Cybren is using a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks.
Business impact analysis is the most important technique to determine critical functions and applications in the business environment by evaluating the impact of cybersecurity incidents on the business. Cybren AI techniques can be used to automate the business impact analysis by evaluating the economic risks based on a known attack vector or by calculating the threat feasibility along with the probability of high-impact security events on critical business areas.
It is an important step to identify the vulnerabilities of applications, servers or other systems and assets of an organization. Our Researchers team have worked on the software vulnerability detection by checking the source code using deep learning and transfer learning.These actions employ text-mining techniques to feed the machine learning based vulnerability detection models in union with a recommendation system to help programmers to write secure code.
We use a proactive security search across networks, endpoints and datasets to detect potentially malicious, suspicious or risky activities within an organization. It identifies and categorizes prospective threats in advance using fresh threat intelligence on data that has already been gathered.
Attack path modelling is a proactive risk reducing approach that supports security teams by mapping the vulnerable routes in the network to assess risk, identify vulnerabilities, and take counter measures to protect key assets. Our Researchers team is using AI techniques for path modelling using intrusion alerts or vulnerability descriptions.
Predictive intelligence is intelligence that is actionable and relevant in a given context and can be used to anticipate attacks. Our Intrusion prediction tools are helpful in providing an active defence against future attacks by predicting the type, intensity and target of an intrusion in advance. Out Researchers team are using deep-learning approaches to forecast the alerts from malicious sources [or on a given target,using the sequence of previous alerts, historic spam e-mail, and network traffic data.
Cyber supply chain security requires a secure integrated network between the incoming and outgoing chain's subsystems. Therefore, it is essential to understand and predict threats using both internal and threat intelligence resources to limit the disruption of the business. Cybren has integrated cyber threat intelligence data and used machine learning techniques to predict cyberattack patterns on cyber supply chain systems.
The protect function helps plan and implement appropriate controls to limit or contain the impact of a potential cybersecurity event. This includes a number of technical and procedural controls to proactively protect against internal and external cyber threats. Our AI models can improve the resilience of the system by authenticating users, devices and other assets, monitoring the user behaviour, automated access control, adaptive training, data leakage prevention & integrity monitoring, automated information protection and processes and provision of protective solutions to proactively secure the system.
Intelligent e-mail protection is our class of software solutions to prevent sophisticated e-mail focused cyberattacks. Traditionally, a spam e-mail was used as a tactic for hawking goods and services by sending unsolicited e-mails to bulk lists. Today, however, it is actively being used to spread malware, steal authentication credentials or commit financial frauds. Our AI techniques are being used for automated protection against malicious spam e-mails.
The detection of malicious websites by our AI models works by training machine learning algorithms with a rich collection of malicious and non-malicious website features. These features can be divided into four main categories: website design features, domain-based features, URL-based features, and hybrid features.
Our team designed a dynamic backup system with intelligent scheduling algorithims to improve the stability and predictability of the backup environment. The proposed system schedules the backup efficiently by determining which backup starts first and which storage is assigned to that backup for improving the efficiency
Our researchers team has tested the performance of a variety of supervised machine learning approaches for detecting the evidence of malicious remote desktop protocol (RDP) sessions using windows RDP event logs. We propose a new approach for data presentation using a storytelling technique to generate a natural language report for recognizing cyber threat information according to the users’ level of knowledge.
Our Intrusion prevention systems monitor the network traffic and then take an appropriate action to thwart the attack by reporting, blocking, dropping or resetting the connection. Our Researchers team has proposed unsupervised isolation forest, self-organizing incremental neural networks and SVM based intrusion prevention systems for embedded systems in automotive electronics and IoT networks, respectively.
Our Researchers team have created anti-virus programs for detecting malware using the features retrieved from the executables or dynamic data analysis as an input to artificial neural networks (ANNs) or recurrent neural network (RNN) models, respectively.
Protection by deception is an advanced technique to protect critical documents after an attacker has penetrated the network. We are using AI models to generate credible fake text documents to mislead cyberattacks. We proposed the creation of decoy files to divert the adversary away from the real target when the adversary is already in the system. Their decoy text-creation approach uses a genetic algorithm that manipulates real document's comprehensibility to hard-to-comprehend, but believable fake documents.
Our Multi-category classification, models deal with the problem of classification into three or more classes. In the case of IDSs, the multi-category classification differentiates between different types of attacks and provides users with more information to deal with the attack.
We present an automated, situational awareness model that uses real-time awareness features provided by the software-defined network (SDN) paradigm to perform a vulnerability assessment on network-enabled entities, their assignment to a connectivity appropriate slice and the continuous monitoring of the underlying infrastructure.
Our Automated analysis tools are used to identify the potential threats by analysing the language and targets of hackers without manually monitoring the large volume of posts made on the dark web. We use Sentiment analysis to automate the mining of opinion, views and emotions from the text using natural language processing (NLP).
Our AI models run an automated assessment of different threat intelligence sources will help extract useful information from various sources, such as vulnerability databases, twitter, news sites, incident reports, and research articles to take timely actions to ensure the overall security of the system. This involves processing evidence-based knowledge from multiple sources about threats and actors to improve security and the decision-making process
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