SUNNYVALE, Calif., Oct. 20, 2014 /PRNewswire/ -- Spansion Inc. (NYSE: CODE), a global leader in embedded systems, today added 96 new products to the Spansion® FM4 Family of flexible microcontrollers (MCUs). Based on the ARM® Cortex®-M4F core, the new MCUs boast a 200 MHz operating frequency and support a diverse set of on-chip peripherals for enhanced human machine interfaces (HMIs) and machine-to-machine (M2M) communications. The rich set of periphera...
|By Dana Gardner||
|January 13, 2014 08:45 AM EST||
The high cost of unwanted intrusion and malware across corporate networks is well known. Less talked-about are the successful ways that organizations are thwarting ongoing, adaptive and often-insider-driven security breaches.
Companies are understandably reluctant to readily discuss either their defenses or mishaps. Yet HP, one of the world's largest companies, is both a provider and a practitioner of enterprise intrusion detection systems (IDS). And so we asked HP to explain how it is both building and using such technologies, along with seeking some insider tips on best practices. [Disclosure: HP is a sponsor of BriefingDirect podcasts.]
And so the next edition of the HP Discover Podcast Series explores the ins and outs of improving enterprise intrusion detection systems (IDS). We learn how HP and its global cyber security partners have made the HP Global Network more resilient and safe. We also gain more insight into HP's vision for security and learn how that has been effectively translated into actual implementation.
The inside story comes from Jim O'Shea, Network Security Architect for HP Cyber Security Strategy and Infrastructure Engagement. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: What are some of the major trends that are driving the need for better intrusion detection and prevention nowadays?
O’Shea: If you look at the past, you had reaction technologies. We had firewalls that blocked and looked at the port level. Then we evolved to trying to detect things that were malicious with intent by using IDS. But that was still a reactionary-type thing. It was a nice approach, but we were reacting. But if you knew it was bad, why did we let it in in the first place?
The evolution was in the IDS, to the prevention. If you know it's bad, why do you even want to see it? Why do you want to try to react to it? Just block it early. That’s the trend that we’ve been following.
Gardner: But we can’t just have a black-and-white entry. We want access control, rather than just a firewall. So is there a new thinking, a new vision, that’s been developed over the past several years about these networks and what should or shouldn't be allowed through them?
O’Shea: You’re talking about letting the good in. Those are the evolutions and the trends that we are all trying to strive for. Let the good traffic in. Let who you are be a guide. Maybe look at what you have. You can also explore the health of your device. Those are all trends that we’re all striving for now.
Number of attacks
O’Shea: The Ponemon study was illustrating the vast number of attacks and the trend toward the costs for intrusion. It was highlighting those type of trends, all of which we’re trying to head off. Those type of reports are guiding factors in taking a more proactive, automated-type response. [Learn more about prevention detection.]
Gardner: I suppose what’s also different nowadays is that we’re not only concerned with outside issues in terms of risk, but also insider attacks.
O’Shea: You’re exactly right. Are you hiring the right people? That’s a big issue. Are they being influenced? Those are all huge issues. Big data can handle some of that and pull that in. Our approach on intrusion detection isn’t to just look at what’s coming from the outside, but also look at all data traversing the network.
When we deployed the TippingPoint solution, we didn’t change our policies or profiles that we were deploying based on whether it’s starting on the inside or starting on the outside. It was an equal deployment.
An insider attack could also be somebody who walks into a facility, gains physical access, and connects to your network. You have a whole rogue wireless-type approach in which people can gain access and can they probe and poke around. And if it’s malware traffic from our perspective, with the IDS we took the approach, inside or outside -- doesn’t matter. If we can detect it, if we can be in the path, it’s a block.
TippingPoint technology is an appliance-based technology. It’s an inline device. We deploy it inline. It sits in the network, and the traffic is flowing through it. It’s looking for characteristics or reputation on the type of traffic, and reputation is a more real-time change in the system. This network, IP address, or URL is known for malware, etc. That’s a dynamic update, but the static updates are signature-type, and the detection of vulnerability or a specific exploit aimed at an operating system.
So intrusion prevention is through the detection of that, and blocking and preventing that from completing its communication to the end node.
All the events get logged into HP ArcSight to create the bigger picture. Are you seeing these type of events occurring other places? So you have the bigger picture correlation.
Network-based anomaly detection is the ability to detect something that is occurring in the network and it's based on an IP address or it’s based on a flow. Taking advantage of reputation we can insert those IP addresses, detected based on flow, that are doing something anomalous.
It could be that they’re beaconing out, spreading a worm. If they look like they’re causing concerns with a high degree of accuracy, then we can put that into the reputation and take advantage of moving blocks.
So reputation is a self-deploying feature. You insert an IP address into it and it can self-update. We haven’t taken the automated step yet, although that’s in the plan. Today, it’s a manual process for us, but ideally, through application programming interfaces (APIs), we can automate all that. It works in a lab, but we haven’t deployed it on our production that way.
Gardner: Clearly HP is a good example of a large enterprise, one of the largest in the world, with global presence, with a lot of technology, a lot of intellectual property, and therefore a lot to protect. Let’s look at how you actually approached protecting the HP network.
What’s the vision, if you will, for HP's Global Cyber Security, when it comes to these newer approaches? Do you have an overarching vision that then you can implement? How do we begin to think about chunking out the problem in order to then solve it effectively?
O’Shea: You must be able to detect, block, and prevent as an overarching strategy. We also wanted to take advantage of inserting a giant filter inline on all data that’s going into the data center. We wanted to prevent mal traffic, mal-formed traffic, malware -- any traffic with the "mal" intent of reaching the data center.
So why make that an application decision to block and rely on host-level defenses, when we have the opportunity to do it at the network? So it made the network more hygienically clean, blocking traffic that you don’t want to see.
We wrapped it around the data center, so all traffic going into our data centers goes through that type of filter. [Learn more about prevention detection.]
Key to deployment
Because this is all an inline technology, and you are going inline in the network, you’re changing flows. It could be mal traffic, but yet maybe a researcher is trying to do something. So we need to have the ability to have that level of partnership with the network team. They have to see it. They have to understand what it is. It has to be manageable.
When we deployed it, we looked at what could go wrong and we designed around that. What could go wrong? A device failed. So we have an N+1 type installation. If a single device fails, we’re not down, we are not blocking traffic. We have the ability to handle the capacity of our network, which grows, and we are growing, and so it has to be built for the now and the future. It has to be manageable.
It has to be able to be understood by “first responders,” the people that get called first. Everybody blames the network first, and then it's the application afterward. So the network team gets pulled in on many calls, at all types of hours, and they have to be able to get that view.
That was key to get them broad-based training, so that the technology was there. Get a process integrated into how you’re going to handle updates and how you’re going to add beyond what TippingPoint recommended. TippingPoint makes a recommendation on profiles and new settings. If we take that, do we want to add other things? So we have to have a global cyber-security view and a global cyber-security input and have that all vetted.
The application team had to be onboard and aware, so that everybody understands. Finally, because we were going into a very large installed network that was handling a lot of different types of traffic, we brought in TippingPoint Professional Services and had everything looked at, re-looked at, and signed off on, so that what we’re doing is a best practice. We looked at it from multiple angles and took a lot of things into consideration.
Gardner: Is there something about TippingPoint and ArcSight that provides data, views, and analytics in such a way that it's easier for these groups to work together in ways that they hadn’t before?
O’Shea: One of the nice things about the way the TippingPoint events occur is that you have a choice. You can send them from an individual IDS units themselves or you can proxy them from the management console. Again, the ability to manage was critical to us, so we chose to do it from the console.
We proxy the events. That gives us the ability to have multiple ArcSight instances and also to evolve. ArcSight evolves. When they’re changing, evolving, and growing, and they want to bring up a new collector, we’re able to send very rapidly to the new collector.
ArcSight pulls in firewall logs. You can get proxy events and events from antivirus. You can pull in that whole view and get a bigger picture at the ArcSight console. The TippingPoint view is of what’s happening from the inline TippingPoint and what's traversing it. Then, the ArcSight view adds a lot of depth to that.
So it gives a very broad picture, but from the TippingPoint view, we’re very flexible and able to add and stay in step with ArcSight growth quickly. It's kind of a concert. That includes sending events on different ports. You’re not restricted to one port. If you want to create a secure port or a unique port for your events to go on to ArcSight, you have that ability.
After the deployment we’ve had some DoS attacks against us, and they have been blocked and deflected. We’ve had some other events that we have been able to block and defend rapidly. [Learn more about prevention detection.]
If you think back historically of how we dealt with them, those were kind of a Whac-A-Mole-type of defenses. Something happened, and you reacted. So I guess the metric would be that we’re not as reactionary, but do we have hard metrics to prove that? I don’t have those.
How much volume?
Gardner: We can appreciate the scale of what the systems are capable of. Do we have a number of events detected or that sort of thing, blocks per month, any sense of how much volume we can handle?
O’Shea: We took a month’s sample. I’m trying to recall the exact number, but it was 100 million events in one month that were detected as mal events. That’s including Internet-facing events. That’s why the volume is high, but it was 100 million events that were automatically blocked and that were flagged as mal events.
The Professional Services teams have been able to deploy in a very large network and have worked with the requirements that a large enterprise has. That includes standard deployment, how things are connected and what the drawings are going to look like, as well as how are you going to cable it up.
A large enterprise has different standards than a small business would have, and that was a give back to the Professional Services to be able to deploy it in a large enterprise. It has been a good relationship, and there is always opportunity for improvement, but it certainly has helped.
Gardner: Jim, looking to the future a little bit, we know that there’s going to be more and more cloud and hybrid-cloud types of activities. We’re certainly seeing already a huge uptick in mobile device and tablet use on corporate networks. This is also part of the bring-your-own-device (BYOD) trend that we’re seeing.
So should we expect a higher degree of risk and more variables and complication, and what does that portend for the use of these types of technologies going forward? How much gain do you get by getting on the IDS bandwagon sooner rather than later?
O’Shea: BYOD is a new twist on things and it means something different to everybody, because it's an acronym term, but let's take the view of you bringing in a product you buy.
Somebody is always going to get a new device, they are going to bring in it, they are going to try it out, and they are going to connect it to the corporate network, if they can. And because they are coming from a different environment and they’re not necessarily to corporate standards, they may bring unwanted guests into the network, in terms of malware.
Now, we have the opportunity, because we are inline, to detect and block that right away. Because we are an integrated ecosystem, they will show up as anomalous events. ArcSight and our Cyber Defense Center will be able to see those events. So you get a bigger picture.
Those events can be then translated into removing that node from the network. We have that opportunity to do that. BYOD not only brings your own device, but it also brings things you don’t know that are going to happen, and the only way to block that is prevention and anomalous type detection, and then try to bring it altogether in a bigger picture.
Sponsor: HP. Learn more about prevention detection.
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