Aritran Piplai
YOU?
Author Swipe
View article: Impugan: Learning Conditional Generative Models for Robust Data Imputation
Impugan: Learning Conditional Generative Models for Robust Data Imputation Open
Incomplete data are common in real-world applications. Sensors fail, records are inconsistent, and datasets collected from different sources often differ in scale, sampling rate, and quality. These differences create missing values that ma…
View article: Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability Open
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intellige…
View article: Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Open
The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is …
View article: An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey
An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey Open
Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with mali…
View article: An in-depth Investigation into the Performance of State-of-the-Art zero-shot, single-shot, and few-shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection
An in-depth Investigation into the Performance of State-of-the-Art zero-shot, single-shot, and few-shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection Open
N-shot learning has emerge in recent year as potential learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal lear…
View article: Towards an In-depth Evaluation of the Performance, Suitability and Plausibility of Few-Shot Meta Transfer Learning on An Unknown Out-of-Distribution Cyber-attack Detection
Towards an In-depth Evaluation of the Performance, Suitability and Plausibility of Few-Shot Meta Transfer Learning on An Unknown Out-of-Distribution Cyber-attack Detection Open
The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta…
View article: An In-Depth Investigation into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection
An In-Depth Investigation into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection Open
N-shot learning has emerge in recent year as potential learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal lear…
View article: PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification
PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification Open
Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as dat…
View article: An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey Open
To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around inf…
View article: Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework
Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework Open
The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the i…
View article: Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework
Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework Open
The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the i…
View article: An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey Open
In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for comparative study to i…
View article: LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge Open
Security Operations Center (SoC) analysts gather threat reports from openly accessible global threat repositories and tailor the information to their organization's needs, such as developing threat intelligence and security policies. They …
View article: Use of Graph Neural Networks in Aiding Defensive Cyber Operations
Use of Graph Neural Networks in Aiding Defensive Cyber Operations Open
In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks …
View article: Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy
Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy Open
Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic knowledge contained in knowledge graphs (KGs) to enhance exp…
View article: Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy Open
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainabili…
View article: Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and Defenses
Knowledge Guided Two-player Reinforcement Learning for Cyber Attacks and Defenses Open
Cyber defense exercises are an important avenue to understand the technical capacity of organizations when faced with cyber-threats. Information derived from these exercises often leads to finding unseen methods to exploit vulnerabilities …
View article: PriveTAB
PriveTAB Open
Machine Learning has increased our ability to model large quantities of data efficiently in a short time. Machine learning approaches in many application domains require collecting large volumes of data from distributed sources and combini…
View article: Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs
Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs Open
Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat I…
View article: Cybersecurity Knowledge Graph Improvement with Graph Neural Networks
Cybersecurity Knowledge Graph Improvement with Graph Neural Networks Open
Cybersecurity Knowledge Graphs (CKGs) help in aggregating information about cyber-events. CKGs combined with reasoning and querying systems such as SPARQL enable security researchers to look up information about past cyberevents that is he…
View article: CyBERT: Contextualized Embeddings for the Cybersecurity Domain
CyBERT: Contextualized Embeddings for the Cybersecurity Domain Open
We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-…
View article: A Policy-Driven Approach to Secure Extraction of COVID-19 Data From Research Papers
A Policy-Driven Approach to Secure Extraction of COVID-19 Data From Research Papers Open
The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time…
View article: Generating Fake Cyber Threat Intelligence Using Transformer-Based Models
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models Open
Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread th…
View article: A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity
A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity Open
Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber entities or key expressions can be used to model a cyber-att…
View article: Knowledge Enrichment by Fusing Representations for Malware Threat Intelligence and Behavior
Knowledge Enrichment by Fusing Representations for Malware Threat Intelligence and Behavior Open
Security engineers and researchers use their disparate knowledge and discretion to identify malware present in a system. Sometimes, they may also use previously extracted knowledge and available Cyber Threat Intelligence (CTI), about known…
View article: Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 Open
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation duri…
View article: Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 Open
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation duri…
View article: NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion Open
With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attac…
View article: A Smart-Farming Ontology for Attribute Based Access Control
A Smart-Farming Ontology for Attribute Based Access Control Open
With the advent of smart farming, individual farmers have started adopting the concepts of agriculture 4.0. Modern smart farms leverage technologies like big data, Cyber Physical Systems (CPS), Artificial Intelligence (AI), blockchain, etc…
View article: NAttack! Adversarial Attacks to bypass a GAN based classifier trained to\n detect Network intrusion
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to\n detect Network intrusion Open
With the recent developments in artificial intelligence and machine learning,\nanomalies in network traffic can be detected using machine learning approaches.\nBefore the rise of machine learning, network anomalies which could imply an\nat…