760,832 research outputs found
Ring-LWE Cryptography for the Number Theorist
In this paper, we survey the status of attacks on the ring and polynomial
learning with errors problems (RLWE and PLWE). Recent work on the security of
these problems [Eisentr\"ager-Hallgren-Lauter, Elias-Lauter-Ozman-Stange] gives
rise to interesting questions about number fields. We extend these attacks and
survey related open problems in number theory, including spectral distortion of
an algebraic number and its relationship to Mahler measure, the monogenic
property for the ring of integers of a number field, and the size of elements
of small order modulo q.Comment: 20 Page
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector
Machine Applications
Dos and Don'ts of Machine Learning in Computer Security
With the growing processing power of computing systems and the increasing
availability of massive datasets, machine learning algorithms have led to major
breakthroughs in many different areas. This development has influenced computer
security, spawning a series of work on learning-based security systems, such as
for malware detection, vulnerability discovery, and binary code analysis.
Despite great potential, machine learning in security is prone to subtle
pitfalls that undermine its performance and render learning-based systems
potentially unsuitable for security tasks and practical deployment. In this
paper, we look at this problem with critical eyes. First, we identify common
pitfalls in the design, implementation, and evaluation of learning-based
security systems. We conduct a study of 30 papers from top-tier security
conferences within the past 10 years, confirming that these pitfalls are
widespread in the current security literature. In an empirical analysis, we
further demonstrate how individual pitfalls can lead to unrealistic performance
and interpretations, obstructing the understanding of the security problem at
hand. As a remedy, we propose actionable recommendations to support researchers
in avoiding or mitigating the pitfalls where possible. Furthermore, we identify
open problems when applying machine learning in security and provide directions
for further research.Comment: to appear at USENIX Security Symposium 202
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation
Many security and privacy problems can be modeled as a graph classification
problem, where nodes in the graph are classified by collective classification
simultaneously. State-of-the-art collective classification methods for such
graph-based security and privacy analytics follow the following paradigm:
assign weights to edges of the graph, iteratively propagate reputation scores
of nodes among the weighted graph, and use the final reputation scores to
classify nodes in the graph. The key challenge is to assign edge weights such
that an edge has a large weight if the two corresponding nodes have the same
label, and a small weight otherwise. Although collective classification has
been studied and applied for security and privacy problems for more than a
decade, how to address this challenge is still an open question. In this work,
we propose a novel collective classification framework to address this
long-standing challenge. We first formulate learning edge weights as an
optimization problem, which quantifies the goals about the final reputation
scores that we aim to achieve. However, it is computationally hard to solve the
optimization problem because the final reputation scores depend on the edge
weights in a very complex way. To address the computational challenge, we
propose to jointly learn the edge weights and propagate the reputation scores,
which is essentially an approximate solution to the optimization problem. We
compare our framework with state-of-the-art methods for graph-based security
and privacy analytics using four large-scale real-world datasets from various
application scenarios such as Sybil detection in social networks, fake review
detection in Yelp, and attribute inference attacks. Our results demonstrate
that our framework achieves higher accuracies than state-of-the-art methods
with an acceptable computational overhead.Comment: Network and Distributed System Security Symposium (NDSS), 2019.
Dataset link: http://gonglab.pratt.duke.edu/code-dat
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN environment
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack
Tactics, Techniques and Procedures (TTPs) to Augment Cyber Threat Intelligence (CTI): A Comprehensive Study
Sharing Threat Intelligence is now one of the biggest trends in cyber security industry. Today, no one can deny the necessity for information sharing to fight the cyber battle. The massive production of raw and redundant data coupled with the increasingly innovative attack vectors of the perpetrators demands an ecosystem to scrutinize the information, detect and react to take a defensive stance. Having enough sources for threat intelligence or having too many security tools are the least of our problems. The main challenge lies in threat knowledge management, interoperability between different security tools and then converting these filtered data into actionable items across multiple devices. Large datasets may help filtering the massive information gathering, open standards may somewhat facilitate the interoperability issues, and machine learning may partly aid the learning of malicious traits and features of attack, but how do we coordinate the actionable responses across devices, networks, and other ecosystems to be proactive rather than reactive? This paper presents a study of current threat intelligence landscape (Tactic), information sources, basic Indicators of Compromise (IOCs) (Technique) and STIX and TAXII standard as open source frameworks (Procedure) to augment Cyber Threat Intelligence (CTI) sharing
Data Mining and Machine Learning for Software Engineering
Software engineering is one of the most utilizable research areas for data mining. Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may occur. In the early phases of software development, analyzing software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques
A Survey of Privacy Attacks in Machine Learning
As machine learning becomes more widely used, the need to study its
implications in security and privacy becomes more urgent. Although the body of
work in privacy has been steadily growing over the past few years, research on
the privacy aspects of machine learning has received less focus than the
security aspects. Our contribution in this research is an analysis of more than
40 papers related to privacy attacks against machine learning that have been
published during the past seven years. We propose an attack taxonomy, together
with a threat model that allows the categorization of different attacks based
on the adversarial knowledge, and the assets under attack. An initial
exploration of the causes of privacy leaks is presented, as well as a detailed
analysis of the different attacks. Finally, we present an overview of the most
commonly proposed defenses and a discussion of the open problems and future
directions identified during our analysis.Comment: Under revie
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