Invented by Robert Winslow Pratt, Ravi Prasad Bulusu, Splunk Inc
The market for identifying threat indicators through processing multiple anomalies is growing rapidly. This market includes a variety of different products and services, including threat intelligence platforms, security information and event management (SIEM) systems, and advanced analytics tools.
Threat intelligence platforms are designed to collect and analyze data from a variety of sources, including social media, dark web forums, and other online sources. These platforms use advanced algorithms to identify patterns and anomalies that may indicate a potential threat. They can also provide real-time alerts and notifications to security teams, allowing them to respond quickly to potential threats.
SIEM systems are another important tool for identifying threat indicators. These systems collect and analyze data from a variety of sources, including network logs, firewall logs, and other security-related data. They use advanced analytics to identify patterns and anomalies that may indicate a potential threat. They can also provide real-time alerts and notifications to security teams, allowing them to respond quickly to potential threats.
Advanced analytics tools are also becoming increasingly important in the market for identifying threat indicators. These tools use machine learning algorithms to analyze large amounts of data and identify patterns and anomalies that may indicate a potential threat. They can also provide real-time alerts and notifications to security teams, allowing them to respond quickly to potential threats.
Overall, the market for identifying threat indicators through processing multiple anomalies is growing rapidly. As the threat landscape continues to evolve, it’s essential for organizations to have effective measures in place to protect against potential threats. By investing in advanced threat intelligence platforms, SIEM systems, and advanced analytics tools, organizations can stay one step ahead of potential threats and protect their sensitive information from cybercriminals.
The Splunk Inc invention works as follows
Techniques for processing anomalies using user-specified rules and anomalies detected by machine-learning-based behavioral analysis models to detect threat indicators and security threats to computer networks are described. A method for detecting anomalies is to process event data from a network security system that uses rules-based anomaly detection. A network security system using machine-learning-based anomaly detection acquires these rules-based anomalies. Machine learning anomalies and rules-based anomalies are combined to detect security threats or threat indicators to the computer network. Alerts are sent to the network security system using rules-based anomaly detection containing security threats and threat indicators.
Background for Identifying threat indicators through processing multiple anomalies
Computer network administrators have long considered “Activity detection”, both benign and malicious, a top priority. Users use a variety of devices, including desktop computers, laptops, tablets, smart phone, and browsers in various computer networks. Interact with other users through computers and servers that are connected to the network. Interconnected network devices transmit digital data, usually in the form data packets, along the network.
Malicious activity can cause damage to network hardware or users who use it. Unauthorized access to network resources or data, as well as subsequent unpermitted uses of them, are examples of malicious activities. Network administrators are trained to spot suspicious behavior and seek out patterns that may be different from the expected use patterns of certain entities. This includes individuals, groups, organizations, IP addresses, nodes, or groups of nodes within the network. Network administrators can use hardware appliances to monitor network traffic and software products such as anti-virus and anti-malware software, to combat these activities.
In this description, there are references to?an embodiment? ?one embodiment? or similar, means that at least one embodiment contains the specific feature, function, structure, or characteristic described. These phrases may not all be used to refer to the same embodiment. However, the embodiments referred too are not necessarily mutually exclusive.
Modern data centers and other computing environments may contain a small number of host computers or thousands of systems that can process data and service remote clients. Many components in these computing environments can generate large amounts of machine-generated information during operation. Machine data can be generated by many components of the information technology environment (IT), such as servers and sensors, routers, mobile devices and Internet of Things devices (IoT). Machine-generated data includes system logs and network packet data, sensor information, application program data as well as error logs, stack trace, system performance data, and system logs. Machine-generated data may also include diagnostic information and performance data.
Machine-generated data can be analyzed with a variety of tools. Many of these tools pre-process data to reduce the amount of potentially large amounts of machine data. To facilitate the retrieval and analysis of data items during search time, pre-specified items can be extracted from machine data and stored in a database. The rest of the data is usually not saved or discarded during pre-processing. There are many reasons to keep more data, as storage capacity is becoming more affordable and more readily available.
This abundant storage capacity makes it possible to store large quantities of minimally processed data for later retrieval or analysis. An analyst can search the entire machine data at once, rather than focusing on a specific set of items. This allows for greater flexibility and allows them to store minimally processed data. An analyst may be able to examine different aspects of the data, which could allow them to analyze other parts.
However, it is difficult to analyze and search large amounts of machine data. A data center, server, or network appliance may produce many types and formats (e.g. system logs, packet data (e.g. wire data), etc. ), sensor data, application program data, error logs, stack traces, system performance data, operating system data, virtualization data, etc.) There are thousands of components that can make it difficult to analyze. Mobile devices can also generate large amounts information about data accesses and network performance. These types of information can be reported by millions of mobile devices.
In certain cases, machine data may have a predefined structure, in which data items with particular data formats are stored at specific locations within the data. Machine data could include data stored in fields within a database table. Machine data may also not be in a predefined format. This means that the data is not stored at a fixed location, but it does not follow a predictable pattern and is not random. Machine data may contain a variety of data items from different types, and these data items can be stored in different places within the data. An example is that if the data source is an Operating System Log, an event may include one or more lines of the log that contain raw data. This data can include diagnostic information and performance data associated with a particular point in time.
Examples that may generate machine data, from which events can be deduced, include web servers, application server, databases, firewalls and routers, operating system, and software programs that run on computers, mobile devices, or sensors. Internet of Things (IoT), devices, etc. Data generated from such data sources may include, but are not limited to, server log files and activity log files, configuration file files, messages, network data, performance measurements, sensor measurement, and so forth.
In certain embodiments, a common name for a field may be used to refer to two or more fields that contain equivalent data items. However, the fields could be associated with different types events, which may have different data formats and differing extraction rules. The system allows for a common name to be used to identify identical fields from different types events generated by disparate sources. This facilitates the use of a ‘common information model? (CIM) across disparate data sources.
Overview of Anomaly Detection in Identifying Network Security Threats
These are methods for processing data for network security purposes. The techniques described here can be used for any type of application, including security applications, security information, and event management (SIEM), applications. Automated machine-data-based fraud detection systems can, for example, implement at least some of these techniques. The techniques described here can be used in security-related anomaly detection and threat detection, but they can also be used with any behavioral or rules-based analysis (e.g. fraud detection or environmental monitoring systems that process data).
Users with trusted access can attack various types of networks and businesses. These attacks are often not detected by security systems and products. Traditional security products are often limited in their ability to detect new threats and insider threats as well as in their ability to scale up or process large amounts of data. Attackers don’t often need additional malware to gain access to the target system. Attacks that rely on seemingly valid access are therefore difficult to identify, correct, or remedy in a timely fashion.
The patterns of these malicious actions vary dynamically and attackers can almost always find a way to evade traditional security technology such as rules-driven Malware detection, malicious file signature comparision, and Sandboxing. In certain cases, human analysis of data can still provide valuable insights. The problem is that human analysis of data becomes more time-consuming and costly as it grows in volume. Machine-learning-based behavioral analysis techniques may not be able to detect threats in certain cases that would have been obvious or anticipated by a human.
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