detect new cyber threats, the difficulty of creating an accurate model of “normal” behavior means that these systems must balance false positives with false negatives.

Hybrid detection – A hybrid IDS uses both signature-based detection and anomaly-based detection. This allows it to detect a greater number of potential attacks with a lower error rate than if either system were used in isolation.

IDS and Firewalls

IDS systems and Firewalls are cybersecurity solutions that can be implemented to protect an Endpoint or a network. However, they differ significantly in their purposes. An IDS is a passive monitoring device that detects potential cyber threats and generates alerts, allowing analysts in an incident response SOC to investigate and respond to the potential incident. However, it does not provide absolute protection for the endpoint or the network. On the other hand, a Firewall is designed to act as a protection system that analyzes the metadata of network packets and allows or blocks traffic based on predefined rules, which creates a limit that certain types of traffic or protocols cannot pass.

In other words, a firewall is an active protection device, more like an intrusion prevention system (IPS). An IPS is like an IDS, except it actively blocks identified cyber threats instead of simply raising an alert. IDS complements the functionality of a Firewall, and many Next-Generation Firewalls (NGFWs) have IDS/IPS capabilities built-in, allowing predefined filtering rules to be applied and detecting and responding to more sophisticated cyber threats (IDS/IPS).

Rule-based IDS

It is the solution that starts from a match with patterns so that the system is capable of detecting them automatically and launching a warning. Some examples are Snort, Suricata, Ossec, Samhain, Bro, or Kismet. All these systems are based on rules that must be pre-configured to work automatically and without supervision. It is also important to remember that they will be as effective as their databases on known threats are updated.

How to choose an IDS solution?

An IDS system is a component that must be present in the cybersecurity implementation of any organization. A simple firewall provides the foundation for network security, but many advanced cyber threats can go unnoticed. An IDS adds an additional line of cyber defense, making it difficult for a cyber attacker to access an organization’s network undetected.

When selecting an IDS, it is important to consider the deployment scenario. In some cases, an intrusion detection system may be the best option for the job, while in others, the built-in protection of an IPS may be a better option. An NGFW with integrated IDS/IPS functionalities provides an integrated solution and simplifies cyber threat detection and security management.

Conclusion

Cyberattacks do not stop happening, and companies must implement different security measures to guarantee the integrity and availability of information and the correct functioning of the entire system. We have the intrusion detection system among those security measures that can be adopted. Many times, among the security tools a company uses, we find mixed systems that combine an IDS with a Firewall.

While both systems monitor and analyze the network and devices for anomalous cyber threats, the main difference between an IDS and an IPS is that the latter can block attacks since it has a preventive and proactive role.

Regarding the firewall, it blocks all traffic, filtering only that traffic or data packets allowed in its configuration. An IDS does the opposite; it lets all traffic through, scanning it for malicious data or activity. Therefore, the IDS and the firewall must work together, with the second filtering allowed traffic and the first analyzing it for threats or anomalies.