23,944 research outputs found
Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences
In this survey, we first briefly review the current state of cyber attacks,
highlighting significant recent changes in how and why such attacks are
performed. We then investigate the mechanics of malware command and control
(C2) establishment: we provide a comprehensive review of the techniques used by
attackers to set up such a channel and to hide its presence from the attacked
parties and the security tools they use. We then switch to the defensive side
of the problem, and review approaches that have been proposed for the detection
and disruption of C2 channels. We also map such techniques to widely-adopted
security controls, emphasizing gaps or limitations (and success stories) in
current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages.
Listing abstract compressed from version appearing in repor
Emerging Phishing Trends and Effectiveness of the Anti-Phishing Landing Page
Each month, more attacks are launched with the aim of making web users
believe that they are communicating with a trusted entity which compels them to
share their personal, financial information. Phishing costs Internet users
billions of dollars every year. Researchers at Carnegie Mellon University (CMU)
created an anti-phishing landing page supported by Anti-Phishing Working Group
(APWG) with the aim to train users on how to prevent themselves from phishing
attacks. It is used by financial institutions, phish site take down vendors,
government organizations, and online merchants. When a potential victim clicks
on a phishing link that has been taken down, he / she is redirected to the
landing page. In this paper, we present the comparative analysis on two
datasets that we obtained from APWG's landing page log files; one, from
September 7, 2008 - November 11, 2009, and other from January 1, 2014 - April
30, 2014. We found that the landing page has been successful in training users
against phishing. Forty six percent users clicked lesser number of phishing
URLs from January 2014 to April 2014 which shows that training from the landing
page helped users not to fall for phishing attacks. Our analysis shows that
phishers have started to modify their techniques by creating more legitimate
looking URLs and buying large number of domains to increase their activity. We
observed that phishers are exploiting ICANN accredited registrars to launch
their attacks even after strict surveillance. We saw that phishers are trying
to exploit free subdomain registration services to carry out attacks. In this
paper, we also compared the phishing e-mails used by phishers to lure victims
in 2008 and 2014. We found that the phishing e-mails have changed considerably
over time. Phishers have adopted new techniques like sending promotional
e-mails and emotionally targeting users in clicking phishing URLs
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Recent years have seen the rise of more sophisticated attacks including
advanced persistent threats (APTs) which pose severe risks to organizations and
governments by targeting confidential proprietary information. Additionally,
new malware strains are appearing at a higher rate than ever before. Since many
of these malware are designed to evade existing security products, traditional
defenses deployed by most enterprises today, e.g., anti-virus, firewalls,
intrusion detection systems, often fail at detecting infections at an early
stage.
We address the problem of detecting early-stage infection in an enterprise
setting by proposing a new framework based on belief propagation inspired from
graph theory. Belief propagation can be used either with "seeds" of compromised
hosts or malicious domains (provided by the enterprise security operation
center -- SOC) or without any seeds. In the latter case we develop a detector
of C&C communication particularly tailored to enterprises which can detect a
stealthy compromise of only a single host communicating with the C&C server.
We demonstrate that our techniques perform well on detecting enterprise
infections. We achieve high accuracy with low false detection and false
negative rates on two months of anonymized DNS logs released by Los Alamos
National Lab (LANL), which include APT infection attacks simulated by LANL
domain experts. We also apply our algorithms to 38TB of real-world web proxy
logs collected at the border of a large enterprise. Through careful manual
investigation in collaboration with the enterprise SOC, we show that our
techniques identified hundreds of malicious domains overlooked by
state-of-the-art security products
An Efficient Analytical Solution to Thwart DDoS Attacks in Public Domain
In this paper, an analytical model for DDoS attacks detection is proposed, in
which propagation of abrupt traffic changes inside public domain is monitored
to detect a wide range of DDoS attacks. Although, various statistical measures
can be used to construct profile of the traffic normally seen in the network to
identify anomalies whenever traffic goes out of profile, we have selected
volume and flow measure. Consideration of varying tolerance factors make
proposed detection system scalable to the varying network conditions and attack
loads in real time. NS-2 network simulator on Linux platform is used as
simulation testbed. Simulation results show that our proposed solution gives a
drastic improvement in terms of detection rate and false positive rate.
However, the mammoth volume generated by DDoS attacks pose the biggest
challenge in terms of memory and computational overheads as far as monitoring
and analysis of traffic at single point connecting victim is concerned. To
address this problem, a distributed cooperative technique is proposed that
distributes memory and computational overheads to all edge routers for
detecting a wide range of DDoS attacks at early stage.Comment: arXiv admin note: substantial text overlap with arXiv:1203.240
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