10,695 research outputs found
Detection of drive-by download attacks using machine learning approach
Copyright © 2017, IGI Global. Drive-by download refers to attacks that automatically download malwares to user\u27s computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate
An Evasion and Counter-Evasion Study in Malicious Websites Detection
Malicious websites are a major cyber attack vector, and effective detection
of them is an important cyber defense task. The main defense paradigm in this
regard is that the defender uses some kind of machine learning algorithms to
train a detection model, which is then used to classify websites in question.
Unlike other settings, the following issue is inherent to the problem of
malicious websites detection: the attacker essentially has access to the same
data that the defender uses to train its detection models. This 'symmetry' can
be exploited by the attacker, at least in principle, to evade the defender's
detection models. In this paper, we present a framework for characterizing the
evasion and counter-evasion interactions between the attacker and the defender,
where the attacker attempts to evade the defender's detection models by taking
advantage of this symmetry. Within this framework, we show that an adaptive
attacker can make malicious websites evade powerful detection models, but
proactive training can be an effective counter-evasion defense mechanism. The
framework is geared toward the popular detection model of decision tree, but
can be adapted to accommodate other classifiers
Malware Detection using Machine Learning and Deep Learning
Research shows that over the last decade, malware has been growing
exponentially, causing substantial financial losses to various organizations.
Different anti-malware companies have been proposing solutions to defend
attacks from these malware. The velocity, volume, and the complexity of malware
are posing new challenges to the anti-malware community. Current
state-of-the-art research shows that recently, researchers and anti-virus
organizations started applying machine learning and deep learning methods for
malware analysis and detection. We have used opcode frequency as a feature
vector and applied unsupervised learning in addition to supervised learning for
malware classification. The focus of this tutorial is to present our work on
detecting malware with 1) various machine learning algorithms and 2) deep
learning models. Our results show that the Random Forest outperforms Deep
Neural Network with opcode frequency as a feature. Also in feature reduction,
Deep Auto-Encoders are overkill for the dataset, and elementary function like
Variance Threshold perform better than others. In addition to the proposed
methodologies, we will also discuss the additional issues and the unique
challenges in the domain, open research problems, limitations, and future
directions.Comment: 11 Pages and 3 Figure
User-Behavior Based Detection of Infection Onset
A major vector of computer infection is through exploiting software or design flaws in networked applications such as the browser. Malicious code can be fetched and executed on a victim’s machine without the user’s permission, as in drive-by download (DBD) attacks. In this paper, we describe a new tool called DeWare for detecting the onset of infection delivered through vulnerable applications. DeWare explores and enforces causal relationships between computer-related human behaviors and system properties, such as file-system access and process execution. Our tool can be used to provide real time protection of a personal computer, as well as for diagnosing and evaluating untrusted websites for forensic purposes. Besides the concrete DBD detection solution, we also formally define causal relationships between user actions and system events on a host. Identifying and enforcing correct causal relationships have important applications in realizing advanced and secure operating systems. We perform extensive experimental evaluation, including a user study with 21 participants, thousands of legitimate websites (for testing false alarms), as well as 84 malicious websites in the wild. Our results show that DeWare is able to correctly distinguish legitimate download events from unauthorized system events with a low false positive rate (< 1%)
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
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
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