Naveed Ahmad

Pir Sami Ullah Shah, Naveed Ahmad, Mirza Omer Beg

Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learning model and deploy the model to the production as well as store different versions of model and dataset. Benefits of MLOps is to make sure the fast delivery of the new trained models to the production to have accurate results. Furthermore, MLOps practice impacts the overall quality of the software products and is completely dependent on open-source tools and selection of relevant open-source tools is considered as challenged while a generalized method to select an appropriate open-source tools is desirable. In this paper, we present a framework for recommendation system that processes the contextual information (e.g., nature of data, type of the data) of the machine learning project and recommends a relevant toolchain (tech-stack) for the operationalization of machine learning systems. To check the applicability of the proposed framework, four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated where precision, recall and f-score is measured, the random forest out classed other approaches with highest f-score value of 0.66.

Muhammed Fatih Bulut, Yingqi Liu, Naveed Ahmad et al.

Large and Small Language Models (LMs) are typically pretrained using extensive volumes of text, which are sourced from publicly accessible platforms such as Wikipedia, Book Corpus, or through web scraping. These models, due to their exposure to a wide range of language data, exhibit impressive generalization capabilities and can perform a multitude of tasks simultaneously. However, they often fall short when it comes to domain-specific tasks due to their broad training data. This paper introduces SecEncoder, a specialized small language model that is pretrained using security logs. SecEncoder is designed to address the domain-specific limitations of general LMs by focusing on the unique language and patterns found in security logs. Experimental results indicate that SecEncoder outperforms other LMs, such as BERTlarge, DeBERTa-v3-large and OpenAI's Embedding (textembedding-ada-002) models, which are pretrained mainly on natural language, across various tasks. Furthermore, although SecEncoder is primarily pretrained on log data, it outperforms models pretrained on natural language for a range of tasks beyond log analysis, such as incident prioritization and threat intelligence document retrieval. This suggests that domain specific pretraining with logs can significantly enhance the performance of LMs in security. These findings pave the way for future research into security-specific LMs and their potential applications.

Muhammed Fatih Bulut, Acar Tamersoy, Naveed Ahmad et al.

This paper introduces TIPS: Threat Actor Informed Prioritization using SecEncoder, a specialized language model for security. TIPS combines the strengths of both encoder and decoder language models to detect and prioritize compromised applications. By integrating threat actor intelligence, TIPS enhances the accuracy and relevance of its detections. Extensive experiments with a real-world benchmark dataset of applications demonstrate TIPS's high efficacy, achieving an F-1 score of 0.90 in identifying malicious applications. Additionally, in real-world scenarios, TIPS significantly reduces the backlog of investigations for security analysts by 87%, thereby streamlining the threat response process and improving overall security posture.

Roquia Mushtaq, Naveed Ahmad, Aimal Rextin et al.

The high number of products available makes it difficult for a user to find the most suitable products according to their needs. This problem is especially exacerbated when the user is trying to optimize multiple attributes during product selection, e.g. memory size and camera resolution requirements in case of smartphones. Previous studies have shown that such users search extensively to find a product that best meets their needs. In this paper, we propose an interface that will help users in selecting a multi-attribute product through a series of visualizations. This interface is especially targeted for users that desire to purchase the best possible product according to some criteria. The interface works by allowing the user to progressively shortlist products and ultimately select the most appropriate product from a very small consideration set. We evaluated our proposed interface by conducting a controlled experiment that empirically measures the efficiency, effectiveness and satisfaction of our visualization based interface and a typical e-commerce interface. The results showed that our proposed interface allowed the user to find a desired product quickly and correctly, moreover, the subjective opinion of the users also favored our proposed interface.