3,261 research outputs found
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
Evading Classifiers by Morphing in the Dark
Learning-based systems have been shown to be vulnerable to evasion through
adversarial data manipulation. These attacks have been studied under
assumptions that the adversary has certain knowledge of either the target model
internals, its training dataset or at least classification scores it assigns to
input samples. In this paper, we investigate a much more constrained and
realistic attack scenario wherein the target classifier is minimally exposed to
the adversary, revealing on its final classification decision (e.g., reject or
accept an input sample). Moreover, the adversary can only manipulate malicious
samples using a blackbox morpher. That is, the adversary has to evade the
target classifier by morphing malicious samples "in the dark". We present a
scoring mechanism that can assign a real-value score which reflects evasion
progress to each sample based on the limited information available. Leveraging
on such scoring mechanism, we propose an evasion method -- EvadeHC -- and
evaluate it against two PDF malware detectors, namely PDFRate and Hidost. The
experimental evaluation demonstrates that the proposed evasion attacks are
effective, attaining evasion rate on the evaluation dataset.
Interestingly, EvadeHC outperforms the known classifier evasion technique that
operates based on classification scores output by the classifiers. Although our
evaluations are conducted on PDF malware classifier, the proposed approaches
are domain-agnostic and is of wider application to other learning-based
systems
Towards a Robust Defense: A Multifaceted Approach to the Detection and Mitigation of Neural Backdoor Attacks through Feature Space Exploration and Analysis
From voice assistants to self-driving vehicles, machine learning(ML), especially deep learning, revolutionizes the way we work and live, through the wide adoption in a broad range of applications. Unfortunately, this widespread use makes deep learning-based systems a desirable target for cyberattacks, such as generating adversarial examples to fool a deep learning system to make wrong decisions. In particular, many recent studies have revealed that attackers can corrupt the training of a deep learning model, e.g., through data poisoning, or distribute a deep learning model they created with “backdoors” planted, e.g., distributed as part of a software library, so that the attacker can easily craft system inputs that grant unauthorized access or lead to catastrophic errors or failures.
This dissertation aims to develop a multifaceted approach for detecting and mitigating such neural backdoor attacks by exploiting their unique characteristics in the feature space. First of all, a framework called GangSweep is designed to utilize the capabilities of Generative Adversarial Networks (GAN) to approximate poisoned sample distributions in the feature space, to detect neural backdoor attacks. Unlike conventional methods, GangSweep exposes all attacker-induced artifacts, irrespective of their complexity or obscurity. By leveraging the statistical disparities between these artifacts and natural adversarial perturbations, an efficient detection scheme is devised. Accordingly, the backdoored model can be purified through label correction and fine-tuning
Secondly, this dissertation focuses on the sample-targeted backdoor attacks, a variant of neural backdoor that targets specific samples. Given the absence of explicit triggers in such models, traditional detection methods falter. Through extensive analysis, I have identified a unique feature space property of these attacks, where they induce boundary alterations, creating discernible “pockets” around target samples. Based on this critical observation, I introduce a novel defense scheme that encapsulates these malicious pockets within a tight convex hull in the feature space, and then design an algorithm to identify such hulls and remove the backdoor through model fine-tuning. The algorithm demonstrates high efficacy against a spectrum of sample-targeted backdoor attacks.
Lastly, I address the emerging challenge of backdoor attacks in multimodal deep neural networks, in particular vision-language model, a growing concern in real-world applications. Discovering that there is a strong association between the image trigger and the target text in the feature space of the backdoored vision-language model, I design an effective algorithm to expose the malicious text and image trigger by jointly searching in the shared feature space of the vision and language modalities
Detailed Review on The Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs) and Defense Strategies
The development of Software Defined Networking (SDN) has altered the landscape of computer networking in recent years. Its scalable architecture has become a blueprint for the design of several advanced future networks. To achieve improve and efficient monitoring, control and management capabilities of the network, software defined networks differentiate or decouple the control logic from the data forwarding plane. As a result, logical control is centralized solely in the controller. Due to the centralized nature, SDNs are exposed to several vulnerabilities such as Spoofing, Flooding, and primarily Denial of Service (DoS) and Distributed Denial of Service (DDoS) among other attacks. In effect, the performance of SDN degrades based on these attacks. This paper presents a comprehensive review of several DoS and DDoS defense/mitigation strategies and classifies them into distinct classes with regards to the methodologies employed. Furthermore, suggestions were made to enhance current mitigation strategies accordingly
GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE
During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack
Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions
Although backdoor learning is an active research topic in the NLP domain, the
literature lacks studies that systematically categorize and summarize backdoor
attacks and defenses. To bridge the gap, we present a comprehensive and
unifying study of backdoor learning for NLP by summarizing the literature in a
systematic manner. We first present and motivate the importance of backdoor
learning for building robust NLP systems. Next, we provide a thorough account
of backdoor attack techniques, their applications, defenses against backdoor
attacks, and various mitigation techniques to remove backdoor attacks. We then
provide a detailed review and analysis of evaluation metrics, benchmark
datasets, threat models, and challenges related to backdoor learning in NLP.
Ultimately, our work aims to crystallize and contextualize the landscape of
existing literature in backdoor learning for the text domain and motivate
further research in the field. To this end, we identify troubling gaps in the
literature and offer insights and ideas into open challenges and future
research directions. Finally, we provide a GitHub repository with a list of
backdoor learning papers that will be continuously updated at
https://github.com/marwanomar1/Backdoor-Learning-for-NLP
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