With the advancement in technology, hackers have also raised the bar to develop the next level of cyberthreats like polymorphic and fileless malware. Traditional security tools, such as signature-based anti-malware, antivirus, or anti-spyware, are no longer helpful in combating these threats. This is where the role of behavior-based security software comes in, which can detect such threats by analyzing and detecting any suspicious behavior, like observing or logging the keystrokes, attempts to make changes to system files, or producing or unpacking further malicious code by itself. The software works on the model of “user and entity behavior” (UEBA), which collects and analyzes the data of every possible finest activity that gives an overview of how the particular entity may behave in the network or system after execution. When any suspicious or malicious behavioral pattern is identified, it is flagged and kept in isolation for further analysis. The traditional signature-based security tools can identify only already-known and listed patterns in the threat, whereas behavior-based security tools can detect those threats that are not yet identified but are suspicious. To protect yourself from modern threats and keep up with the hacker’s advanced tactics, you ought to start considering reliable behavior-based security tools for your organization. Here are the top tools and solutions you can consider to fully secure and safeguard your organization.
Exabeam’s user and entity behavior analytics security solution signifies modern threat detection by using behavioral modeling and machine learning to detect and assess risky activity on the network. It enables IT security teams to quickly detect and respond to cyberattacks and insider threats in real-time. The framework developed by Exabeam is known as the Exabeam Security Management Platform, which solves the problem of inconsistent taxonomy among security analysts and security tools that gives rise to complications due to collaboration during threat detection and investigation. It provides a common framework that can be used by analysts to describe attacker tactics and techniques. Exabeam’s advanced analytics is also useful in detecting lateral movement, which is a method adopted by the cyberattackers to intrude into the network by using IP addresses, credentials, and machines in search of key assets. The advanced analytics technology tracks suspected activities even if there are changes to devices, IP addresses, or credentials. Advanced analytics is also able to determine the owner of a device based on their pattern of behavior and interactions.
Microsoft Advanced Threat Analytics
Microsoft Advanced Threat Analytics is a continuous learning process of the behavior of organizational entities like users, systems, endpoints of the network, devices, resources, etc. It is an on-premises platform that helps protect your organization from potential cybersecurity threats and attacks. The Advanced Threat Analytics (ATA) technology builds a behavioral profile of users and other organizational entities by taking information from multiple data sources such as logs and events in your network and learning their behavior. Any anomaly in that behavioral pattern is detected by ATA technology. It also detects multiple suspicious activities that focus on several phases of the cyberattack kill chain, including reconnaissance, lateral movement cycle, and domain dominance. These phases of a cyberattack are similar and predictable. The three main types of attacks that are searched and prevented by ATA are malicious attacks, abnormal behavior, and security issues and risks.
Malicious attacks are detected and logged in the ATA console, including a clear view of who, what, when, and how of every potential threat. Abnormal behavior is detected by ATA using behavioral analytics and machine learning to detect anomalies in the behavior of users and devices in the network. ATA also detects security issues and risks, including broken trust, weak protocols, and known protocol vulnerabilities.
The Interset platform is a combination of connectors and sensors that collect specific metadata from enterprise applications, existing security systems, and network endpoints. The collected metadata is then aggregated by the platform, and a correlation between users, their devices, applications, and files is developed. Interset’s Adaptive Entity Analytics (AEA) engine capabilities are combined with machine learning techniques to detect anomalies and identify threats. By measuring “unique normal” with contextual intelligence, Interset’s UEBA solution creates an integrated view of cybersecurity risk generated by users, machines, files, IP addresses, projects, resources, services, shares, websites, volumes, and printers. Unique normal is the individual digital footprint of each entity. The unique normal is also continuously measured for the relationship between the aforementioned entities. The baseline of unique normal can then be continuously compared to itself over time to see anomalies. The accurate method for measurement of unique normal across an enterprise requires unsupervised machine learning technology, which does not require labels (for example, a dictionary for the machine to learn from).
To get a complete picture of cybersecurity risks, Bay Dynamics leverages UEBA capabilities, combined with its unique technology to detect and expose deviations from the actual pattern that provides a clear visualization for risk identification and prioritization. The Risk Fabric Platform offers several capabilities: threat matrix, risk analysis, user profiling, kill chain analysis, and improving policy effectiveness:
- Threat matrix identifies and prioritizes threats to accelerate investigation and response.
- Risk analysis isolates emerging threats, analyzes distributed risk vectors, and hunts down known threats.
- User profiling investigates users and entities to unearth real threats.
- Kill chain analysis visualizes emerging threats and their impact and stops known malicious threats to prevent user accounts.
- Policy effectiveness is responsible for the identification of problematic policies and employing intelligent remediation.
Securonix is a next-gen security information and event management (SIEM) solution that collects, detects, and responds to threats using a single, scalable platform based on machine learning and behavioral analytics. Built on Hadoop, Securonix is a cloud-based software-as-a-service (SaaS) solution that uses signature-less sophisticated machine learning algorithms to track data in real-time to accurately detect advanced and insider threats. It utilizes multiple algorithms working in harmony to identify and detect potential attacks launched from within or outside the organization. Securonix’s offerings also include “response bot,” an artificial intelligence-based recommendation engine that suggests remediation actions based on the previous behavior patterns of Tier 3 analysts.
Preempt Security delivers a modern approach to authentication and securing identity in the enterprise. It is a conditional-access platform that continuously analyzes, adapts, and responds to threats based on identity, behavior, and risk. Usually, organizations do not track activities such as who, when, how, where, and what is being accessed across multiple security solutions and platforms. Preempt makes it easier to do this by auto-discovering all users, privileges, accounts, devices, and behavioral access patterns, whether on-premises, in the cloud, or hybrid environments. By learning the behavior of all users, system accounts, and all endpoints in the network, Preempt develops a risk score for every entity and establishes real-time behavior-based policies to detect and eliminate threats with the least manual intervention. Trusted and untrusted access is recorded through analysis of live authentication traffic combined with SSO, cloud directories, and VPN, via supervised and unsupervised learning. Preempt works on a two-tier architecture:
- Centralized management system deployed either on-premises or on AWS/Azure.
- Sensors that are distributed across the network in either passive or active mode to enable real-time threat detection and prevention.
Boost your defenses with behavior-based security tools
Hackers keep trying to find new and innovative ways to get into the systems, and traditional signature-based anti-malware and firewalls are not going to be 100 percent foolproof. UEBA is a relatively new option that can be adopted by organizations that want to add advanced analytics or machine learning capabilities to their IT security arsenal. But it cannot be said that new behavior-based tools and systems are going to replace the traditional security systems entirely. Both signature-based and behavior-based malware detection techniques have their strengths and weaknesses. For instance, for prevention against massive floods of known threat-based cyberattacks, signature-based firewalls, and anti-malware solutions acts as a giant wall of defense, without having any major negative impact on the network performance. So, having the right combination of both technologies can help attain the optimal layers of security to withstand all sorts of cyberattacks.
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