Estructura y Composición de ExtJS

Many network administrators, security professionals, and business leaders struggle in the effort to prevent data loss within their organizations. The ability to identify anomalous behavior in data flows is crucial to detect and prevent data loss. The application of analytics to data collected via NetFlow can aid security professionals in detecting anomalous amounts of data leaving the organization and abnormal traffic patterns inside of the organization.

Using NetFlow along with identity management systems, you can detect who initiated the data transfer, the hosts (IP addresses) involved, the amount of data transferred, and the services used. In addition, you can measure how long the communications lasted as well as the frequency of the same connection

attempts.

TIP

Often, tuning is necessary because certain traffic behavior could cause false positives. For instance, your organization may be legitimately sharing large amounts of data or streaming training

videos to business partners and customers. In addition, analytics software that examines baseline behavior may be able to detect typical file transfers and incorporate them into existing baselines.

In the following scenario, a large retail organization is the victim of a major breach where attackers stole more than 100,000 credit card numbers. The retailer headquarters is in New York, NY and has two large secondary offices in Raleigh, North Carolina, and San Juan, Puerto Rico. This retailer also has more than 1000 stores in the United States and Canada. Figure 4-13 illustrates these offices and stores.

Figure 4-13 Retailer High-Level Network Topology

The breach was not detected for several months after the attackers had already penetrated the network. The retailer had firewalls and intrusion prevention devices, but those were not enough to detect or mitigate the attack. The attack was thought to be an inside job, because the malware that was extracting the

credit card numbers was very sophisticated and tailored to such an organization. The breach was detected only because law enforcement contacted the victimized retailer, telling them that thousands of fraudulent credit card transactions had been detected on credit cards that were last legitimately used at their stores.

After the organization started their incident response and forensics

investigation, they decided to deploy NetFlow in routers at the edge of the data center. The topology in Figure 4-14 illustrates the network at the New York headquarters and the two routers that were configured with NetFlow.

Figure 4-14 New York Headquarters NetFlow Routers

The data center has numerous servers that are dedicated for credit card processing applications (software), as illustrated in Figure 4-15.

Figure 4-15 Credit Card Processing Servers

After deploying NetFlow in their data center edge routers, the retailer observed that numerous DNS requests were being sent from the credit card processing servers to DNS servers outside of the country (United States). The most interesting fact was that such DNS servers were in embargoed countries where the retailer previously had never transacted any business. In addition, most of these DNS requests were being sent during off-hours (mostly around 2:00 to 4:00 a.m. local time).

The retailer was able to inspect NetFlow traffic and detect the country where the credit card information was sent by using the MaxMind Geolocation database. MaxMind provides IP intelligence to thousands of companies to

locate their Internet visitors and perform analytics. MaxMind has a service called minFraud, which helps businesses prevent fraudulent online transactions and reduce manual review.

NOTE

You can obtain more information about MaxMind at

https://www.maxmind.com. You can also get access to their database files and open source utilities at

https://github.com/maxmind.

The attackers were sending stolen credit card data over DNS using tunneling.

DNS is a protocol that enables systems to resolve domain names (for example, example.com) into IP addresses (for example, 93.184.216.34). DNS is not intended for a command channel or even tunneling. However, attackers have developed software that enables tunneling over DNS. Because traditionally DNS it is not designed for data transfer, it is less inspected in terms of security monitoring. Undetected DNS tunneling (otherwise known as DNS exfiltration) represents a significant risk to any organization.

In this case, the credit cards were base64 encoded and sent over DNS requests (tunneling) to cybercriminals abroad. Attackers nowadays use different DNS record types and encoding methods to exfiltrate data from victims’ systems and networks. The following are some examples of encoding methods:

Base64 encoding

Binary (8-bit) encoding NetBIOS encoding Hex encoding

Several utilities have been created to perform DNS tunneling (for the good and also the bad). The following are a few examples:

DeNiSe: A Python tool for tunneling TCP over DNS.

dns2tcp: Written by Olivier Dembour and Nicolas Collignon in C, dns2tcp supports KEY and TXT request types.

DNScapy: Created by Pierre Bienaime, this Python-based Scapy tool for packet generation even supports SSH tunneling over DNS, including a SOCKS proxy.

DNScat or DNScat-P: This Java-based tool created by Tadeusz Pietraszek supports bidirectional communication through DNS.

DNScat (DNScat-B): Written by Ron Bowes, this tool runs on Linux, Mac OS X, and Windows. DNScat encodes DNS requests in NetBIOS or hex encoding.

Heyoka: This tool, written in C, supports bidirectional tunneling for data exfiltration.

Iodine: Written by Bjorn Andersson and Erik Ekman in C, Iodine runs on Linux, Mac OS X, and Windows, and can even be ported to Android.

Nameserver Transfer Protocol (NSTX): Creates IP tunnels using DNS.

OzymanDNS: Written in Perl by Dan Kaminsky, this tool is used to set up an SSH tunnel over DNS or for file transfer. The requests are base32 encoded, and responses are base64-encoded TXT records.

psudp: Developed by Kenton Born, this tool injects data into existing DNS requests by modifying the IP/UDP lengths.

Feederbot and Moto: This malware using DNS has been used by attackers to steal sensitive information from many organizations.

NOTE

Some of these tools were not created with the intent to steal data, but cybercriminals have used them for their own purposes.

The retailer’s network security personnel were able to perform detailed analysis of the techniques used by the attackers to steal this information and discovered the types of malware and vulnerabilities being exploited in systems in the data center. Network telemetry tools such as NetFlow are invaluable when trying to understand what is happening (good and bad) in the network, and it is a crucial tool for incident response and network forensics.

Retailers or any organizations that process credit cards or electronic payments are often under regulation from the Payment Card Industry Data Security

Standard (PCI DSS). PCI DSS was created to encourage and maintain cardholder data security and expedite the consistent use of data security

methodologies. This standard enforces a baseline of technical and operational requirements. PCI DSS applies to the following: