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There’s bad news and not-so-bad news in the world of cybersecurity. In a recent report, Google revealed a significant rise in zero-day attacks, those exploiting previously unknown software vulnerabilities. However, despite the increase, there’s a silver lining.
Hackathon events like Pwn2Own Automotive 2024 play a crucial role in shedding light on zero-day attacks and significantly benefit security teams in several ways. These events bring together talented security researchers, ethical hackers, and industry professionals to uncover and demonstrate vulnerabilities in automotive technologies, including zero-day exploits.
“Zero-day Summer” refers to the period during summertime when cybercriminals take advantage of the vulnerabilities in software, applications, and computer systems that remain undetected and unpatched by security teams.
We discuss a real-life example of a zero-day exploit, focusing on the MOVEit Transfer Software, and how MixMode can detect and mitigate such threats before they become widespread.
Zero-day attacks are considered the number one cybersecurity threat to company networks large and small. Understand what they are, why it’s so hard to detect them, and how artificial intelligence (AI) is helping to solve this modern problem.
Hosted by Mark Ehr, Senior Consulting Analyst for 451 Research Advisors and Igor Mezic, Chief Scientist and CTO for MixMode on Tuesday, November 1st at 1pm EST / 10am PST, they will discuss why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.
Of all the challenges facing the modern SOC, two stand out among the most persistent and difficult to overcome: an overwhelming volume of false positive alerts and an alarming escalation in zero-day and novel threats that are often imperceptible by legacy systems.
The number of zero-day exploit discoveries hit record numbers in 2021, according to Google Project Zero. Instances of “in-the-wild 0-days” were up nearly double versus 2020, when only 25 0-day exploits were detected. In 2021, the total was 58.
Chief Scientist and CTO for MixMode, Igor Mezic, wrote this article for Forbes magazine on the advantages of moving away from a legacy rule-based cybersecurity platform to a third-wave AI platform that can better detect zero-day threats.
Log4j is the latest example of a zero-day exploit to be discovered and put a big part of the industry into chaos. Given its wide adoption by developers, the impact of the log4j exploit is quite broad and will take a tremendous amount of time to resolve.
Just like the SOC analyst, the TVA has to find these threats and remove them as quickly as possible. Let’s say you are tasked with being this TVA agent and are told you need to search all the timelines (an almost infinite number of them) and find all the Loki variants to have them removed.
Zero-day attacks are among the most challenging Cybersecurity threats SOC teams face on a regular basis. These “never before seen” threats can surprise organizations even if they are protected by traditional Cybersecurity systems.
The next cybersecurity challenge lies with the advances in quantum computing that are set to revolutionize tech while simultaneously equipping threat actors with a new arsenal of cyberweapons.
When it comes to advancements in cybersecurity, rule-based systems are holding the industry back. Relying on humans to constantly input and label rules in order to detect and stay ahead of threats is a bottleneck process that is setting security teams up for failure, especially with tools like SIEM, NDR, and NTA.
Although it is not surprising at all that hackers are taking advantage of the global pandemic —phishing threat reports are always highest when there is some natural disaster happening— we have never before had such an unsafe environment to protect. Here are a few of the most popular malicious acts:
While it’s true that having a SIEM is better than forgoing network monitoring all together, a standalone SIEM solution is simply insufficient in today’s cybersecurity landscape. Hackers and other bad actors have become more sophisticated — many of today’s cybercriminals can easily outsmart a standard SIEM setup.
Advances in Artificial Intelligence (AI) have led to smarter, more robust network security platforms that are quickly replacing legacy security solutions.
Knowing the difference between Discriminative and Generative Unsupervised Learning can tell you a lot about the effectiveness of a cybersecurity solution’s artificial intelligence, for example, whether or not that security solution can perform actions like identifying and stopping a zero-day attack.
In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.
Zero-day attacks cost businesses millions of dollars in lost revenue and recovery costs and can cripple a company that is not prepared to respond decisively and effectively.