Unless you’ve been living under a rock you are probably familiar with the recent Shadow Brokers data dump of the Equation Group tools. In that release a precision SMB backdoor was included called Double Pulsar. This backdoor is implemented by exploiting the recently patched Windows vulnerability: CVE-2017-0143.
Edge computing is likely the most interesting section of the broader world of IoT. If IoT is about connecting all the devices to the Internet, edge computing is about giving more processing power to devices at the edge. Edge computing views these edge devices as mini clouds or mini data centers. They each have their own mini servers, mini networking, mini storage, apps running on top of this infrastructure, and endpoint devices. Rather than sending data to the cloud for processing and receiving already-processed data from a central hub in the cloud, in edge computing all the processing happens on the edge device itself, or close to the edge device.
A type of credential reuse attack known as credential stuffing has been recently observed in higher numbers towards industry verticals. Credential stuffing is the process of automated probing of and access to online services using credentials usually coming from data breaches, or bought in the criminal underground.
An ever-increasing number of organizations are working in the cloud. It depends on their business model what cloud delivery model they use. The three most common deployment models for cloud services are software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-Service (IaaS).
In this post, we continue our discussion of use cases involving account take over and credential access in enterprise data sets. In the first part of this series, we introduced the definition of a VIP account as any account that has privileged or root level access to systems/services. These VIP accounts are important to monitor for changes in behavior, particularly because they have critical access to key parts of the enterprise. As a follow up to our first post, this blog will describe a real-time approach for automatically profiling VIP accounts and detecting when they are potentially being misused.
System administrators hold many key responsibilities within an IT organization. Most importantly, they must ensure that all systems, services, and applications are up, running, and performing as expected. When a system starts to lag or an application is down, the system administrators are called upon to troubleshoot and resolve the issue as quickly as possible to limit the impact on customers.
In a perfect world, computers would function properly on the network at all times. There would be no issues with the operating system and no problems with the applications. Unfortunately, this isn’t a perfect world. System failures can and will occur, and when they do, it is the responsibility of system administrators to diagnose and resolve the issues. But where can system administrators begin the search for solutions when problems arise? The answer is Windows event logs.
The last fifteen years have seen huge increases in developer productivity for several reasons, including the arrival of open source into the mainstream and the ability to better emulate target environments. In addition, the process of resetting a development environment back to the last known stable version has been vastly improved by Vagrant and then Docker.
Today's IT and DevOps teams have not one, but two, feature-rich open source Web servers to choose from: NGINX and Apache HTTP Server (which is often called simply "Apache"). At a high level, both platforms do the same core thing: Host and serve Web content. Both also offer comparable levels of performance and security.
Serverless computing is the latest, greatest thing in the technology world. Although the serverless concept has been around in one form or another for more than a decade, the introduction of serverless platforms from major cloud providers—starting with AWS Lambda in 2014—has brought serverless mainstream for the first time.
Serverless computing is becoming more popular as organizations look for new ways to deploy their applications in the cloud. With higher levels of abstraction, easier maintenance, a focus on high performance, and ephemeral workloads, serverless computing solutions like Lambda are finding a permanent place in the mix of cloud infrastructure options.
The principles of data protection are the same whether your data sits in a traditional on-prem data center or in a cloud environment. The way you apply those principles, however, are quite different when it comes to cloud security vs. traditional security. Moving data to the cloud introduces new attack-surfaces, threats, and challenges, so you need to approach security in a new way.
Database security refers to the various measures organizations take to ensure their databases are protected from internal and external threats. Database security includes protecting the database itself, the data it contains, its database management system, and the various applications that access it.
Internet security, in general, is a challenge that we have been dealing with for decades. It is a regular topic of discussion and concern, but a relatively new segment of internet security is getting the lion’s share of attention—internet of things (IoT). So why is internet of things security… a thing?
Microsoft Windows Internet Information Services (IIS) log files provide valuable information about the use and state of applications running on the web. However, it’s not always easy to find where those files are to determine important aspects of app usage like when requests for servers were made, by whom, and other user traffic concerns.
Here at Sumo Logic we’ve been talking a lot about the shift to Continuous Intelligence, and how software-centric companies and traditional organizations alike are being disrupted by traditional IT models. A newly commissioned white paper by the Enterprise Strategy Group, digs into the future of full-stack system management in the era of digital business. The author and Principle Analyst, Application Development and Deployment, Stephen Hendrick, examines the opportunity and challenge IT faces as an active participant in creating new, digital business models. “The opportunity centers on IT’s ability to create new business models and better address customer needs, while the challenge lies in it’s role as a disruptive force to establish enterprises that underestimate the power and speed of IT-fueled change.” Digital business models are fueling the growing acceptance of cloud computing and DevOps practices, resulting in new customer applications that are transforming many traditional markets into digital disruptors – Amazon, AWS, AirBnB, Facebook, Google, Netflix, Twitter, and Uber spring to mind as common examples. However, the rise of cloud-computing and continuous development and delivery practices also results in greater complexity and change within IT environments. Stephen discusses the emergence of technologies to address this trend. In addition, he introduces a Systems Management Reference model to analyze the role and relevance of continuous intelligence technologies to increase the adaptability of full-stack system management, thereby better serving the dynamic needs of the IT infrastructure and business. Stephen concludes, “continuous intelligence brings together the best that real-time, advance analytics has to offer by leveraging continuous real-time data to proactively support the evaluation of IT asset availability and performance within a highly secure environment. This approach reflects and is aligned with today’s modern architecture for application development and deployment, which includes microservices and immutable infrastructure.” Where does Sumo Logic fit into all of this? Quite simply we believe Sumo Logic’s purpose-built, cloud-native, machine data analytics service was designed to deliver real-time continuous intelligence across the entire infrastructure and application stack. This in turn enables organizations to answer questions they didn’t even know they had, by transforming the velocity, variety and volume of unstructured machine data overwhelming them into rich, actionable insights, to address and diffuse complexity and risk.