• Mr. Lagineni Sai Kiran

Malicious cyber-attacks can lurk in enormous amounts of legitimate data in unbalanced network traffic. In cyberspace, it uses a high level of stealth and obfuscation, making it difficult for Network Intrusion Detection Systems (NIDS) to ensure detection accuracy and completeness. Computer vision and deep learning are investigated in this paper for malware detection in unbalanced network data. To address the problem of class imbalance, it presents a novel Difficult Set Sampling Technique (DSSTE) algorithm. To begin, partition the imbalanced training set into the challenging and easy sets using the Edited Nearest Neighbor (ENN) algorithm. Then, to minimize the majority, apply the KMeans technique to compress the majority samples in the challenging set. In the challenging set, zoom in and out the continuous properties of the minority samples, then synthesis fresh samples to enhance the minority number. Then, the enhancement data are mixed with the simple set, the reduced set of majorities in the challenging, and the minority in the tough set to create a new training dataset. The method evens out the initial feature set's imbalance and generates tailored data supplementation for the minority group that wants to understand. It allows the classification algorithm to better learn the distinctions in the training process and increase classification accuracy. We test the suggested strategy on the classic infiltration dataset NSL-KDD as well as the more recent comprehensive intrusion sample CSE-CIC-IDS2018. We employ traditional categorization methods such as random forest (RF) and support vector machine (SVM) (SVM), Mini-VGGNet, XGBoost, MLP AlexNet We evaluate our suggested DSSTE algorithms to some other 24 techniques; the test data showed that the proposed method approach outperforms the others.

Detecting Cyber Attack in Network Dataset using Machine Learning

"Detecting Cyber Attack in Network Dataset using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h179-h184, May-2022, Available :http://www.jetir.org/papers/JETIR2205823.pdf

"Detecting Cyber Attack in Network Dataset using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph179-h184, May-2022, Available at : http://www.jetir.org/papers/JETIR2205823.pdf