428 research outputs found
Defending Malicious Script Attacks Using Machine Learning Classifiers
Theweb application has become a primary target for cyber criminals by injecting malware especially JavaScript to performmalicious
activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious
activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an
interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper
method for dimensionality reduction. Supervisedmachine learning classifiers were used on the dataset for achieving high accuracy.
Experimental results show that our method can efficiently classify malicious code from benign code with promising results
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Machine learning based solutions have been successfully employed for
automatic detection of malware in Android applications. However, machine
learning models are known to lack robustness against inputs crafted by an
adversary. So far, the adversarial examples can only deceive Android malware
detectors that rely on syntactic features, and the perturbations can only be
implemented by simply modifying Android manifest. While recent Android malware
detectors rely more on semantic features from Dalvik bytecode rather than
manifest, existing attacking/defending methods are no longer effective. In this
paper, we introduce a new highly-effective attack that generates adversarial
examples of Android malware and evades being detected by the current models. To
this end, we propose a method of applying optimal perturbations onto Android
APK using a substitute model. Based on the transferability concept, the
perturbations that successfully deceive the substitute model are likely to
deceive the original models as well. We develop an automated tool to generate
the adversarial examples without human intervention to apply the attacks. In
contrast to existing works, the adversarial examples crafted by our method can
also deceive recent machine learning based detectors that rely on semantic
features such as control-flow-graph. The perturbations can also be implemented
directly onto APK's Dalvik bytecode rather than Android manifest to evade from
recent detectors. We evaluated the proposed manipulation methods for
adversarial examples by using the same datasets that Drebin and MaMadroid (5879
malware samples) used. Our results show that, the malware detection rates
decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just
a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure
Recommended from our members
Detecting Cross-Site Scripting Attacks Using Machine Learning
Cross-site scripting (XSS) is one of the most frequently occurring types of attacks on web applications, hence is of importance in information security. XSS is where the attacker injects malicious code, typically JavaScript, into the web application in order to be executed in the user’s browser. Identifying that a script is malicious is an important part of the defence of a web application. This paper investigates using SVM, k-NN and Random Forests to detect and limit these attacks, whether known or unknown, by building classifiers for JavaScript code. It demonstrated that using an interesting feature set combining language syntax and behavioural features results in classifiers that give high accuracy and precision on large real world data sets without restricting attention only to obfuscation
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
Recommended from our members
Preventing Cross-Site Scripting Attacks by Combining Classifiers
Cross-Site Scripting (XSS) is one of the most popular attacks targeting web applications. Using XSS attackers can obtain sensitive information or obtain unauthorized privileges. This motivates building a system that can recognise a malicious script when the attacker attempts to store it on a server, preventing the XSS attack. This work uses machine learning to power such a system. The system is based on a combination of classifiers, using cascading to build a two phase classifier and the stacking ensemble technique to improve accuracy. The system is evaluated and shown to achieve high accuracy and high detection rate on a large real world dataset
XSS-FP: Browser Fingerprinting using HTML Parser Quirks
There are many scenarios in which inferring the type of a client browser is
desirable, for instance to fight against session stealing. This is known as
browser fingerprinting. This paper presents and evaluates a novel
fingerprinting technique to determine the exact nature (browser type and
version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks
exercised through XSS. Our experiments show that the exact version of a web
browser can be determined with 71% of accuracy, and that only 6 tests are
sufficient to quickly determine the exact family a web browser belongs to
Detecting TCP SYN Flood Attack in the Cloud
In this paper, an approach to protecting virtual machines (VMs) against TCP SYN flood attack in a cloud environment is proposed. An open source cloud platform Eucalyptus is deployed and experimentation is carried out on this setup. We investigate attacks emanating from one VM to another in a multi-tenancy cloud environment. Various scenarios of the attack are executed on a webserver VM. To detect such attacks from a cloud provider’s perspective, a security mechanism involving a packet sniffer, feature extraction process, a classifier and an alerting component is proposed and implemented. We experiment with k-nearest neighbor and artificial neural network for classification of the attack. The dataset obtained from the attacks on the webserver VM is passed through the classifiers. The artificial neural network produced a F1 score of 1 with the test cases implying a 100% detection accuracy of the malicious attack traffic from legitimate traffic. The proposed security mechanism shows promising results in detecting TCP SYN flood attack behaviors in the cloud
A Deep-Learning Based Robust Framework Against Adversarial P.E. and Cryptojacking Malware
This graduate thesis introduces novel, deep-learning based frameworks that are resilient to adversarial P.E. and cryptojacking malware. We propose a method that uses a convolutional neural network (CNN) to classify image representations of malware, that provides robustness against numerous adversarial attacks. Our evaluation concludes that the image-based malware classifier is significantly more robust to adversarial attacks than a state-of-the-art ML-based malware classifier, and remarkably drops the evasion rate of adversarial samples to 0% in certain attacks. Further, we develop MINOS, a novel, lightweight cryptojacking detection system that accurately detects the presence of unwarranted mining activity in real-time. MINOS can detect mining activity with a low TNR and FPR, in an average of 25.9 milliseconds while using a maximum of 4% of CPU and 6.5% of RAM. Therefore, it can be concluded that the frameworks presented in this thesis attain high accuracy, are computationally inexpensive, and are resistant to adversarial perturbations
- …