Detecting Lateral Movement by Data Analysis

Organizations are learning the hard way that preventive control will never be 100% perfect, and today’s security teams are increasingly being judged on their ability to prevent network intrusion from turning into data loss.

Ensar Seker
Jun 4, 2020 · 4 min read

A major challenge for data breach indicators is the lack of a detailed analysis of data that cannot be easily accessed. Security teams must also refer to a variety of sources of information in order to gain insights.

If you are constantly on the lookout for suspicious activity on your network, such as login activity, many of the actions of logged-in users may not make it through security defenses. Therefore, it is important to monitor logins on pre-attack indicators such as logons hours at strange times of day, as this may indicate lateral movement. By carefully monitoring login activity, you may be able to identify compromises before they occur.

With increasing access to network data, lateral movement becomes even more important. Tracking user behavior may be the key to detecting and combating this behavior, but it is also important to invest in a security system that provides a high level of protection against the lateral movements of the attacker, as security “rips the masks off” the attacker’s movements so that you can protect the network or data in the event of an attack, even if it results in a loss of access.

The key to the fast and reliable detection of lateral movements in the network, therefore, lies not only in data analysis but also in the data itself.

Sideways are the techniques used by cyber attackers to move through the network to look for key data they can target. In many ways, lateral movement in the attack phase is the most important part of a cyber attack on a network and its infrastructure. Lateral movements refer to various techniques used by attackers to spread from a single point of attack, such as a command and control (C&C) server, to another point of an attack.

Typically, they are not worried about being discovered, and most organizations have no staff or tools to detect if something unusual is going on. They use this to gain basic access and then pretend to be legitimate users in order to look for elevated privileges.

The fact that this behaviour is linked to some kind of internal investigation or suspicious behaviour should be an immediate alarm signal. Even if a bad player gains a foothold, it can take days or weeks to identify the weaknesses in the system. The presence of an external person controlling an internal device is something that tools for analyzing network behavior can quickly recognize.

In addition, cross-movement facilitates the theft and reuse of valid user data to spread malware. Needless to say, imitating valid users allows attackers to exploit multiple machines directly and spread across the network in a quieter and more secretive way.

As a result, older security solutions that use appropriate signature heuristics and analyze agent behavior in isolated contexts do not detect threat activity, including, but not limited to, cross-movements of malicious agents within the network. As a result, it is critical for security experts to develop and establish user identity and network intelligence that can detect signs when credentials are misused, used abnormally, or machines exploited.

Moreover, current security solutions cannot detect data exfiltration malware, which prevents companies from properly assessing and controlling the damage that occurs after malware infects a system. This type of detection and security solution does not detect infections caused by dubious or disgruntled employees. The failure of this type of threat activity, which is detected on computers and networks, can cause infected computers on the network to lose productivity, be unable to restore or repair them, and even worse, lose data.

To better understand anomalous user behavior, companies are increasingly adding the ability to find signs of unauthorized activity through the use of advanced analytical tools such as detecting and analyzing anomalies.

Sideways movement is a technique used to identify, access, and exfiltrate sensitive data. Access patterns that differ from colleagues in similar roles, or users who take unusual paths through systems and applications, can signal the presence of an intruder or malicious insider. Lateral movements refer to various techniques that attackers use to spread over a network, leaving a permanent back door for ongoing access.

Attackers use various tools and methods to gain access to high privileges, allowing them to quickly map systems, identify targets, and eventually reach the organization’s crown jewels. Once an attacker is able to secure administrative privileges, it can be extremely difficult to detect malicious lateral movement activity, as it can occur so quickly that security professionals do not have the differentiating capabilities and are overwhelmed by a flood of warnings. Therefore, it is important for security teams to detect and detect lateral movements in order to contain malicious actors before extending their reach to organizations.

Cited Sources

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Ensar Seker

Written by

Cybersecurity | Artificial Intelligence | Blockchain

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +794K followers.

Ensar Seker

Written by

Cybersecurity | Artificial Intelligence | Blockchain

The Startup

Get smarter at building your thing. Follow to join The Startup’s +8 million monthly readers & +794K followers.

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