Adversarial Attacks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Adversarial Attacks ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Adversarial Attacks Projects
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GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
This project studies how advanced AI, called GANs, can trick wireless communication systems. It shows that fake signals made by GANs can look almost exactly like real ones. The research tested this on modern multi-carrier signals used in networks. The results show that receivers can be fooled 99.7% of the time, revealing a serious security risk. -
LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features
This project studies how deep learning models, especially convolutional neural networks, can be tricked by tiny changes in input that humans cannot notice. The research introduces a new method called LAFIT to test how strong these models are against such attacks. It shows that using hidden information inside the model can make attacks more effective. The work helps make AI systems safer by better evaluating their weaknesses. -
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
This project focuses on making wireless networks smarter and more secure. It uses artificial intelligence to manage network resources for many users and base stations. The system also studies how a jammer can disrupt the network and how to defend against it. The methods are tested in simulations to show they work well. -
On-Device Smishing Classifier Resistant to Text Evasion Attack
This project focuses on detecting smishing, which are fake text messages meant to steal personal data. The researchers built a smart system that can run directly on mobile phones and tell fake messages apart from real ones. They trained the system using hundreds of thousands of real messages from users in Korea. The system is small, fast, private, and can handle even slightly changed versions of smishing messages. -
Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems
This project studies ways to bypass modern network security systems that use machine learning. The researchers show that even without knowing the system details, an attacker can trick it by slightly changing network traffic. They tested their method on several security systems and achieved a high success rate. The work also suggests ways to defend against such attacks. -
Evasion Attack and Defense on Machine Learning Models in CyberPhysical Systems: A Survey
This project studies how machine learning in cyber-physical systems can be attacked by hackers. It focuses on a type of attack called evasion attacks, where attackers trick the system by changing data. The work reviews current research on both attacks and defenses and organizes them into clear categories. It also points out gaps and future directions to make these systems safer.
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