Unlocking Cybersecurity with Behavior Analysis
By Tom Seest
At BestCybersecurityNews, we help entrepreneurs, solopreneurs, young learners, and seniors learn more about cybersecurity.
Network behavior analysis (NBA) is a security solution that uses machine learning to detect unusual patterns in data traffic. It’s designed to assist organizations in improving their cybersecurity posture and guard against cyberattacks.
Security personnel can utilize end-user activity monitoring software to detect suspicious end-user activity that could indicate an attacker is present on the company network. Furthermore, it provides a baseline of normal user behavior, making it simpler to spot inconsistencies.
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Network behavior analysis (NBA) is an emerging cybersecurity approach that allows companies to monitor network data traffic for signs of malicious activity. It helps organizations maintain a high level of security through automation and round-the-clock monitoring that gives visibility into suspicious data as it occurs.
Behavior-based security solutions utilize computational mathematics and machine learning to assess data stream behaviors and detect anomalies that are statistically significant compared to a baseline of usual behavior. They can detect unusual data patterns like hidden worms or malware within normal network traffic.
Behavioral analysis techniques can also be effective for detecting zero-day vulnerabilities and malware since they are able to spot threats that traditional signature-based security tools might overlook. For instance, cryptojacking attacks present a unique danger that might go undetected due to a lack of signatures in their databases.
Dynamic malware analysis utilizes an automated sandbox to run a suspected malicious file in a secure environment without harming either the system or the network. This allows security teams to observe the malware in action and gain insight into its true nature.
Furthermore, static malware analysis is another technique that detects signs of malicious intent in files. With this approach, technical indicators like file names, hashes, strings, IP addresses, domains, and file header data can be identified and used to assess a piece of malware.
The ability to detect a shift in user behavior allows organizations to alert to potential abuse by privileged users or unauthorized employees. This is essential for safeguarding business-critical data and complying with evolving data privacy and security regulations such as GDPR or PCI-DSS.
Behavioral analysis is an invaluable cybersecurity technique, as it fills in any gaps left by other security measures like firewalls and spyware detection tools. It may also detect anomalies other security solutions might overlook, like an abrupt spike in bandwidth usage or a previously undetected packet that could indicate malware activity.
As cyberattacks become more sophisticated, businesses require more advanced cybersecurity tools to safeguard their systems and data. Common solutions include behavior analysis – which identifies abnormal network or user behaviors to alert potential threats.
Behavior analysis, as defined by the American Psychological Association, is “the scientific study of human behavior as a means for learning and change” (Division 25). This field plays a particularly significant role in cybersecurity, where hackers can impersonate employees and access corporate networks without detection.
Intruders can steal information, damage equipment, and cause financial loss. A behavioral analytics solution that tracks employee behavior can help identify these individuals and prevent them from committing further crimes.
Behavior analysis encompasses two methods of intrusion detection: signature-based and anomaly-based. An anomaly-based IDS utilizes machine learning algorithms to detect suspicious network traffic that doesn’t match known malware signatures. These types of IDS are ideal for spotting zero-day attacks since they can identify new malware that hasn’t been seen before.
Another type of behavior monitoring is user and entity behavior analysis (UEBA), which focuses on both users and devices/servers. UEBA allows security teams to detect unusual or malicious activity not flagged by traditional perimeter protection systems, saving time and resources in the process.
UEBA detects changes in user behavior that may indicate an attacker’s intent to steal or misuse privileges. It also assesses how frequently someone accesses applications and assets to detect potentially suspicious activities.
The American Psychological Association (APA) describes UEBA as “a tool for behavioral analysis of entities.” This type of cybersecurity software is more powerful than user behavior monitoring, as it monitors behaviors from multiple sources to detect suspicious activities. Furthermore, UEBA can connect these activities to an individual user rather than just a device or IP address, making it easier to recognize insider threats and distinguish whether they represent genuine employees or external parties posing as employees.
Many behavior analytics tools integrate with other cybersecurity solutions to offer a complete solution. SolarWinds Security Event Manager (SEM) serves as both an intrusion detection system and a high-intensity intrusion prevention system, comparing sequences of network traffic against predefined rules and alerting IT administrators if there’s suspicion that the traffic poses a threat.
Cyber security anomaly detection is the process of recognizing and classifying data points that deviate from normal behavior. This step in data mining is essential because anomalies can indicate a range of issues, such as website hacks, bank frauds, technical bugs, structural flaws, or business-altering changes in customer behavior.
Different anomaly detection techniques exist, some focusing on global patterns while others on local outliers. These patterns may include unexpected events like sudden spikes in activity or dramatic drops in revenue.
Effective anomaly detection algorithms must not only assess each alert based on its statistical significance but also adjust to new threats as they emerge. This means they should evolve slowly over time to incorporate emerging patterns before analysts keep labeling them as anomalies and retrain completely new models when major shifts are detected.
Anomaly detection algorithms should also be able to distinguish between outliers and noise. This distinction is crucial since noise can be indicative of an underlying issue but also suggests the system is neglecting something obvious.
Some anomaly detection techniques utilize graphs to spot outliers, while other approaches rely on machine learning algorithms. For instance, ML systems can detect irregular changes in consumer spending patterns to help prevent online banking fraudulence.
Another approach is to utilize network behavior anomaly detection, which scans traffic flow patterns and packet signatures for potential threats. By detecting suspicious network behaviors, organizations can minimize the impact of malicious attacks and reduce the time spent responding to such issues.
Anomaly detection not only alerts potential risks, but it can also enhance network performance and minimize maintenance requirements to guarantee a company’s technology is running optimally. This helps minimize operational expenses, avoid downtime due to unexpected issues and allows businesses to adhere to regulatory guidelines.
Anomaly detection can also assist companies in tracking critical issues, such as website hacks, bank or insurance frauds, technical bugs, network errors, structural defects, and business-altering changes in customer behavior. This allows companies to detect and address these problems before they lead to costly losses and reputational harm.
Malware is a type of software that can be used to steal data, encrypt sensitive files, spy on computer systems, and disrupt essential business operations. Many common malware infections occur through social engineering techniques like coercing users into clicking links in malicious email attachments or drive-by downloads, as well as vulnerabilities in software installed on a user’s device.
Network behavior analysis (NBA) is an effective means of thwarting malicious activity by continuously monitoring data streams in real-time and comparing them against a baseline of expected behavior. This gives security teams with an effective means of detecting and stopping threats before they spread across the network.
Companies can create benchmarks for network traffic and detect when something deviates from these standards, enabling them to take action before causing any significant harm. For instance, if an employee signs into the system outside regular hours, this might be cause for alarm and require further investigation.
Behavioral analytics can also be employed to protect against advanced malware attacks that alter their signatures in order to avoid detection. These attacks typically employ evasion techniques such as time delays, device fingerprinting, and recent file detection in order to make them harder to identify and target.
Another effective way to prevent malware attacks is adhering to the principle of least privilege, which means users only have the permissions necessary for them to perform their job responsibilities. Doing this can guard against hackers gaining access to critical network resources.
Additionally, employees should be taught how to detect malware and take appropriate action if they believe their device has been infected. This could involve altering default operating system policies, using app-based multi-factor authentication, or installing anti-malware and spam filters.
Unfortunately, there is no guaranteed way to completely prevent malware attacks, but behavior analysis can give businesses a better chance at avoiding them before they happen. It also speeds up the investigation process by identifying where malicious traffic originates and passes through. This is especially useful in compliance-related investigations since regulations such as the European Union’s General Data Protection Regulation (EU GDPR) impose severe fines for not adhering to security protocols.
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