Traffic analysis is the process of intercepting and examining messages in order to deduce information from patterns in communication, which can be performed even when the messages are encrypted.^[1] In general, the greater the number of messages observed, or even intercepted and stored, the more can be inferred from the traffic. Traffic analysis can be performed in the context of military intelligence, counter-intelligence, or pattern-of-life analysis, and is a concern in computer security.

Traffic analysis tasks may be supported by dedicated computer software programs. Advanced traffic analysis techniques may include various forms of social network analysis.

Breaking the anonymity of networks

Traffic analysis method can be used to break the anonymity of anonymous networks, e.g., TORs ^[1]. There are two methods of traffic-analysis attack, passive and active.

• In passive traffic-analysis method, the attacker extracts features from the traffic of a specific flow on one side of the network and looks for those features on the other side of the network.
• In active traffic-analysis method, the attacker alters the timings of the packets of a flow according to a specific pattern and looks for that pattern on the other side of the network; therefore, the attacker can link the flows in one side to the other side of the network and break the anonymity of it. It is shown, although timing noise is added to the packets, there are active traffic analysis methods robust against such a noise.^[1]

In military intelligence

In a military context, traffic analysis is a basic part of signals intelligence, and can be a source of information about the intentions and actions of the target. Representative patterns include:

• Frequent communications – can denote planning
• Rapid, short communications – can denote negotiations
• A lack of communication – can indicate a lack of activity, or completion of a finalized plan
• Frequent communication to specific stations from a central station – can highlight the chain of command
• Who talks to whom – can indicate which stations are 'in charge' or the 'control station' of a particular network. This further implies something about the personnel associated with each station
• Who talks when – can indicate which stations are active in connection with events, which implies something about the information being passed and perhaps something about the personnel/access of those associated with some stations
• Who changes from station to station, or medium to medium – can indicate movement, fear of interception

There is a close relationship between traffic analysis and cryptanalysis (commonly called codebreaking). Callsigns and addresses are frequently encrypted, requiring assistance in identifying them. Traffic volume can often be a sign of an addressee's importance, giving hints to pending objectives or movements to cryptanalysts.

Traffic flow security

Traffic-flow security is the use of measures that conceal the presence a

Traffic analysis tasks may be supported by dedicated computer software programs. Advanced traffic analysis techniques may include various forms of social network analysis.

Traffic analysis method can be used to break the anonymity of anonymous networks, e.g., TORs ^[1]. There are two methods of traffic-analysis attack, passive and active.

• In passive traffic-analysis method, the attacker extracts features from the traffic of a specific flow on one side of the network and looks for those features on the other side of the network.
• In active traffic-analysis method, the attacker alters the timings of the packets of a flow according to a specific pattern and looks for that pattern on the other side of the network; therefore, the attacker can link the flows in one side to the other side of the network and break the anonymity of it. It is shown, although timing noise is added to the packets, there are active traffic analysis methods robust against such a noise.^[1]

In military intelligence

In a military context, traffic analysis is a basic part of signals intelligence, and can be a source of information about the intentions and actions of the target. Representative patterns include:

• Frequent communications – can denote planning
• Rapid, short communications – can denote negotiations
• A lack of communication – can indicate a lack of activity, or completion of a finalized plan
• Frequent communication to specific stations from a central station – can highlight the chain of command
• Who talks to whom – can indicate which stations are 'in charge' or the 'control station' of a particular network. This further implies something about the personnel associated with each station
• Who talks when – can indicate which stations are active in connection with events, which implies something about the information being passed and perhaps something about the personnel/access of those associated with some stations
• Who changes from station to station, or medium to medium – can indicate movement, fear of interception

There is a close relationship between traffic analysis and cryptanalysis (commonly called codebreaking). signals intelligence, and can be a source of information about the intentions and actions of the target. Representative patterns include:

• Frequent communications – can denote planning
• Rapid, short communications – can denote negotiations
• A lack of communication – can indicate a lack of activity, or completion of a finalized plan
• Frequent communication to specific stations from a central station – can highlight the chain of command
• Who talks to whom – can indicate which stations are 'in charge' or the 'control station' of a particular network. This further implies something about the personnel associated with each station
• Who talks when – can indicate which stations are active in connection with events, which implies something about the information being passed and perhaps something about the personnel/access of those associated with some stations
• Who changes from station to station, or medium to medium – can indicate movement, fear of interception

There is a close relationship between traffic analysis and cryptanalysis (commonly called codebreaking). Callsigns and addresses are frequently encrypted, requiring assistance in identifying them. Traffic volume can often be a sign of an addressee's importance, giving hints to pending objectives or movements to cryptanalysts.

Traffic flow security

Traffic-flow security is the use of measures that conceal the presence and properties of valid messages on a network to prevent traffic analysis. This can be done by operational procedures or by the protection resulting from features inherent in some cryptographic equipment. Techniques used include:

• changing radio callsigns frequently
• encryption of a message's sending and receiving addresses (codress messages)
• causing the circuit to appear busy at all times or much of the time by sending dummy callsigns frequently
• encryption of a message's sending and receiving addresses (codress messages)
• causing the circuit to appear busy at all times or much of the time by sending dummy traffic<

  Traffic-flow security is one aspect of communications security.

  COMINT metadata analysis

  communications intelligence (COMINT) referring to the concept of producing intelligence by analyzing only the technical metadata, hence, is a great practical example for traffic analysis in intelligence. While traditionally information gathering in COMINT is derived from intercepting transmissions, tapping the target's communications and monitoring the content of conversations, the metadata intelligence is not based on content but on technical communicational data. Non-content COMINT is usually used to deduce information about the user of a certain transmitter, such as locations, contacts, activity volume, routine and its exceptions. Examples For example, if a certain emitter is known as the radio transmitter of a certain unit, and by using direction finding (DF) tools, the position of the emitter is locatable; hence the changes of locations can be monitored. That way we're able to understand that this certain unit is moving from one point to another, without listening to any orders or reports. If we know that this unit reports back to a command on a certain pattern, and we know that another unit reports on the same pattern to the same command, then the two units are probably related, and that conclusion is based on the metadata of the two units' transmissions, and not on the content of their transmissions. Using all, or as much of the metadata available is commonly used to build up an Electronic Order of Battle (EOB) – mapping different entities in the battlefield and their connections. Of course the EOB could be built by tapping all the conversations and trying to understand which unit is where, but using the metadata with an automatic analysis tool enables a much faster and accurate EOB build-up that alongside tapping builds a much better and complete picture. World War I British analysts in World War I noticed that the call sign of German Vice Admiral Reinhard Scheer, commanding the hostile fleet, had been transferred to a land station. Admiral of the Fleet Beatty, ignorant of Scheer's practice of changing callsigns upon leaving harbor, dismissed its importance and disregarded Room 40 analysts' attempts to make the point. The German fleet sortied, and the British were late in meeting them at the Battle of Jutland.[2] If traffic analysis had been taken more seriously, the British might have done better than a "draw".[original research?] French military intelligence, shaped by Kerckhoffs's legacy, had erected a network of intercept stations at the Western front in pre-war times. When the Germans crossed the frontier, the French worked out crude means for direction-finding based on intercepted signal intensity. Recording of call-signs and volume of traffic further enabled them to identify German combat groups and to distinguish between fast-moving cavalry and slower infantry.[2] World War II In early World War II, the direction finding (DF) tools, the position of the emitter is locatable; hence the changes of locations can be monitored. That way we're able to understand that this certain unit is moving from one point to another, without listening to any orders or reports. If we know that this unit reports back to a command on a certain pattern, and we know that another unit reports on the same pattern to the same command, then the two units are probably related, and that conclusion is based on the metadata of the two units' transmissions, and not on the content of their transmissions. Using all, or as much of the metadata available is commonly used to build up an Electronic Order of Battle (EOB) – mapping different entities in the battlefield and their connections. Of course the EOB could be bu Using all, or as much of the metadata available is commonly used to build up an Electronic Order of Battle (EOB) – mapping different entities in the battlefield and their connections. Of course the EOB could be built by tapping all the conversations and trying to understand which unit is where, but using the metadata with an automatic analysis tool enables a much faster and accurate EOB build-up that alongside tapping builds a much better and complete picture. Traffic analysis is also a concern in computer security. An attacker can gain important information by monitoring the frequency and timing of network packets. A timing attack on the SSH protocol can use timing information to deduce information about passwords since, during interactive session, SSH transmits each keystroke as a message.[7] The time between keystroke messages can be studied using hidden Markov models. Song, et al. claim that it can recover the password fifty times faster than a brute force attack. Onion routing systems are used to gain anonymity. Traffic analysis can be used to attack anonymous communication systems like the Tor anonymity network. Adam Back, Ulf Möeller and Anton Stiglic present traffic analysis attacks against anonymity providing systems .[8] Steven J. Murdoch and George Danezis from University of Cambridge presented [9] research showing that traffic-analysis allows adversaries to infer which nodes relay the anonymous streams. This reduces the anonymity provided by Tor. They have shown that otherwise unrelated streams can be linked back to the same initiator. Remailer systems can also be attacked via traffic analysis. If a message is observed going to a remailing server, and an identical-length (if now anonymized) message is seen exiting the server soon after, a traffic analyst may be able to (automatically) connect the sender with the ultimate receiver. Variations of remailer operations exist that can make traffic analysis less effective. Countermeasures It is difficult to defeat traffic analysis without both encrypting messages and masking the channel. When no actual messages are being sent, the channel can be masked [10] by sending dummy traffic, similar to the encrypted traffic, thereby keeping bandwidth usage constant .[11] "It is very hard to hide information about the size or timing of messages. The known solutions require Alice to send a continuous stream of messages at the maximum bandwidth she will ever use...This might be acceptable for military applications, but it is not for most civilian applications." The military-versus-civilian problems applies in situations where the user is charged for the volume of information sent. Even for Internet access, where there is not a per-packet charge, ISPs make statistical assumption that connections from user sites will not be busy 100% of the time. The user cannot simply increase the bandwidth of the link, since masking would fill that as well. If masking, which often can be built into end-to-end encryptors, becomes common practice, ISPs will have to change their traffic assumptions. See also Chatter (signals intelligence) Data warehouse ECHELON Electronic order of battle ELINT Pattern-of-life analysis SIGINT Social network analysis Telecommunications data retention Zendian Problem References ^ a b c Soltani, Ramin; Goeckel, Dennis; Towsley, Don; Houmansadr, Amir (2017-11-27). "Towards Provably Invisible Network Flow Fingerprints". 2017 51st Asilomar Conference on Signals, Systems, and Computers. pp. 258–262. arXiv:1711.10079. doi:10.1109/ACSSC.2017.8335179. ISBN 978-1-5386-1823-3.