4,438 research outputs found
SecMon: End-to-End Quality and Security Monitoring System
The Voice over Internet Protocol (VoIP) is becoming a more available and
popular way of communicating for Internet users. This also applies to
Peer-to-Peer (P2P) systems and merging these two have already proven to be
successful (e.g. Skype). Even the existing standards of VoIP provide an
assurance of security and Quality of Service (QoS), however, these features are
usually optional and supported by limited number of implementations. As a
result, the lack of mandatory and widely applicable QoS and security guaranties
makes the contemporary VoIP systems vulnerable to attacks and network
disturbances. In this paper we are facing these issues and propose the SecMon
system, which simultaneously provides a lightweight security mechanism and
improves quality parameters of the call. SecMon is intended specially for VoIP
service over P2P networks and its main advantage is that it provides
authentication, data integrity services, adaptive QoS and (D)DoS attack
detection. Moreover, the SecMon approach represents a low-bandwidth consumption
solution that is transparent to the users and possesses a self-organizing
capability. The above-mentioned features are accomplished mainly by utilizing
two information hiding techniques: digital audio watermarking and network
steganography. These techniques are used to create covert channels that serve
as transport channels for lightweight QoS measurement's results. Furthermore,
these metrics are aggregated in a reputation system that enables best route
path selection in the P2P network. The reputation system helps also to mitigate
(D)DoS attacks, maximize performance and increase transmission efficiency in
the network.Comment: Paper was presented at 7th international conference IBIZA 2008: On
Computer Science - Research And Applications, Poland, Kazimierz Dolny
31.01-2.02 2008; 14 pages, 5 figure
KISS: Stochastic Packet Inspection Classifier for UDP Traffic
This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications
No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone
It is generally recognized that the traffic generated by an individual
connected to a network acts as his biometric signature. Several tools exploit
this fact to fingerprint and monitor users. Often, though, these tools assume
to access the entire traffic, including IP addresses and payloads. This is not
feasible on the grounds that both performance and privacy would be negatively
affected. In reality, most ISPs convert user traffic into NetFlow records for a
concise representation that does not include, for instance, any payloads. More
importantly, large and distributed networks are usually NAT'd, thus a few IP
addresses may be associated to thousands of users. We devised a new
fingerprinting framework that overcomes these hurdles. Our system is able to
analyze a huge amount of network traffic represented as NetFlows, with the
intent to track people. It does so by accurately inferring when users are
connected to the network and which IP addresses they are using, even though
thousands of users are hidden behind NAT. Our prototype implementation was
deployed and tested within an existing large metropolitan WiFi network serving
about 200,000 users, with an average load of more than 1,000 users
simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned
out to be very effective, with an accuracy greater than 90%. We also devised
new tools and refined existing ones that may be applied to other contexts
related to NetFlow analysis
I Know Where You are and What You are Sharing: Exploiting P2P Communications to Invade Users' Privacy
In this paper, we show how to exploit real-time communication applications to
determine the IP address of a targeted user. We focus our study on Skype,
although other real-time communication applications may have similar privacy
issues. We first design a scheme that calls an identified targeted user
inconspicuously to find his IP address, which can be done even if he is behind
a NAT. By calling the user periodically, we can then observe the mobility of
the user. We show how to scale the scheme to observe the mobility patterns of
tens of thousands of users. We also consider the linkability threat, in which
the identified user is linked to his Internet usage. We illustrate this threat
by combining Skype and BitTorrent to show that it is possible to determine the
file-sharing usage of identified users. We devise a scheme based on the
identification field of the IP datagrams to verify with high accuracy whether
the identified user is participating in specific torrents. We conclude that any
Internet user can leverage Skype, and potentially other real-time communication
systems, to observe the mobility and file-sharing usage of tens of millions of
identified users.Comment: This is the authors' version of the ACM/USENIX Internet Measurement
Conference (IMC) 2011 pape
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