5,330 research outputs found
Beyond Counting: New Perspectives on the Active IPv4 Address Space
In this study, we report on techniques and analyses that enable us to capture
Internet-wide activity at individual IP address-level granularity by relying on
server logs of a large commercial content delivery network (CDN) that serves
close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015,
these logs recorded client activity involving 1.2 billion unique IPv4
addresses, the highest ever measured, in agreement with recent estimates.
Monthly client IPv4 address counts showed constant growth for years prior, but
since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it
seems we have entered an era marked by increased complexity, one in which the
sole enumeration of active IPv4 addresses is of little use to characterize
recent growth of the Internet as a whole.
With this observation in mind, we consider new points of view in the study of
global IPv4 address activity. Our analysis shows significant churn in active
IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over
the course of a year. Second, by looking across the active addresses in a
prefix, we are able to identify and attribute activity patterns to network
restructurings, user behaviors, and, in particular, various address assignment
practices. Third, by combining spatio-temporal measures of address utilization
with measures of traffic volume, and sampling-based estimates of relative host
counts, we present novel perspectives on worldwide IPv4 address activity,
including empirical observation of under-utilization in some areas, and
complete utilization, or exhaustion, in others.Comment: in Proceedings of ACM IMC 201
Profiling Users by Modeling Web Transactions
Users of electronic devices, e.g., laptop, smartphone, etc. have
characteristic behaviors while surfing the Web. Profiling this behavior can
help identify the person using a given device. In this paper, we introduce a
technique to profile users based on their web transactions. We compute several
features extracted from a sequence of web transactions and use them with
one-class classification techniques to profile a user. We assess the efficacy
and speed of our method at differentiating 25 users on a dataset representing 6
months of web traffic monitoring from a small company network.Comment: Extended technical report of an IEEE ICDCS 2017 publicatio
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
Firewall monitoring using intrusion detection systems
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2005Includes bibliographical references (leaves: 79-81)Text in English Abstract: Turkish and Englishviii,79 leavesMost organizations have intranet, they know the benefits of connecting their private LAN to the Internet. However, Internet is inherently an insecure network. That makes the security of the computer systems an imported problem. The first step of network security is firewalls. Firewalls are used to protect internal networks from external attacks through restricting network access according to the rules. The firewall must apply previously defined rules to each packet reaching to its network interface. If the application of rules are prohibited due to malfunction or hacking, internal network may be open to attacks and this situation should be recovered as fast as possible. In order to be sure about the firewall working properly, we proposed to use Intrusion Detection Systems (IDS)to monitor firewall operation. The architecture of our experimental environment is composed of a firewall and two IDSs. One IDS is between external network and firewall, while the other is between firewall and private network. Those two IDSs are invisible to the both networks and they send their information to a monitoring server, which decides, based on two observations, whether the firewall is working properly or not
Analysing the Security of Google's implementation of OpenID Connect
Many millions of users routinely use their Google accounts to log in to
relying party (RP) websites supporting the Google OpenID Connect service.
OpenID Connect, a newly standardised single-sign-on protocol, builds an
identity layer on top of the OAuth 2.0 protocol, which has itself been widely
adopted to support identity management services. It adds identity management
functionality to the OAuth 2.0 system and allows an RP to obtain assurances
regarding the authenticity of an end user. A number of authors have analysed
the security of the OAuth 2.0 protocol, but whether OpenID Connect is secure in
practice remains an open question. We report on a large-scale practical study
of Google's implementation of OpenID Connect, involving forensic examination of
103 RP websites which support its use for sign-in. Our study reveals serious
vulnerabilities of a number of types, all of which allow an attacker to log in
to an RP website as a victim user. Further examination suggests that these
vulnerabilities are caused by a combination of Google's design of its OpenID
Connect service and RP developers making design decisions which sacrifice
security for simplicity of implementation. We also give practical
recommendations for both RPs and OPs to help improve the security of real world
OpenID Connect systems
Analysis of intrusion prevention methods
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2004Includes bibliographical references (leaves: 105-108)Text in English; Abstract: Turkish and Englishviii, 108 leavesToday, the pace of the technological development and improvements has compelled the development of new and more complex applications. The obligatory of application development in a short time to rapidly changing requirements causes skipping of some stages, mostly the testing stage, in the software development cycle thus, leads to the production of applications with defects. These defects are, later, discovered by intruders to be used to penetrate into computer systems. Current security technologies, such as firewalls, intrusion detection systems, honeypots, network-based antivirus systems, are insufficient to protect systems against those, continuously increasing and rapid-spreading attacks. Intrusion Prevention System (IPS) is a new technology developed to block today.s application-specific, data-driven attacks that spread in the speed of communication. IPS is the evolved and integrated state of the existing technologies; it is not a new approach to network security. In this thesis, IPS products of various computer security appliance developer companies have been analyzed in details. At the end of these analyses, the requirements of network-based IPSs have been identified and an architecture that fits those requirements has been proposed. Also, a sample network-based IPS has been developed by modifying the open source application Snort
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
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