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Found 16 papers, 1 papers with code
Making Large Language Models Better Data Creators
In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17. 5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks.
Ranked #3 on
Visual Question Answering
on ViP-Bench
Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review
Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities.
ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator
The so generated data and simulations could be used to bolster the defenses of an IDS against an actual AI-based metamorphic attack from advanced malware and ransomware.
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach
Long-Short-Term-Memory (LSTM) networks have shown great promise in artificial intelligence (AI) based language modeling.
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems
We also developed a system named DRoID that uses the DRo mechanism for enhancing the performance of an existing Malware Detection System that uses (low information features like the) Android implicit Intent(s) as the only features.
Identification of Significant Permissions for Efficient Android Malware Detection
In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network.
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering
Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries.
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware
With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware.
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning
Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15. 22% against a single policy attack, thereby increasing the robustness of the detection models i. e. the proposed model can effectively detect variants (metamorphic) of malware.
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS
In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations.
DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS
We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS.
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture
So far neither the feasibility of developing an Intrusion Detection System solely on implicit Intent has been explored, nor are any benchmarks available of a malware classifier that is based on implicit Intent alone.
Android Malicious Application Classification Using Clustering
However, it appears that detection accuracy can be improved by using the clustering method.
Malware Detection using Machine Learning and Deep Learning
Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection.
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection
Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc.
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware detection.
