1. Detection Detect the breach and protect the data. By,
Kaushik Thallapally.
STUDENT
Computer Science Department.
Concordia University Ann arbor.
Professor DR. William Mcmillan.

2. What is Detection?

3. How to Detect the hack?

4. Old generation Detection Techniques:
Intrusion Detection System
An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator.
Security Information and Event management.
security information and event management (SIEM) software products and services combine security information management (SIM) and security event management(SEM). They provide real-time analysis of security alerts generated by applications and network hardware.

5. Why IDS and SIEM Not so Effective?

6. Data Breaches: What is Data Breach?
A data breach is an incident in which sensitive, protected or confidential data has potentially been viewed, stolen or used by an individual unauthorized to do so.
Data breaches may involve personal health information(PHI), personally identifiable information (PII), trade secrets or intellectual property.
The most common concept of a data breach is an attacker hacking into a corporate network to steal sensitive data. 

7. Prominent Data Breaches:
Ameriprise solution stolen laptop(2005).
Department of veteran Affairs(2006).
GE Money and General Electric (2008).
Play station Data Breach(2011).
Citi group Data Breach(2011).
Adobe Systems (2013).
Apple iCloud Data breach(2014).
Equifax Data breach(2017).
Military Documents of USA and South Korea(2017).

8. How to Protect Sensitive data?

9. Breach Detection
Breach detection systems (BDS) are a category of applications and security devices designed to detect the activity of malware inside a network after a breach has occurred.
BDS has 3 different deployment methods:
Out-of band systems scan data mirrored from port scans from a switch or network tap.
In-line systems are deployed between the network and WAN interface just like tier 1 firewalls and intrusion prevention systems.
Endpoint deployments that use a client installed on endpoint machines.

10. What’s Next? Behavioral Focus. Improved threat intelligence.
Better analytics capabilities.
Automated detection and response.
Longer Data Retention and historical Forensics.

11. Deploying Machine Learning and Deep Learning Algorithms in Detection and Prevention:

12. Machine Learning in Data Security:
Machine is a science of designing and applying algorithms that are able to learn things from past cases.
It uses complex algorithms that iterate over large data sets and analyze the patterns in data.
Machine Learning converts data intensive and confusing information into a simple format that suggests actions to decision makers.
A user further trains the ML system by continually adding data and experience.
Thus at its core, machine learning is a 3-part cycle i.e. Train-Test-Predict.
Optimizing the cycle can make predictions more accurate and relevant to the specific use-case.

13. Empowering People About Detection of security attack:

14. Empowering People About Detection of security attack:
Each person in an organization plays a key role in helping to ward off a potential data breach. 
Conversely, IT’s job is to educate and equip employees through cybersecurity awareness training, including internal tests like phishing training, targeted attacks and even social engineering to make sure employees understand the various methods hackers will use to gain entry to the enterprise.
 The beauty of this is that identity governance gives us the power to do it, while keeping our data safely in our hands.

15. Any Questions?

16. References:
“Data Breach”
“Detection Technologies” technologies.com/about.html.
Jonathon Ng, Deepti Joshi, Shankar M. Banik, "Applying Data Mining Techniques to Intrusion Detection",  th International Conference on Information Technology - New Generations (ITNG), vol. 00, no. , pp , 2015, doi: /ITNG
Fu Zhu, Amr Youssef, Walaa Hamouda, "Detection techniques for data-level spoofing in GPS-based phasor measurement units", 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), vol. 00, no. , pp. 1-8, 2016, doi: /MoWNet

17. Thank You.
Kaushik Thallapally.
Concordia University Ann Arbor.