<p>Due to the invention of automated hacking tools, Hacking is not a black art anymore. Even script kiddies can launch attacks in few seconds. Therefore, there is a great emphasize on the Security to protect the resources from camouflage. Intrusion Detection System is also one weapon in the security arsenal. It is the process of monitoring and analyzing information sources in order to detect vicious traffic. With its unique capabilities like monitoring, analyzing, detecting and archiving, IDS assists the organizations to combat against threats, to have a snap-shot of the networks, and to conduct Forensic Analysis. Unfortunately there are myriad products inthe market. Selecting a right product at time is difficult. Due to the wide spread rumors and paranoia, in this work I have presented the state-of-the-art IDS technologies, assessed the products, and evaluated. I have also presented some of the novel challenges that IDS products are suffering. This work will be a great help for pursuing IDS technology and to deploy Intrusion Detection Systems in an organization. It also gives in-depth knowledge of the present IDS challenges.</p>

<p dir="ltr">This research explores adversarial attacks on Graph Convolutional Transformer (GCT) models that utilize Electronic Health Record (EHR) data. As deep learning models become increasingly integral to healthcare, securing their robustness against adversarial threats is critical. This research assesses the susceptibility of GCT models to specific adversarial attacks, namely the Fast Gradient Sign Method (FGSM) and the Jacobian-based Saliency Map Attack (JSMA). It examines their effect on the model’s prediction of mortality and readmission. Through experiments conducted with the MIMIC-III and eICU datasets, the study finds that although the GCT model exhibits superior performance in processing EHR data under normal conditions, its accuracy drops when subjected to adversarial conditions—from an accuracy of 86% with test data to about 57% and an area under the curve (AUC) from 0.86 to 0.51. These findings averaged across both datasets and attack methods, underscore the urgent need for effective adversarial defense mechanisms in AI systems used in healthcare. This thesis contributes to the field by identifying vulnerabilities and suggesting various strategies to enhance the resilience of GCT models against adversarial manipulations.</p>

Due to the invention of automated hacking tools, Hacking is not a black art anymore. Even script kiddies can launch attacks in few seconds. Therefore, there is a great emphasize on the Security to protect the resources from camouflage. Intrusion Detection System is also one weapon in the security arsenal. It is the process of monitoring and analyzing information sources in order to detect vicious traffic. With its unique capabilities like monitoring, analyzing, detecting and archiving, IDS assists the organizations to combat against threats, to have a snap-shot of the networks, and to conduct Forensic Analysis. Unfortunately there are myriad products inthe market. Selecting a right product at time is difficult. Due to the wide spread rumors and paranoia, in this work I have presented the state-of-the-art IDS technologies, assessed the products, and evaluated. I have also presented some of the novel challenges that IDS products are suffering. This work will be a great help for pursuing IDS technology and to deploy Intrusion Detection Systems in an organization. It also gives in-depth knowledge of the present IDS challenges.