ExeonTrace | Breaking Cybersecurity News | The Hacker News

Here's How to Enhance Your Cyber Resilience with CVSS In late 2023, the Common Vulnerability Scoring System (CVSS) v4.0 was unveiled, succeeding the eight-year-old CVSS v3.0, with the aim to enhance vulnerability assessment for both industry and the public. This latest version introduces additional metrics like safety and automation to address criticism of lacking granularity while presenting a revised scoring system for a more comprehensive evaluation. It further emphasizes the importance of considering environmental and threat metrics alongside the base score to assess vulnerabilities accurately. Why Does It Matter? The primary purpose of the CVSS is to evaluate the risk associated with a vulnerability. Some vulnerabilities, particularly those found in network products, present a clear and significant risk as unauthenticated attackers can easily exploit them to gain remote control over affected systems. These vulnerabilities have frequently been exploited over the years, ...

Did you know that Network Detection and Response (NDR) has become the most effective technology to detect cyber threats? In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false alerts and efficient threat response. Are you aware of  Network Detection and Response (NDR)  and how it's become the most effective technology to detect cyber threats?  NDR massively upgrades your security through risk-based alerting, prioritizing alerts based on the potential risk to your organization's systems and data. How? Well, NDR's real-time analysis, machine learning, and threat intelligence provide immediate detection, reducing alert fatigue and enabling better decision-making. In contrast to SIEM, NDR offers adaptive cybersecurity with reduced false positives and efficient threat response. Why Use Risk-Based Alerting? Risk-based alerting is an approach where security alerts and responses are prioritized based on the level of risk they pose to an organization's syst...

CrowdStrike is named a Leader in the 2024 Gartner® Magic Quadrant™ for Endpoint Protection Platforms for the fifth consecutive time, positioned highest on Ability to Execute and furthest to the right on Completeness of Vision.

While IT security managers in companies and public administrations rely on the concept of Zero Trust, APTS (Advanced Persistent Threats) are putting its practical effectiveness to the test. Analysts, on the other hand, understand that Zero Trust can only be achieved with comprehensive insight into one's own network.  Just recently, an attack believed to be perpetrated by the Chinese hacker group  Storm-0558  targeted several government agencies. They used fake digital authentication tokens to access webmail accounts running on Microsoft's Outlook service. In this incident, the attackers stole a signing key from Microsoft, enabling them to issue functional access tokens for Outlook Web Access (OWA) and Outlook.com and to download emails and attachments. Due to a plausibility check error, the digital signature, which was only intended for private customer accounts (MSA), also worked in the Azure Active Directory for business customers.  Embracing the Zero Trust Revo...

Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table. Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity a...

As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called "needle in the haystack". With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad. The Role of ML-Driven Network Security Solutions ...