Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis

Kinoon, Mohammed Al; Omar, Marwan; Mohaisen, Manar; Mohaisen, Aziz

doi:10.1007/978-3-030-91434-9_16

Cited by 14 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Drebin dataset, with its vast collection of An-droid applications, has been extensively utilized to benchmark various detection techniques, offering insights into the intricacies of Android malware (Biggio et al, 2013). Similarly, EMBER, with its standardized representation of PE files, has been a cornerstone in several machine learning-based malware detection studies, serving as a consistent platform for evaluations (Al Kinoon, Omar, Mohaisen & Mohaisen, 2021).…”

Section: E Drebin and Ember In Malware Researchmentioning

confidence: 99%

“…In this paper, we delve deep into this hypothesis, harnessing the power of GPT-2 to detect malware, employing two of the widely used datasets in the domain: EMBER (Al Kinoon, Omar, Mohaisen & Mohaisen, 2021) and Drebin (Biggio et al, 2013). EMBER offers a collection of features from PE (Portable Executable) files, presenting a standardized representation conducive for ML experimentation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

Basharat,

Omar

2024

Land Forces Academy Review

Self Cite

View full text Add to dashboard Cite

In the rapidly advancing digital age, the surge in malware complexity presents a formidable challenge to cybersecurity efforts, rendering traditional signature-based detection methods increasingly obsolete. These methods struggle to keep pace with the swift evolution of malware, particularly with the emergence of polymorphic and metamorphic variants designed to bypass conventional detection. This study introduces a groundbreaking approach to malware detection by utilizing GPT-2, a cutting-edge language model developed by OpenAI, specifically for the purpose of feature extraction. By applying GPT-2’s deep learning capabilities to the EMBER and Drebin datasets, this research explores the model’s effectiveness in identifying malware through the intricate patterns present in binary data. Contrary to its original design for natural language processing, GPT-2’s application in this context demonstrates a significant potential for enhancing malware detection strategies. The model’s proficiency in extracting complex features from binary sequences marks a notable advancement over traditional methods, providing a more adaptive and robust mechanism for identifying malicious software. However, the study also acknowledges the challenges associated with the interpretability of deep learning models and their susceptibility to adversarial attacks, underscoring the imperative for ongoing innovation in the field of cybersecurity. This exploration into the unconventional use of GPT-2 for feature extraction in malware detection not only showcases the model’s versatility beyond language tasks but also sets a new precedent for the application of unsupervised learning models in enhancing cybersecurity defenses.

show abstract

Section: E Drebin and Ember In Malware Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

Basharat,

Omar

2024

Land Forces Academy Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Myriad studies have unveiled methodologies that employ machine learning techniques for this pursuit. Some concentrate on static analysis, extracting salient features from the code to be input into predictive machine-learning frameworks [1,2], while others hinge on dynamic analysis, running the code and monitoring its behavior to discern vulnerabilities [3,4]. A nascent inclination towards harnessing deep learning models for source code vulnerability detection has been observed.…”

Section: Related Workmentioning

confidence: 99%

“…Static analysis of source code offers one such detection avenue, embracing methodologies ranging from code similarity assessment to pattern-recognition techniques. Notably, while code similarity evaluation can pinpoint vulnerabilities emanating from code replication, it may incur considerable false negatives [1,2,9,10,[15][16][17][18][19][20][21][22][23][24]. In a bid to tackle these vulnerability detection quandaries, the academic sphere has introduced methods such as fuzzing, symbolic scrutiny, and rule-centric testing.…”

Section: Introductionmentioning

confidence: 99%

SecuGuard: Leveraging pattern-exploiting training in language models for advanced software vulnerability detection

Basharat,

Omar

2024

International Journal of Mathematics and Computer in Engineering

Self Cite

View full text Add to dashboard Cite

Identifying vulnerabilities within source code remains paramount in assuring software quality and security. This study introduces a refined semi-supervised learning methodology that capitalizes on pattern-exploiting training coupled with cloze-style interrogation techniques. The research strategy employed involves the training of a linguistic model on the SARD and Devign datasets, which are replete with vulnerable code fragments. The training procedure entails obscuring specific segments of the code and subsequently prompting the model to ascertain the obfuscated tokens. Empirical analyses underscore the efficacy of our method in pinpointing vulnerabilities in source code, benefiting substantially from patterns discerned within the code fragments. This investigation underscores the potential of integrating pattern-exploiting training and cloze-based queries to enhance the precision of vulnerability detection within source code.

show abstract

“…First, in the process of system development, it is necessary to avoid security issues such as patient medical information data leakage. Due to the enormous value of data in the medical field, it is frequently the target of theft by criminal groups who profit from the malicious use of data (48). Medical data leakage will aggravate patient distrust of medical institutions, which will lead to major crises in medical institutions.…”

Section: Machine Learning Technology E Ectively Promotes the Developm...mentioning

confidence: 99%

Prediction of patient choice tendency in medical decision-making based on machine learning algorithm

Lyu

Xu²,

Yang³

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

ObjectiveMachine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions.MethodPatient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.ResultsThe accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94.ConclusionAmong the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.

show abstract

Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis

Cited by 14 publications

References 16 publications

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

Harnessing GPT-2 for Feature Extraction in Malware Detection: A Novel Approach to Cybersecurity

SecuGuard: Leveraging pattern-exploiting training in language models for advanced software vulnerability detection

Prediction of patient choice tendency in medical decision-making based on machine learning algorithm

Contact Info

Product

Resources

About