Mamoona Humayun

Bio: Mamoona Humayun is an academic researcher. The author has co-authored 1 publications.

Cybersecurity for Data Science: Issues, Opportunities, and Challenges

Abstract: Cybersecurity (CS) is one of the critical concerns in today’s fast-paced and interconnected world. Advancement in IoT and other computing technologies had made human life and business easy on one hand, while many security breaches are reported daily. These security breaches cost millions of dollars loss for individuals as well as organizations. Various datasets for cybersecurity are available on the Internet. There is a need to benefit from these datasets by extracting useful information from them to improve cybersecurity. The combination of data science (DS) and machine learning (ML) techniques can improve cybersecurity as machine learning techniques help extract useful information from raw data. In this paper, we have combined DS and ML for improving cybersecurity. We will use the CS dataset, and ML techniques will be applied to these datasets to identify the issues, opportunities, and cybersecurity challenges. As a contribution to research, we have provided a framework that will provide insight into ML and DS’s use for protecting cyberspace from CS attacks.

Novel Detection of Accurate Spam Content using Logistic Regression Algorithm Compared with Gaussian Algorithm

Abstract: Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.

Novel Detection of Accurate Spam Content using Logistic Regression Algorithm Compared with Gaussian Algorithm

Abstract: Aim: To detect the spam content over the internet and social media using Logistic Regression algorithm over Gaussian algorithm. Methods and Materials: Detection of spam content messages are performed using Logistic Regression algorithm and Gaussian algorithm (sample size=20) Where values are taken randomly. G-power was maintained to be 80%. Results and Discussion: This article is an attempt to improve the accuracy of spam content detection using the Logistic Regression algorithm, a machine learning algorithm. The AI based Application avoids overfitting. The proposed model has improved accuracy of 95% with p value which is less than 0.03(p<0.05) in spam detection than Gaussian algorithm having accuracy of 93%. Conclusion: The outcomes of the proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed model Logistic regression algorithm was compared with the Gaussian algorithm. The proposed algorithm seems to have higher accuracy than the Gaussian algorithm.

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

Abstract: 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.

A Comparative Analysis Of Data Integrity In Healthcare

Abstract: For any professional, maintaining data integrity is a demanding task. The purpose of this study is to develop a list of research efforts in the field of healthcare data integrity. Through assault statistics, the purpose of data safety issues in healthcare is highlighted in this study. Data integrity breaches are perhaps a greater serious threat to the healthcare business than data theft, as they might allow attackers to change anything in the record. By their very nature, Breach of healthcare data integrity is harder to identify, and in many situations, the true consequences of such hacks have yet to be determined. This survey examines a few of the key benefits of maintaining information integrity, and the risks that are associated with the technology industry these days, as well as the healthcare industry in particular, if it is not strictly followed.