Polymorphic malware has been around since the early 1990s, but it’s still wreaking havoc in our computers and networks. SC Magazine recently reported on a particularly nasty strain of polymorphic malware that, according to the article, “is able to evade over 75 percent of antivirus engines tested.” That’s a very disturbing statistic.
Organizations across all vertical markets are dealing with the effects of shadow IT, whether they realize it or not. Shadow IT is technology that is adopted and deployed by individual employees or business units without the knowledge or consent of corporate IT teams. The popularity of SaaS applications and services has specifically enabled shadow IT to grow at an impressive rate, fueled in part by its ease of purchase and deployment. According to a recent survey, 72 percent of executives are unaware of how many shadow applications are in use within their organization.
For financial services firms, the risks of shadow IT are amplified due to the value of the data their organizations possess, and the strict regulatory standards with which they must comply. As the adoption of shadow IT continues to grow, financial services firms have to be aware of the risks associated with it, as well as ways to mitigate its risks without impacting network performance.
In a group test by NSS Labs that included detecting malware engineered to avoid detection, Lastline Enterprise detected every threat NSS Labs threw at it --something that FireEye couldn’t do.
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Organizations require better connections to branch offices to deliver higher-quality network services. SD-WAN is more agile and cost-effective than traditional networking solutions, but there are challenges, especially around security.
Gartner’s research note explores the challenges that are transforming SD-WAN and discusses four SD-WAN architectures. For all the details, download the research note today.
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What's driving cyber security today? The answer is simple – cyber crime.
Financially motivated attacks have security organizations scrambling for resources and technologies to keep up defenses. But gaps still exist.
Read Radware's 2017-2018 Global Application & Network Security Report to learn more about:
Bitcoin & Ransom Attacks – with a 40% increase in ransom attacks from 2016 and Bitcoin valuations spiking – be prepared.
IoT Threats Emerge – but no one claims responsibility for the security of these devices. Botnets will keep increasing while the ownership debate continues.
Data Protection reigns as the number one business concern two years in a row. We examine how to prevent data leakage as GDPR also looms.
Targeted Organizations Will Suffer Legal Action or customer churn following an attack in nearly 25% of cases.
AI & Machine Learning to the Rescue? Maybe so with 20% already trying it on for size and another 28% planning to. But is it fail-safe?
It’s been famously said that there are only two types of companies: those who know they have been breached, and those who don’t know it yet.
Here is what you’re up against (not good odds):
Per cloud security’s shared responsibility model, Office 365 customers are responsible for actions users take within the platform that compromise data, and Skyhigh has found the average enterprise experiences 2.7 such threats in the platform each month. This number includes compromised accounts, insider threats, and privileged user threats.
Sandboxes are very good at detecting malware. However, some sandbox architectures are significantly more effective than others when it comes to identifying the more advanced strains of malware.
The idea behind a sandbox is simple—it’s an isolated, secure environment to open a file and determine if it is either benign or malicious by monitoring and analyzing its behavior. The sandbox allows the program to execute and perform all of its operations, which are monitored and recorded by the sandbox. After a specific period of time, the sandbox stops the program and analyzes its behaviors for malicious activities and patterns.
Since sandboxes do not rely on signatures, it is even possible to detect zero-day or highly targeted malware that security researchers and AV tools have not yet seen or evaluated.
With 2017 in the rearview mirror, we’re looking towards the changes a new year will bring. However, while the calendar may have changed, the threat of ransomware across industries and geographies has remained. And worse, it‘s looking like it’s about to evolve into an even greater problem.
This evolution is being largely fueled by the proliferation of the Internet of Things (IoT). According to Gartner data, there were about 8 billion connected “things” in 2017. But that number is expected to nearly triple to more than 20billion in just the next two years, which is roughly three connected devices per person on Earth. Simply put, the opportunity for cybercriminals to enter networks and hold aspects (or the entirety) of them hostage is growing at an exponential rate, with no signs of slowing down.
Artificial Intelligence (AI)-enabled analytics offer great promise for furthering the science of advanced threat detection. While it is difficult to imagine AI superseding the cognitive and instinctive power of talented security analysts and threat hunters in the immediate future, AI can and will advance the science of threat detection to accelerate speed and accuracy, while reducing that bane of all security operations centers—false negatives and false positives.
When discussing advanced threat detection, I’m referencing a class of threats able to evade the preventative and detective measures of both new and old security infrastructures. These are the class of threats that leverage zero-day exploits, develop targeted and stealthy malware, or operate from within the perimeter as a malicious insider or imposter. Organizations striving to detect this class of threat have long struggled to find the right balance between false negative risk and false positive frequency.
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