Adversarial Attack Research Topics

An adversarial Attack research topic is one of the significant topics that remove misidentifications in the adversarial attack. In this we propose a Neural Network based Adversarial attacks and it tackles several existing technology issues. Here we provide some Adversarial Attack related explanations as below:

  1. Define Adversarial Attack in Neural Network

At the initial stage first we take a look on the definition of Adversarial Attack, Adversarial attack is the hostile attack on the data that is seem okay to the human point of view but that generates misidentification in the machine learning channel. These attacks are frequently prepared in the form of especially planned “noise” that removes misidentification.

  1. What is Adversarial Attack in Neural Network?

Next to the definition of Adversarial Attack we see the deep explanation of Adversarial Attack; it is a method that is utilized to handle machine learning technique’s outcome, especially neural network by presenting cautiously designed confusions to the input data. The aim of the adversarial attack is to generate the model to make wrong or unpredicted outputs whereas maintaining the modifications to the input data undetectable to human spectators.

Types of Adversarial Attack in Neural Network

There are three types of adversarial attacks in neural networks.  They are

  • Grey box attack
  • White box attack
  • Black box attack

Grey box attack:

Gray box testing divides the variance by offering the assessor with limited skills of the system inward. For illustration, gray box testers not yet have broad knowledge of an application’s source code but have limited knowledge of it and/or obtain plan documentation.

White box attack:

In this attack the attacker has the whole access to the system, say ‘F’ or the model’s whole details, like the model parameters, training dataset, gradient of the model and hyperparameters.

Black box attack:

In this the attacker has no details about the framework and parameters of the aimed model, and the only ability of the attacker is to input the selected data to the aimed model and analyze the outcomes characterized by the aimed model.

  1. Where Adversarial Attack in Neural Network used?

After the detailed explanation of Adversarial Attack in neural network, we will notice where it will be utilized. It is employed to test the strength of the machine learning models in different applications namely model evaluation, cybersecurity and security-sensitive fields like autonomous vehicles.

  1. Why Adversarial Attack in Neural Network technology proposed? , previous technology issues

Adversarial Attack in Neural Network is proposed in this research and we overcome some existing technology issues to propose this novel one. In this the Adversarial Attack detection is proposed to overcome the weakness machine learning of models, especially neural networks to adversarial attacks. The weakness of the model utilizes these attacks and generates them to create wrong forecasting or classifications that are the important concern in safety related applications. The proposed method enhancing model reliability, Economic benefits, facilitating false positives/negative, preventing security breaches, maintaining data integrity and facilitating trust and adoption. Some of the existing technology issues are Safety-critical application, model vulnerabilities, lack of robustness. Transferability and model opacity,

  1. Algorithms / protocols

The Adversarial Attack in Neural Network technology is proposed in this work and it overcomes some difficulties in previous technology. We utilized various algorithms or protocols for Adversarial Attacks namely Fast gradient sign method (FGSM), Label Changing Rate (LCR), Artificial Bee Colony (ABC), Projection Gradient Descent (PGD) and Paradigm with Swarm Optimization.

  1. Comparative study / Analysis

Here we compare various methods for Adversarial Attack to obtain the possible method. The existing work training and estimation of sample in the group will decrease the effect of modifications. Several countermeasures are replaceable, significance that countermeasures planned for one model and it will be effective in other model. The detection model is constructed on known attacks and these attacks cannot be detected in a correct manner. Depending on the negative depiction will result in false positives (usual samples are not considered as negative samples) and false negatives (unsuitable negative samples). It is complex to beat the proper balance among sensitivity and specificity.

  1. Simulation results / Parameters

The adversarial attack in neural network is compared with various parameters to obtain a correctly predicted outcome. The parameters that we compared are Accuracy, F1 score, Execution time and precision.

  1. Dataset LINKS / Important URL

For Adversarial Attack in Neural Network, we provide several links to go across it that will aids in configuring adversarial attack related explanations, methods, or any related information:

  1. Adversarial Attack in Neural Network Applications

Adversarial Attack in Neural network is now utilized in many applications some of the applications are IoT security, Defense and military, Autonomous vehicles, malware detection, healthcare, Cybersecurity, Image and video analysis and Natural Language Processing.

  1. Topology for Adversarial Attack in Neural network

Hyperparameter tuning, preprocessing, Adversarial attack detection and training process are some of the topology that we utilized for Adversarial Attack in Neural network.

  1. Environment in Adversarial Attack in Neural network

The environment in Adversarial Attack in Neural network contains the formation of essential libraries, apparatus and resources to improve, train and estimate detection models.

  1. Simulation tools

Here we utilize some software requirements for Adversarial Attacks in neural networks. The tool that we used for development is NS 3.26 and the adversarial attacks can be implemented by employing the languages like C++, Python. And this will be operated by the operating system namely Ubuntu 16.06 [LTS].

  1. Results

Adversarial attack is a technique that is utilized to handle the output of machine learning model. In this we propose a neural network based adversarial attack to overcome the previous issue. Here we implement this by using the language C++, Python and it can be developed by the tool Ubuntu 16.06[LTS].

Adversarial Attack Research Ideas:

Below we provided are some of the research topics on the basis of Adversarial Attack. These topics will be useful when we have any doubts or clarifications related to the Adversarial Attacks:

  1. Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
  2. Simulation of Physical Adversarial Attacks on Vehicle Detection Models
  3. A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
  4. A GAN-based Adversarial Attack Method for Data-driven State Estimation
  5. Black-box Speech Adversarial Attack with Genetic Algorithm and Generic Attack Ideas
  6. Adversarial Attack Mitigation Strategy for Machine Learning-Based Network Attack Detection Model in Power System
  7. Defense Method against Adversarial Attacks Using JPEG Compression and One-Pixel Attack for Improved Dataset Security
  8. Research of Black-Box Adversarial Attack Detection Based on GAN
  9. Data-Driven Defenses Against Adversarial Attacks for Autonomous Vehicles
  10. Adversarial Attack of ML-based Intrusion Detection System on In-vehicle System using GAN
  11. Benchmarking Adversarial Attacks and Defenses in Remote Sensing Images
  12. Type-I Generative Adversarial Attack
  13. Systematic Literature Review: Evaluating Effects of Adversarial Attacks and Attack Generation Methods
  14. A Multimodal Adversarial Database: Towards A Comprehensive Assessment of Adversarial Attacks and Defenses on Medical Images
  15. A Black-Box Adversarial Attack Method via Nesterov Accelerated Gradient and Rewiring Towards Attacking Graph Neural Networks
  16. An Ensemble Learning to Detect Decision-Based Adversarial Attacks in Industrial Control Systems
  17. Security Concerns of Adversarial Attack for LSTM/BiLSTM Based Solar Power Forecasting
  18. Point Cloud Adversarial Perturbation Generation for Adversarial Attacks
  19. Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
  20. From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories
  21. Investigating adversarial attacks against Random Forest-based network attack detection systems
  22. Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
  23. Exploiting the Divergence Between Output of ML Models to Detect Adversarial Attacks in Streaming IoT Applications
  24. Adversarial Attacks and Defense on an Aircraft Classification Model Using a Generative Adversarial Network
  25. On the Defense of Spoofing Countermeasures Against Adversarial Attacks
  26. Contextual Adversarial Attack Against Aerial Detection in The Physical World
  27. Average Gradient-Based Adversarial Attack
  28. A Multi-Strategy Adversarial Attack Method for Deep Learning Based Malware Detectors
  29. A Wasserstein GAN-based Framework for Adversarial Attacks Against Intrusion Detection Systems
  30. Investigation of the Security of ML-models in IoT Networks from Adversarial Attacks
  31. Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition
  32. The Impact of Adversarial Attacks on Interpretable Semantic Segmentation in Cyber–Physical Systems
  33. Experimental Evaluation of Adversarial Attacks Against Natural Language Machine Learning Models
  34. Feature Fusion Based Adversarial Example Detection Against Second-Round Adversarial Attacks
  35. Discrete Point-Wise Attack is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition
  36. A Machine Learning-Based Survey Of Adversarial Attacks And Defenses In Malware Classification
  37. Adversarial Attacks & Detection on a Deep Learning-Based Digital Pathology Model
  38. Toward Robust Neural Image Compression: Adversarial Attack and Model Finetuning
  39. Stochastic Computing as a Defence Against Adversarial Attacks
  40. Local Texture Complexity Guided Adversarial Attack
  41. A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security
  42. Quantum Annealing-Based Machine Learning for Battery Health Monitoring Robust to Adversarial Attacks
  43. AdvRevGAN: On Reversible Universal Adversarial Attacks for Privacy Protection Applications
  44. Intra-Class Universal Adversarial Attacks on Deep Learning-Based Modulation Classifiers
  45. Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling
  46. Adversarial Attack and Defense on Graph Data: A Survey
  47. Universal Object-Level Adversarial Attack in Hyperspectral Image Classification
  48. Physical Black-Box Adversarial Attacks Through Transformations
  49. Detection of Physical Adversarial Attacks on Traffic Signs for Autonomous Vehicles
  50. Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey