Distributed Denial of Service(DDoS) and False Data Injection Attack Detection in Cyber Physical System

PRESENTED BY: SUPERVISED BY:NURJAHAN DR. M. SHAMIM KAISERFARHANA NIZAMSHUDARSHON CHAKI

2Outline

Abstract Related Work Introduction System Model Flowchart of Intrusion Detection Method Attack Detection Using Fuzzy Logic Attack Classifier Simulation Result References

3Abstract

Proposes DDoS and False data injection attack detection in Cyber Physical System.

The Chi square detector and Fuzzy logic based attack classifier (FLAC) were used to identify distributed denial of service and False data injection attacks.

An example scenario has been created using OpNET Simulator.

Proposes intrusion detection algorithm in the underlying cyber network.

4Related Work

In (1), Authors have surveyed the vulnerabilities in smart grid networks, the types of attacks and attackers, the current and needed solutions.

Limitation-Do not perform any types of simulation or design any security frameworks.

In (2), Detecting false data injection attacks by Euclidean detector with Kalman filter and also detects DDoS attacks, short term and long term random attacks by Chi-square detector with Kalman filter.

Limitation- Focusing that Chi-Square detector is unable to detect the statistically derived false data-injection attack.

5Continue

In (3), Highlighting security requirements and issues of smart grid and describing smart grid anomalies and protecting smart grid from cyber vulnerabilities.

Limitation-No smart grid cyber attack risk assessment and mitigation discussion and implementation of intrusion detection algorithms throughout system.

In (4), Focus on both random and targeted false data injection attack. Limitation-Protection of the confidentiality of sensor measurements against false data injection is not revealed.

6Introduction

Physical objects are connected with each other through cyber networks are collectively called cyber physical system.

Smart grid is an example of such a system where grid is automated, controlled and has access via internet.

But this system is much more vulnerable to various cyber-attacks, there is more scope of damaging physical infrastructures and making the power station unstable.

7System Model

8Cyber Attack Scenario In the Network Infrastructure

9Flowchart of Intrusion Detection In the Network Infrastructure

10Attack detection Based on Chi-Square Test With Fuzzy Logic Attack Classifier

BY LMS filter, we get decision boundary shifting.

11Continue….

Then through statistical measurement of sensitivity and specificity, we derived the confusion matrix [5], True Positive = Correctly identified False Positive = Incorrectly identified True Negative = Correctly rejected False negative = Incorrectly rejected

In general, positive = identified Negative = rejected. Therefore,

Confusion Matrix

DDoS False Data Injection

DDoS 96% 4%False Data Injection

4% 96%

12Continue….

Data miner along with Kuok’s algorithm is used for optimizing association rule algorithm.[6]

13Comparison of accuracy between Proposed and Existing Methodology

Accuracy Rate90%

92%

94%

96%

Accuracy Rate for Proposed Attack Detection Technique

FL and Data Mining Proposed

FL and data mining

92%

Proposed 94.2%

14References

[1]F. Aloul, A. R. Al-Ali, R. Al-Dalky, M. Al-Mardini, and W. El-Hajj, “Smart grid security: Threats, vulnerabilities and solutions,” International Journal Of Smart Grid And Clean Energy, pp. 1–6, 2012.[2]K. Manandhar, X. Cao, F. Hu, and Y. Liu, “Detection of faults and attacks including false data injection attack in smart grid using kalman filter,” IEEE Transactions On Control Of Network Systems, vol. 1, no. 4, pp. 370–379, 2014. [3]K. Sgouras, A. Birda, and D. Labridis, “Cyber attack impact on critical smart grid infrastructures,” in Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES, pp. 1–5, Feb 2014.

15Continue

[4]R. B. Bobba, K. M. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T. J. Overbye, “Detecting false data injection attacks on dc state estimation,” Preprints Of the First Workshop On Secure Control Systems, CPSWEEK, vol. 2010, 2010.[5]Wikipedia, "Sensitivity and specificity", 2015. [Online]. Available: https://en.wikipedia.org/wiki/Sensitivity_and_specificity. [Accessed: 31- DEC- 2015][6]C. M. Kuok, A. Fu, and M. H. Wong, “Mining fuzzy association rules in databases,” ACM SIGMOD Record, vol. 27, no. 1, pp. 41–46, 1998.

16

THANK YOU