1,029 research outputs found
DDoS-Capable IoT Malwares: comparative analysis and Mirai Investigation
The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
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
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.First author draf
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
Automatic Detection of Malware-Generated Domains with Recurrent Neural Models
Modern malware families often rely on domain-generation algorithms (DGAs) to
determine rendezvous points to their command-and-control server. Traditional
defence strategies (such as blacklisting domains or IP addresses) are
inadequate against such techniques due to the large and continuously changing
list of domains produced by these algorithms. This paper demonstrates that a
machine learning approach based on recurrent neural networks is able to detect
domain names generated by DGAs with high precision. The neural models are
estimated on a large training set of domains generated by various malwares.
Experimental results show that this data-driven approach can detect
malware-generated domain names with a F_1 score of 0.971. To put it
differently, the model can automatically detect 93 % of malware-generated
domain names for a false positive rate of 1:100.Comment: Submitted to NISK 201
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