| 1 |
| Title |
|
Machine Learning and It's Applications in Biomedical Field |
| Speaker |
|
Muhammet Melih Güler |
| Advisors |
|
Onur Kurt |
| Date |
|
May 3, 2023 |
| Time |
|
13:30 (GMT +3) |
| Abstract |
|
Aim is basically separation of BCC & Normal human cells by preprocessing data and using different machine learning techniques. Dataset is from real human cell samples which is processed by Raman spectroscopy. |
| • |
| 2 |
| Title |
|
Machine Learning in Gene-Networks Using High-Dimensional Modelling |
| Speaker |
|
Furkan Aydın |
| Advisors |
|
Süha Tuna |
| Date |
|
May 10, 2023 |
| Time |
|
13:30 (GMT +3) |
| Meeting Type |
|
Zoom
You may contact the relevant person. |
| Abstract |
|
The paper will introduce a model for separating healthy and diseased genes. To achieve that, features should be extracted in gene data for being used in machine learning models. After feature extraction, the genes will become suitable for machine learning models. Finally, a machine learning model will be trained. |
| • |
| 3 |
| Title |
|
Risk-based real time attack detection using mitre att&ck framework |
| Speaker |
|
Samet Baysal |
| Advisors |
|
Behçet Uğur Töreyin |
| Date |
|
May 10, 2023 |
| Time |
|
14:00 (GMT +3) |
| Meeting Type |
|
Zoom
You may contact the relevant person. |
| Abstract |
|
In recent times, attackers are continuously developing advanced techniques for evading security, stealing personal financial data, Intellectual Property (IP) and sensitive information. These attacks often employ multiple attack vectors for gaining initial access to the systems. Analysts are often challenged to identify malware objective, initial attack vectors, attack propagation, evading techniques, protective mechanisms and unseen techniques. Most of these attacks are frequently referred to as Multi stage attacks and pose a grave threat to organizations, individuals and the government. Early multistage attack detection is a crucial measure to counter malware and deactivate it. Most traditional security solutions use signature-based detection, which frequently fails to thwart zero-day attacks. Manual analysis of these samples requires enormous effort for effectively counter exponential growth of malware samples. In this paper, we present a risk-based approach leveraging MITRE Adversary Tactic Technique and Common knowledge (ATT&CK) framework for early multistage attack detection in real time. |