Encrypted Traffic Security (ETS) White Paper
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1 Encrypted Traffic Security (ETS) White Paper
2 The rapid rise in encrypted traffic is changing the security landscape. As more organizations become digital, an increasing number of services and applications are using encryption as a primary method of securing information. Encrypted traffic has increased by more than 90 percent year over year, with more than 40 percent of websites encrypting traffic in 2016 versus 21 percent in study has revealed by A10 Network and the Ponemon Institute. The trend is expected to grow in parallel with the greater legitimate use of encryption. Inbound encrypted traffic is expected to rise from 60% to 70% in 2018, and outbound encrypted traffic from 55% to 65%. Encryption technology has enabled much greater privacy and security for enterprises that use the Internet to communicate and transact business online. Mobile, cloud and web applications rely on well-implemented encryption mechanisms, using keys and certificates to ensure security and trust. However, businesses are not the only ones to benefit from encryption. Threat actors have leveraged these same benefits to evade detection and to secure their malicious activities. Fig 2 shows the economic impact of such attacks. Malware in nearly half of cyber attacks in the past 12 months has used encrypted traffic as cover for entry, a SSL attacks offer attackers another advantage: the ability to put significant computing stress on the network and application infrastructures they target. The process of decrypting and re-encrypting SSL traffic increases the requirements of processing the traffic in many cases beyond the functional performance of devices used for attack mitigation. Most devices are inline, stateful and unable to handle SSL encrypted attacks, making them vulnerable to SSL floods. Visibility into encrypted traffic isn t the only challenge related to SSL/TLS. When surveyed about the ability of existing security solutions to decrypt, inspect and reencrypt traffic, 75% of organization are working blind. Fig1: Encryption is transforming the threat landscape
3 When the enterprise network traffic is encrypted, it makes sense from the criminal perspective to also encrypt their activities since it would be harder for IT administrators to be able to tell the difference between bad and good traffic. Malware families are increasingly using SSL to encrypt the communications between the compromised endpoint and the command-andcontrol systems to hide. Contents According to Gartner, less than 20% of organizations decrypt inbound traffic at the network perimeter; less than 30% inspect encrypted traffic leaving the network. Further, more than 90% with public websites decrypt inbound Web traffic (often through a Web Application Firewall); however, many of the encrypted attack vectors are doing their damage before traffic gets this deep into the network or application infrastructure.(1)(2) The Problems Overview Encrypted Traffic Security Description Results Conclusions Appendix A Fig 2: Economic impact of cyber Attacks SSL-based attacks take many forms. Among them: Encrypted SSL floods. These attacks seek to exhaust the resources in place. Encrypted SSL floods complicate the challenge by encrypting traffic and forcing resource use of SSL handshake resources. SSL renegotiation. These attacks work by initiating a regular SSL handshake and then immediately requesting the renegotiation of the encryption key. The tool continuously repeats this renegotiation request until all server resources have been exhausted. HTTPS floods. These attacks generate floods of encrypted HTTP traffic, often as part of multi-vector attack campaigns. Compounding the impact of normal HTTPS floods, encrypted HTTP attacks add the burden of encryption and decryption mechanisms. Encrypted Web application attacks. Multi-vector campaigns also increasingly leverage non-dos, Web application logic attacks. By encrypting the traffic that masks these attacks, they often pass undetected through both DDoS and Web application protections.
4 The Problem of Encryption Identifying attack traffic within encrypted traffic flows is akin to finding a black cat in a black room blindfolded. Most cyber-attack solutions struggle to identify potentially malicious traffic from encrypted traffic sources and to isolate that traffic for further analysis (and potential mitigation). Many solutions that can do some level of decryption tend to rely on limiting the rate of request, which results in legitimate traffic being dropped and effectively completes the attack. Finally, many solutions require the customer to share actual server certificates. That requirement complicates implementation and certificate management and forces customers to share private keys for protection in the cloud. Visibility into encrypted traffic isn t the only challenge related to SSL/TLS. When surveyed about the ability of existing security solutions to decrypt, inspect and reencrypt traffic, most are similarly working blind. Specifically, 75% of industry practitioners doubt their security solutions provide full-encrypted attack protection. 4 According to Gartner, less than 20% of organizations decrypt inbound traffic at the network perimeter; less than half inspect encrypted traffic leaving the network. Further, more than 90% with public websites decrypt inbound Web traffic (often through a Web Application Firewall); however, many of the encrypted attack vectors are doing their damage before traffic gets this deep into the network or application infrastructure. Encrypted Cognitive Analytics A new approach is needed to detect attacks more accurately and on both normal and encrypted traffic and that approach will reply on metadata analytics and interflow metadata, IN and OUT packets inside of a flow that, can be collected, analyzed and stored. This approach is derived by using new types of data elements or telemetry that are independent of protocol details, such as the lengths and arrival times of packets within a flow. These data elements have the attractive property of applying equally well to both encrypted and unencrypted flows.
5 Benefits 1. Real Time: Detect attacks in real time (milliseconds) from the whole IT infrastructure of a business 2. Visibility: Get a visibility on your encrypted traffic and detect attacks and malware 3. High Accuracy: Detect attacks with 99% accuracy and reducing false positive by more than 95% to help you focus on the real problems 4. Privacy: No need to decrypt the data and to hit security issues 5. Scalability: Ability to collect and analyze more than 100 million event per - Ipsum second Metadata Traffic Analytics A new approach for encrypted traffic analytics Metadata Traffic Analytics focuses on identifying malware communications in normal and encrypted traffic using metadata instead of data through passive monitoring, the extraction of relevant elements, supervised machine learning and behavioral analytics with cloud based global visibility. Protocol/Internet Protocol, TCP/IP is a set of rules (protocols) governing communications among all computers on the Internet. More specifically, TCP/IP dictates how information should be packaged (turned into bundles of information called packets), sent, and received, as well as how to get to its destination Transport Layer Security (TLS) is a cryptographic protocol that provides privacy for applications. TLS was designed to operate on top of a reliable transport protocol such as TCP. However, it has also been adapted to run over datagram protocols such as UDP. The Datagram Transport Layer Security (DTLS) protocol, defined in RFC 6347, is based on the TLS protocol and is able to provide similar security guarantees while preserving the datagram delivery model. Matadata Traffic Analytics extracts six main data elements: The sequence of packet, the byte distribution of the sequence message, TCP/TLS-specific features, IN & OUT packets, the initial data packet and the certificate used by both addresses. Barac port mirroring and unique architecture provides the ability to extract these data elements in real time without slowing down the data network. Sequence of Packets (SP) represents the different packets exchanged by two IP addresses on the same ports on TLS (Transport Layer Security) and TCP (Transmission Control Protocol). We reconstitute the message and extract the lengths, times and calculate new features such as the time to respond, average window length, etc... For example: An IP Address A is sending a message to an IP B, we collect the TLS packets of the handshake then the packets containing the data, reconstitute all the interaction in real time, calculate new feature using artificial intelligence and then send the sequence to the cognitive platform, Byte Distribution (BD) represents the min, average, max and deviation of packets and byte distribution on the message sequence and SSL record IN & OUT packets: Some important features that impossible to mimic for attackers such Time to respond, number of packets, control flags and diverse entropy,
6 TCP/TLS specific features: Using our patent pending technology, we calculate around 150 features in real time with variance and entropy Appendix A shows a detailed table of some new data elements calculated in real time Metadata Traffic Analytics Components Barac Data Collection In the Barac architecture, metadata is transmitted from exporter to collector in sets of records. We port mirror the traffic into a server or a virtual machine and collect metadata and the IN and OUT TCP packets and SSL records information instantly and send it to the cognitive platform, each packet and record collected has the same format, which is specified by its template. These templates use several globally defined elements administered by IANA. Some of the global elements, such as IP addresses and Layer 4 port numbers, form a familiar 5-tuple that is used as a unique flow identifier (flow key). Additional elements are used to report basic packet/octet statistics and timestamps. These globally defined elements are enhanced with a unique collection ID to detect any problem on one of the collectors and the time in nanoseconds to be able to reconstitute the packet sequence message with very high accuracy. Barac Cognitive Platform Barac cognitive platform is compose of two main elements: Collection & Pre-processing: Collect the data and the IN and OUT data, packets and information from the port mirrored traffic, reconstitute the sequence packet message in real time between IP addresses, calculate new features as packets are received and send the whole message to the analytics platform as explained on the image B below and on appendix A Analytics: use a combination of machine learning and behavioral analytics in real time on the packet message sequence constituted by the pre-processing engine. We score and categorize each sequence to detect if it s a normal or abnormal activity or an early sign of know malware. The global risk map and Encrypted Traffic Analytics data elements reinforce each other in the Cognitive Analytics engine. Rather than decrypting the traffic, barac uses artificial intelligence to pinpoint malicious patterns in encrypted traffic to help identify threats and improve incident response. We reconstitute the sequence message and calculate new features
7 After the analytics and the scoring of each connection, the Barac platform allows a multiple interface points with the users to help them quickly respond to threats while following their internal procedures. We also help the organization investigate those threats using metadata information and analytics from the barac cognitive solution. The Barac user interface and dashboard provides a view of affected users identified by Cognitive Analytics by risk type and score. An expanded Analytics dashboard provides detailed information regarding the top threat exposure, the vulnerabilities on your infrastructure and a real time interface of the attacks happening by threat risk. Threat Web Attacks Man in the Middle Necurs Type SQL injections, SSL renegotiation, Encrypted Web Application Attack, XSS Detect Man in the Middle Attacks Information stealer, backdoor, botnet Service Denial DDOS attack, Encrypted SSL Floods, HTTPS Floods
8 Upon discovery, a malicious encrypted flow can be blocked or quarantined by barac to help organizations in a real time response by interfacing with the firewall using API and send the information to the right people at the organization. Across Your Network Barac Metadata Analytics platform give you the ability to detect potential intrusions across your whole IT infrastructure on both normal and encrypted traffic to secure your infrastructure and your IoT devices: HTTPS Protect your website and your applications and detect attacks on HTTPS encrypted traffic IoT Devices Detect any abnormal activity on your IoT infrastructure or from the IoT device traffic to your servers to detect possible hack or usage of device for zombies attacks FTP Connections As we are monitoring all the traffic, we can detect that an intruder is trying to get their data out of your system using Results In experiments based on real-world data, we were able to achieve over 99.91% accuracy with % false positives on both normal and encrypted traffic (only an average of 2 false positive for every 30,000 TLS connections) seen. This was based on a large sample of real-world HTTPS sessions and SSL connection of IoT devices of customers and tester.
9 Conclusions In summary, our patent pending technology and new approach based on metadata, packet information and artificial intelligence is more effective than traditional approaches working on the deep network level, giving visibility to detect attacks on encrypted traffic, generating a very low false-positive to help organizations be more accurate and respond in real time to threats Appendix A At barac, we calculate around 150 features while rebuilding the sequence message. Below some of the features that we use and calculate in real time while receiving packets: Feature Sequence of Packet Initial Packets TLS Records TLS Records Entropy TLS message lengths TLS message arrival TLS message types TLS extensions TLS extension Description The sequence of the packets during the exchange of information from an IP A to an IP B on the same ports exp: SYN_SYN_ACK_ACK_ACK The content of the initial packets of the sequence message is very important in a connection A combination of length values, inter-arrival time values, content type values, followed by an array of handshake values for the whole sequence of TLS exchange The entropy of the TLS exchange for the different information detailed above The minimum, average and maximum length between the TLS flow packets Thee sequence of TLS interarrival times for up to the first N records of a TLS flow. The sequence of handshakes and the content of the TLS sequence The Value and types of the TLS extensions observed in the Hello message for a TLS flow The Type and size of the TLS extensions observed in the TLS Hello message for a flow
10 TLS Message Entropy Packet length OUT_IN Packet length MIN Average Length Window Entropy packet length Entropy Time to Live Entropy Length Window Time to Respond Entropy Time to Respond Sequence Length TLS Sequence Length Sequence Debit References The entropy of the flow of message and packets collected for the TLS Hello message The total packet length for the In and OUT packet of the TCP message after the TLS The min packet length for the In and OUT packet of the TCP message after the TLS The average packet length for the In and OUT packet of the TCP message after the TLS The entropy calculated on the IN and OUT packet lengths The entropy calculated for the time to leave for each packet of the sequence message The entropy calculated window length for each packet of the sequence message The time to respond between each IN and OUT packets of the TLS and TCP flow The entropy calculated Time to respond for the sequence message The total length of the TLS messages The Total length of the whole sequence message The debit for the whole sequence message calculated in real time 1. Gartner: Security Leaders Must Address Threats from Rising SSL Traffic 2. Security Leaders Must Address Threats From Rising SSL Traffic Gartner Research, January 8, Ponemon Institute: Uncovering Hidden Threats Within Encrypted Traffic, NSS Labs: TLS/SSL: Where Are We Today? The Encrypted Web Part 1 An Upward Trajectory 5. Identifying Encrypted Malware Traffic with Contextual Flow Data, Blake Anderson and David McGrew, AISEC
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