A Survey of Network-based Security Attacks

Khan, K.; Goodridge, Wayne

doi:10.35444/ijana.2019.10051

Cited by 5 publications

(2 citation statements)

References 35 publications

(36 reference statements)

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“…While sensing the frequency band of the SU, an individual sensing provide false detection due to couple of problems namely multipath fading and hidden terminal problems occurred in the wireless communication [20]. To overcome such problem statements, cooperative spectral sensing (CSS) has been introduced to detect the highly complicated problems and high prior for the security measures in the network [21].…”

Section: Introductionmentioning

confidence: 99%

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

Biradar,

Singh,

Patil

2024

Preprint

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The demand for wireless transmission and existence of the cognitive radio networks (CRN) insist the need for detecting the Primary User Emulsion Attack (PUEA) that authenticates the network as the licensed user for accessing the spectrum in CRN. Such condition creates disruptions in the communications. Hence, in this research, PUEA detection in the CRN is proposed using the deep learning and key sharing approach (KCS2TM model). The key sharing approach enables sharing the secret key between Primary User (PU) and Secondary User (SU) that supports the generation of the communication data, which easily assists in detecting the PUEA for which the ChimSp optimizer enabled Bidirectional Long Short Term Memory (CS2TM) model is proposed. The ChimSp optimizer is designed theoretically and mathematically to train the classifier and ensure the improved detection performances. The model provides increased accuracy, highly reliable, reduce computational cost and time are the supremacy of the model. The performance of the model is analyzed with the existing methods, which shows better accuracy sensitivity and specificity of 85.01%, 87.61% and 82.42% compared to other state of art methods.

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Section: Introductionmentioning

confidence: 99%

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

Biradar,

Singh,

Patil

2024

Preprint

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“…The Edge-Enhanced Video Streaming Taxonomy (EEVST) is a structured framework designed to provide a comprehensive overview of the myriad roles that edge computing plays within the video streaming landscape. This taxonomy aims to categorize and elucidate the diverse functionalities of edge computing, covering aspects such as content delivery, latency reduction, quality of service enhancement, security [6], user experience optimization, network efficiency, cost management, and integration with emerging technologies. EEVST serves as a valuable resource for industry professionals, researchers, and anyone interested in understanding how edge computing is redefining and improving the video streaming experience.…”

Section: Introductionmentioning

confidence: 99%

A Edge-Enhanced Video Streaming Taxonomy (EEVST)

Khan

2023

IJLERA

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The Edge-Enhanced Video Streaming Taxonomy (EEVST) provides a comprehensive framework for understanding the multifaceted roles of edge computing in video streaming ecosystems. EEVST categorizes the diverse functionalities of edge computing, spanning content delivery, latency reduction, quality of service enhancement, security, user experience optimization, network efficiency, cost management, and integration with emerging technologies like 5G and IoT. By offering a structured approach to this dynamic field, EEVST serves as a valuable resource for industry professionals and researchers seeking to harness the potential of edge computing in redefining and improving the video streaming experience for end-users.

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A Novel Improvement With an Effective Expansion to Enhance the MD5 Hash Function for Verification of a Secure E-Document

Ali

Farhan

2020

IEEE Access

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MD5 is a one-way cryptographic function used in various fields for maintaining data integrity. The application of a Hash function can provide much protection and privacy and subsequently reduce data usage. Most users are familiar with validating electronic documents based on a Hash function, such as the MD5 algorithm and other hash functions, to demonstrate the data integrity. There are many weaknesses of the current MD5 algorithm, mainly its failures and weaknesses against varying types of attacks, such as brute force attacks, rainbow table attacks, and Christmas attacks. Therefore, the method proposed in this paper enhances the MD5 algorithm by adding a dynamic variable length and a high efficiency that simulates the highest security available. Whereas the logistic system was used to encode ribonucleic acid (RNA) by generating a random matrix based on a new key that was created using the initial permutation (IP) tables used in the data encryption stander (DES) with the linear-feedback shift register (LFSR), this work proposes several structures to improve the MD5 hash function. The experimental results demonstrate its high resistance to hackers while maintaining a suitable duration. This paper discusses the design of a confident hash algorithm. This algorithm has characteristics that enable it to succeed in the field of digital authentication and data integrity.

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A Survey of Network-based Security Attacks

Cited by 5 publications

References 35 publications

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

KCS 2 TM: A key sharing approach and Deep Learning Model for Primary User Emulsion Attack detection in Cognitive Radio Networks

A Edge-Enhanced Video Streaming Taxonomy (EEVST)

A Novel Improvement With an Effective Expansion to Enhance the MD5 Hash Function for Verification of a Secure E-Document

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