425 research outputs found
OnionBots: Subverting Privacy Infrastructure for Cyber Attacks
Over the last decade botnets survived by adopting a sequence of increasingly
sophisticated strategies to evade detection and take overs, and to monetize
their infrastructure. At the same time, the success of privacy infrastructures
such as Tor opened the door to illegal activities, including botnets,
ransomware, and a marketplace for drugs and contraband. We contend that the
next waves of botnets will extensively subvert privacy infrastructure and
cryptographic mechanisms. In this work we propose to preemptively investigate
the design and mitigation of such botnets. We first, introduce OnionBots, what
we believe will be the next generation of resilient, stealthy botnets.
OnionBots use privacy infrastructures for cyber attacks by completely
decoupling their operation from the infected host IP address and by carrying
traffic that does not leak information about its source, destination, and
nature. Such bots live symbiotically within the privacy infrastructures to
evade detection, measurement, scale estimation, observation, and in general all
IP-based current mitigation techniques. Furthermore, we show that with an
adequate self-healing network maintenance scheme, that is simple to implement,
OnionBots achieve a low diameter and a low degree and are robust to
partitioning under node deletions. We developed a mitigation technique, called
SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and
discuss a set of techniques that can enable subsequent waves of Super
OnionBots. In light of the potential of such botnets, we believe that the
research community should proactively develop detection and mitigation methods
to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure
Botnet Detection using Social Graph Analysis
Signature-based botnet detection methods identify botnets by recognizing
Command and Control (C\&C) traffic and can be ineffective for botnets that use
new and sophisticate mechanisms for such communications. To address these
limitations, we propose a novel botnet detection method that analyzes the
social relationships among nodes. The method consists of two stages: (i)
anomaly detection in an "interaction" graph among nodes using large deviations
results on the degree distribution, and (ii) community detection in a social
"correlation" graph whose edges connect nodes with highly correlated
communications. The latter stage uses a refined modularity measure and
formulates the problem as a non-convex optimization problem for which
appropriate relaxation strategies are developed. We apply our method to
real-world botnet traffic and compare its performance with other community
detection methods. The results show that our approach works effectively and the
refined modularity measure improves the detection accuracy.Comment: 7 pages. Allerton Conferenc
Reaction to New Security Threat Class
Each new identified security threat class triggers new research and
development efforts by the scientific and professional communities. In this
study, we investigate the rate at which the scientific and professional
communities react to new identified threat classes as it is reflected in the
number of patents, scientific articles and professional publications over a
long period of time. The following threat classes were studied: Phishing; SQL
Injection; BotNet; Distributed Denial of Service; and Advanced Persistent
Threat. Our findings suggest that in most cases it takes a year for the
scientific community and more than two years for industry to react to a new
threat class with patents. Since new products follow patents, it is reasonable
to expect that there will be a window of approximately two to three years in
which no effective product is available to cope with the new threat class
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
Master of Puppets: Analyzing And Attacking A Botnet For Fun And Profit
A botnet is a network of compromised machines (bots), under the control of an
attacker. Many of these machines are infected without their owners' knowledge,
and botnets are the driving force behind several misuses and criminal
activities on the Internet (for example spam emails). Depending on its
topology, a botnet can have zero or more command and control (C&C) servers,
which are centralized machines controlled by the cybercriminal that issue
commands and receive reports back from the co-opted bots.
In this paper, we present a comprehensive analysis of the command and control
infrastructure of one of the world's largest proprietary spamming botnets
between 2007 and 2012: Cutwail/Pushdo. We identify the key functionalities
needed by a spamming botnet to operate effectively. We then develop a number of
attacks against the command and control logic of Cutwail that target those
functionalities, and make the spamming operations of the botnet less effective.
This analysis was made possible by having access to the source code of the C&C
software, as well as setting up our own Cutwail C&C server, and by implementing
a clone of the Cutwail bot. With the help of this tool, we were able to
enumerate the number of bots currently registered with the C&C server,
impersonate an existing bot to report false information to the C&C server, and
manipulate spamming statistics of an arbitrary bot stored in the C&C database.
Furthermore, we were able to make the control server inaccessible by conducting
a distributed denial of service (DDoS) attack. Our results may be used by law
enforcement and practitioners to develop better techniques to mitigate and
cripple other botnets, since many of findings are generic and are due to the
workflow of C&C communication in general
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