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Found 20 papers, 0 papers with code
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.
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.
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.
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.
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.
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.
A Novel Spatial-Spectral Framework for the Classification of Hyperspectral Satellite Imagery
Therefore, in this paper, we present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification.
An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU
Due to continuous increase in the number of malware (according to AV-Test institute total ~8 x 10^8 malware are already known, and every day they register ~2. 5 x 10^4 malware) and files in the computational devices, it is very important to design a system which not only effectively but can also efficiently detect the new or previously unseen malware to prevent/minimize the damages.
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.
Group-wise classification approach to improve Android malicious apps detection accuracy
Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the malware.
Detection of Advanced Malware by Machine Learning Techniques
In today's digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz.
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware 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.
Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
Semi supervised learning methods have gained importance in today's world because of large expenses and time involved in labeling the unlabeled data by human experts.
