1,721,061 research outputs found
A theoretical framework for dynamic classifier selection
No full textThe common operation mechanism of multiple classifier systems is the combination of classifier outputs. Some researchers have pointed out the potentialities of “dynamic classifier selection” as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper provides a theoretical framework for dynamic classifier selection. To this end, dynamic classifier selection is placed in the general framework of statistical decision theory and it is showed that, under some assumptions, the optimal Bayes classifier can be obtained by the selection of non-optimal classifier
Intrusion detection in computer networks by multiple classifier systems
No full textThe security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools are currently developed. Intrusion Detection Systems aim at detecting intruder who eluded the "first line" protection. In this paper, a pattern recognition approach to network intrusion detection based on the multiple classifier systems paradigm is proposed. The potentialities of classifier combination for data fusion and some open issues are outlined. © 2002 IEEE
Instance-Based Relevance Feedback for Image Retrieval
No full textHigh retrieval precision in content-based image retrieval can be
attained by adopting relevance feedback mechanisms. These
mechanisms require that the user judges the quality of the results of
the query by marking all the retrieved images as being either
relevant or not. Then, the search engine exploits this information to
adapt the search to better meet user’s needs. At present, the vast
majority of proposed relevance feedback mechanisms are
formulated in terms of search model that has to be optimized. Such
an optimization involves the modification of some search
parameters so that the nearest neighbor of the query vector contains
the largest number of relevant images. In this paper, a different
approach to relevance feedback is proposed. After the user
provides the first feedback, following retrievals are not based on knn search, but on the computation of a relevance score for each
image of the database. This score is computed as a function of two
distances, namely the distance from the nearest non-relevant image
and the distance from the nearest relevant one. Images are then
ranked according to this score and the top k images are displayed.
Reported results on three image data sets show that the proposed
mechanism outperforms other state-of-the-art relevance feedback
mechanisms
Pattern Recognition for Intrusion Detection in Computer Networks
No full textNowadays an increasing number of commercial and public services are offered through the Internet, so that security is becoming a key issue. The so-called ?attacks? on Internet service providers are carried out by exploiting both unknown weaknesses or bugs that are always contained in system and application software, and complex unforeseen interactions between software components and/or network protocols [1], [2]. The objective of computer attacks is to obtain unauthorized access to the information stored in computer systems and/or to cause a temporary unavailability of its services. The so-called ?first line? of defence against attacks is made up of a number of access restriction policies that act as a coarse grain filter. Intrusion detection systems (IDSs) are the fine grain filter placed inside the protected network, that look for known or potential threats in network traffic and/or in audit data recorded by hosts [2]
Intrusion detection in computer systems as a pattern recognition task in adversarial environment: a critical review
No full textTowards adversarial malware detection: lessons learned from PDF-based attacks
Full text linkMalware 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 article by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings
Ensemble learning for Intrusion Detection in Computer Networks
No full textThe security of computer networks plays a strategic role in modern
computer systems. In order to enforce high protection levels against threats, a
number of software tools are currently developed. Intrusion Detection Systems aim
at detecting intruder who eluded the "first line" protection. In this paper, a pattern
recognition approach to network intrusion detection based on ensemble learning
paradigms is proposed. The potentialities of such an approach for data fusion and
some open issues are outline
Automotive cybersecurity: Foundations for next-generation vehicles
No full textThe automotive industry is experiencing a serious transformation due to a digitalisation process and the transition to the new paradigm of Mobility-As-A-Service. The next-generation vehicles are going to be very complex cyber-physical systems, whose design must be reinvented to fulfil the increasing demand of smart services, both for safety and entertainment purposes, causing the manufacturers' model to converge towards that of IT companies. Connected cars and autonomous driving are the preeminent factors that drive along this route, and they cause the necessity of a new design to address the emerging cybersecurity issues: The 'old' automotive architecture relied on a single closed network, with no external communications; modern vehicles are going to be always connected indeed, which means the attack surface will be much more extended. The result is the need for a paradigm shift towards a secure-by-design approach. In this paper, we propose a systematisation of knowledge about the core cybersecurity aspects to consider when designing a modern car. The major focus is pointed on the in-vehicle network, including its requirements, the current most used protocols and their vulnerabilities. Moreover, starting from the attackers' goals and strategies, we outline the proposed solutions and the main projects towards secure architectures. In this way, we aim to provide the foundations for more targeted analyses about the security impact of autonomous driving and connected cars
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