91,954 research outputs found
Cyber-crime Science = Crime Science + Information Security
Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions
Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges
open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture
An Efficient Analytical Solution to Thwart DDoS Attacks in Public Domain
In this paper, an analytical model for DDoS attacks detection is proposed, in
which propagation of abrupt traffic changes inside public domain is monitored
to detect a wide range of DDoS attacks. Although, various statistical measures
can be used to construct profile of the traffic normally seen in the network to
identify anomalies whenever traffic goes out of profile, we have selected
volume and flow measure. Consideration of varying tolerance factors make
proposed detection system scalable to the varying network conditions and attack
loads in real time. NS-2 network simulator on Linux platform is used as
simulation testbed. Simulation results show that our proposed solution gives a
drastic improvement in terms of detection rate and false positive rate.
However, the mammoth volume generated by DDoS attacks pose the biggest
challenge in terms of memory and computational overheads as far as monitoring
and analysis of traffic at single point connecting victim is concerned. To
address this problem, a distributed cooperative technique is proposed that
distributes memory and computational overheads to all edge routers for
detecting a wide range of DDoS attacks at early stage.Comment: arXiv admin note: substantial text overlap with arXiv:1203.240
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