Cybersecurity for Data Science: Issues, Opportunities, and Challenges

Humayun, Mamoona; Jhanjhi, N. Z.; Talib, Mohammad; Shah, Mudassar Hussain; Suseendran, G.

doi:10.1007/978-981-16-3153-5_46

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…This review can give important insights into the current state of the art, highlight gaps and limits, and influence future research paths in this subject. In our investigation of Sybil attack detection in Mobile Ad Hoc Networks (MANETs), our research builds upon foundational insights presented in [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]…”

Section: Looking Into the Above-mentioned Tablementioning

confidence: 99%

ML-Based Detection of Sybil Attack on MANETS

Ashraf,

Nawaz,

Jhanjhi

2024

Preprint

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Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.

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Section: Looking Into the Above-mentioned Tablementioning

confidence: 99%

ML-Based Detection of Sybil Attack on MANETS

Ashraf,

Nawaz,

Jhanjhi

2024

Preprint

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“…In this research, the authors [33] introduced a method that runs on the Android platform and encrypts texts before they are sent over the network by the user allowing them to encrypt communications before transmitting data over the network. In the continuum of fortifying SMS security measures, this study builds upon earlier works [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] to address the persistent threats posed by plaintext and dictionary attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved SMS Security Technique to Avoid Plaintext and Dictionary Attacks

Ashraf,

Zam Zam,

Jhanjhi

2024

Preprint

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: Digital communication is becoming an integral component of contemporary society and the use of mobile devices is increasing faster than ever. The function of message communication is becoming more popular as for mobile phone users, a short messaging service (SMS) requires an extremely high level of security. Any SMS that enters a person's smartphone must be considered a private affair, with privacy and security safeguarded. This research study found a novel Deoxyribonucleic acid (DNA) Cryptography method that uses a dynamic DNA sequence table to increase security. Cryptography is a popular approach for ensuring the confidentiality of a message. This paper proposed an Efficient Cryptographic Scheme for SMS (ECSS) algorithm to protect texts received from an Android smartphone. ECSS-based encryption has been identified as a novel method of securing information in the form of DNA molecules that employ DNA strands to conceal the information. This Paper provides the DNA cryptography approach for encrypting and decrypting plain texts in this work. The primary goal of ECSS cryptography is to guarantee secrecy when people transfer data across a network. This paper examines DNA Crypto, the distinction between classical cryptography and DNA Data encryption. An Android-based app is created to perform the proposed technique and the results show that the algorithms produce a consistent encryption result in which the length of the message before encryption is always the same as the length of the message after decryption. The suggested security solution has the potential to greatly improve the security of mobile communication. This Paper also provides experimental results for our chat application performance, such as received text message correctness, cipher text, average encryption time and average decryption time. According to the findings/results of this study, the speed of average encryption and decryption of the description is roughly 0.011 and 0.037 sec and prevents plaintext and dictionary attacks.

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“…Cybersecurity is data, ranging from network logs to SA and DA results. This relationship between collected data, Artificial Intelligence (AI) and ML technologies results in an important instrument called the dataset, which brings together the collection of evidence needed to represent the background to be studied [ 17 ].…”

Section: Aim and Motivation—the Problem With Cyber Threat Datasetsmentioning

confidence: 99%

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning

Hernandez-Suarez

Sánchez-Pérez

Olivares-Mercado

et al. 2023

Sensors

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In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.

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Cybersecurity for Data Science: Issues, Opportunities, and Challenges

Cited by 17 publications

References 15 publications

ML-Based Detection of Sybil Attack on MANETS

ML-Based Detection of Sybil Attack on MANETS

An Improved SMS Security Technique to Avoid Plaintext and Dictionary Attacks

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning

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