Relevant bibliographies by topics / Cuckoo signatures

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  1. Journal articles
  2. Dissertations / Theses

Academic literature on the topic 'Cuckoo signatures'

Author: Grafiati
Published: 28 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Cuckoo signatures"

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Caves, Eleanor M., Martin Stevens, Edwin S. Iversen, and Claire N. Spottiswoode. "Hosts of avian brood parasites have evolved egg signatures with elevated information content." Proceedings of the Royal Society B: Biological Sciences 282, no. 1810 (July 7, 2015): 20150598. http://dx.doi.org/10.1098/rspb.2015.0598.

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Hosts of brood-parasitic birds must distinguish their own eggs from parasitic mimics, or pay the cost of mistakenly raising a foreign chick. Egg discrimination is easier when different host females of the same species each lay visually distinctive eggs (egg ‘signatures’), which helps to foil mimicry by parasites. Here, we ask whether brood parasitism is associated with lower levels of correlation between different egg traits in hosts, making individual host signatures more distinctive and informative. We used entropy as an index of the potential information content encoded by nine aspects of colour, pattern and luminance of eggs of different species in two African bird families (Cisticolidae parasitized by cuckoo finches Anomalospiza imberbis , and Ploceidae by diederik cuckoos Chrysococcyx caprius ). Parasitized species showed consistently higher entropy in egg traits than did related, unparasitized species. Decomposing entropy into two variation components revealed that this was mainly driven by parasitized species having lower levels of correlation between different egg traits, rather than higher overall levels of variation in each individual egg trait. This suggests that irrespective of the constraints that might operate on individual egg traits, hosts can further improve their defensive ‘signatures' by arranging suites of egg traits into unpredictable combinations.
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Useche-Peláez, David Esteban, Daniel Orlando Díaz-López, Daniela Sepúlveda-Alzate, and Diego Edison Cabuya-Padilla. "Building malware classificators usable by State security agencies." ITECKNE 15, no. 2 (December 7, 2018): 107–21. http://dx.doi.org/10.15332/iteckne.v15i2.2072.

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Sandboxing has been used regularly to analyze software samples and determine if these contain suspicious properties or behaviors. Even if sandboxing is a powerful technique to perform malware analysis, it requires that a malware analyst performs a rigorous analysis of the results to determine the nature of the sample: goodware or malware. This paper proposes two machine learning models able to classify samples based on signatures and permissions obtained through Cuckoo sandbox, Androguard and VirusTotal. The developed models are also tested obtaining an acceptable percentage of correctly classified samples, being in this way useful tools for a malware analyst. A proposal of architecture for an IoT sentinel that uses one of the developed machine learning model is also showed. Finally, different approaches, perspectives, and challenges about the use of sandboxing and machine learning by security teams in State security agencies are also shared.
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Kaur, Harjinder, and Gaganpreet Kaur. "Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)." International Journal of Computer Applications 107, no. 18 (December 18, 2014): 28–32. http://dx.doi.org/10.5120/18852-0399.

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Rengarajan, A., and M. Mohammed Thaha. "SPCACF: Secured Privacy-Conserving Authentication Scheme using Cuckoo Filter in VANET." Scalable Computing: Practice and Experience 21, no. 1 (March 19, 2020): 101–5. http://dx.doi.org/10.12694/scpe.v21i1.1626.

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Providing security for vehicular communication is essential since the network is vulnerable to severe attacks.The message authentication between vehicles and pavement units are essential for the purpose of security. Messages that passed between the vehicles should be encrypted and verified before the vehicle nodes could be trusted. The original identity of nodes can only be traceable by authorized parties and should not be exposed at any cause. However authentication between vehicles during message transformation does not guarantees message authentication rate accurately. To address these issues, the SPCACF scheme is proposed which is based on software devoid of relying on any particular hardware. Binary search algorithm is added in partial with Cuckoo Filter to achieve higher accomplishment than the preceding schemes in the batch wise verification phase. In order to guarantee that it can assure message authentication constraint, existential enforceability of underlying signature against adaptively chosen-message attack is proved under the positive filter pool method.
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Xie, Gaopeng, Yuling Liu, Guojiang Xin, and Qiuwei Yang. "Blockchain-Based Cloud Data Integrity Verification Scheme with High Efficiency." Security and Communication Networks 2021 (April 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/9921209.

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With the large-scale application of cloud storage, how to ensure cloud data integrity has become an important issue. Although many methods have been proposed, they still have their limitations. This paper improves some defects of the previous methods and proposes an efficient cloud data integrity verification scheme based on blockchain. In this paper, we proposed a lattice signature algorithm to resist quantum computing and introduced cuckoo filter to simplify the computational overhead of the user verification phase. Finally, the decentralized blockchain network is introduced to replace traditional centralized audit to publicize and authenticate the verification results, which improves the transparency and the security of this scheme. Security analysis shows that our scheme can resist malicious attacks and experimental results show that our scheme has high efficiency, especially in the user verification phase.
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Siddiqui, Shadab, Manuj Darbari, and Diwakar Yagyasen. "Enhancing the Capability of Load Management Techniques in Cloud Using H_FAC Algorithm Optimization." International Journal of e-Collaboration 16, no. 2 (April 2020): 65–81. http://dx.doi.org/10.4018/ijec.2020040105.

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Load balancing is a major research discipline in Cloud computing. The services are provided to users on pay as you go manner. Although a lot of algorithms have been proposed for load balancing, but performance is still an issue. The authors have proposed a new hybrid algorithm H_FAC to optimize the performance in cloud computing. The hybrid technique combines cuckoo search along with the Firefly algorithm of swarm intelligence. The benefit of using hybridization technique is that strength of one algorithm will overcome the shortcomings of other algorithms. Blockchain ID based Signature technique is used to ensure the authenticity of cloud service provider. The experimental results of H_FAC minimize the standard deviation, execution time significantly and improved throughput thereby optimizing the performance. The hybrid algorithm is also compared with other algorithms like ant colony optimization, artificial bee colony, round robin, FCFS and modified throttled. This approach helps the users to get the resources from authentic resource providers with a reduced execution time.
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7

"Predicting the Dynamic Behaviour of Malware using RNN." International Journal of Engineering and Advanced Technology 9, no. 3 (February 29, 2020): 3557–59. http://dx.doi.org/10.35940/ijeat.c6291.029320.

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Malware analysis can be classified as static and dynamic analysis. Static analysis involves the inspection of the malicious code by observing the features such as file signatures, strings etc. The code obfuscation techniques such as string encryption, class encryption etc can be easily applied on static code analysis. Dynamic or behavioural data is more difficult to obfuscate as the malicious payload may have already been executed before it is detected. In this paper, the dataset is obtained from repositories such as VirusShare and is run in Cuckoo Sandbox with the help of the agent.py. The dynamic features are extracted from the generated Cuckoo logs in the html and JSON format and it has to be determined whether it is malicious or not using recurrent neural networks. Recurrent Neural Networks are capable of predicting whether an executable is malicious and have the ability to capture time-series data.
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Dissertations / Theses on the topic "Cuckoo signatures"

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Sidor, Samuel. "Vylepšený sandboxing pro pokročilé kmeny malwaru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442403.

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This Master’s thesis describes reverse engineering with focus on malware analysis. Reader will be informed about theoretical description of static and dynamic analysis. These techniques are later used on analysis of 5 malware families with focus on detection of used anti-sandbox techniques. After that new theoretical improvements are proposed with detection of anti-sandbox techniques or fully avoiding such anti-sandbox evasion techniques. Finally these changes are implemented on main sandbox of Avast Software from which reader can see how effective these improvements are.
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