A Framework for Real-Time Worm Attack Detection and Backbone Monitoring

2006 ◽  
Author(s):  
T. Dubendorfer ◽  
A. Wagner ◽  
B. Plattner
Keyword(s):  
Real Time ◽  
Attack Detection

Real Time early Multi Stage Attack Detection

2021 ◽  
Author(s):  
Yuvraj Sanjayrao Takey ◽  
Sai Gopal Tatikayala ◽  
Satyanadha Sarma Samavedam ◽  
P R Lakshmi Eswari ◽  
Mahesh Uttam Patil
Keyword(s):  
Real Time ◽  
Attack Detection ◽  
Multi Stage

Approach to real time attack detection on the base of simulation and graph oriented modeling

2021 ◽  
Vol 7 ◽  
Author(s):  
V.A. Desnitsky ◽  
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Attack Detection ◽  
Suitable Model ◽  
Distinctive Features ◽  
Detection Process ◽  
Detection Model ◽  
Security Incidents

The article presents an approach to detecting attacks in real time based on simulation and graph-oriented mod- eling. The detection process is performed in a mode close to real-time with the ability to promptly detect known types of security incidents. The distinctive features of the approach include the multidimensional nature of attack detection with the ability to select a specific type of simulation and graph-oriented attack detection model with their subsequent combination. In addition, within the practical part of the work, a software tool has been developed to select the most suitable model apparatus for detecting attacks of each type.

CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS

2010 ◽  
Vol 54 (7) ◽  
pp. 1126-1141 ◽  
Author(s):  
John Felix Charles Joseph ◽  
Amitabha Das ◽  
Bu-Sung Lee ◽  
Boon-Chong Seet
Keyword(s):  
Real Time ◽  
Detection System ◽  
Attack Detection ◽  
Cross Layer ◽  
Routing Attack

scholarly journals Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Francesco Musumeci ◽  
Ali Can Fidanci ◽  
Francesco Paolucci ◽  
Filippo Cugini ◽  
Massimo Tornatore
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Transmission Control Protocol ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Worst Case ◽  
Latency Reduction ◽  
Data Plane ◽  
Network Switches

Abstract Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of $$\upmu \text {s}$$ μ s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language. Graphic Abstract

scholarly journals RT-SAD: Real-Time Sketch-Based Adaptive DDoS Detection for ISP Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haibin Shi ◽  
Guang Cheng ◽  
Ying Hu ◽  
Fuzhou Wang ◽  
Haoxuan Ding
Keyword(s):  
Real Time ◽  
High Speed ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Network Environment ◽  
The Real ◽  
Feature Extraction Method ◽  
Ddos Attack ◽  
High Speed Network ◽  
Ddos Attack Detection

With the great changes in network scale and network topology, the difficulty of DDoS attack detection increases significantly. Most of the methods proposed in the past rarely considered the real-time, adaptive ability, and other practical issues in the real-world network attack detection environment. In this paper, we proposed a real-time adaptive DDoS attack detection method RT-SAD, based on the response to the external network when attacked. We designed a feature extraction method based on sketch and an adaptive updating algorithm, which makes the method suitable for the high-speed network environment. Experiment results show that our method can detect DDoS attacks using sampled Netflowunder high-speed network environment, with good real-time performance, low resource consumption, and high detection accuracy.

scholarly journals Design a Monitoring Device for Heart-Attack Early Detection Based on Respiration Rate and Body Temperature Parameters

2021 ◽  
Vol 3 (3) ◽  
pp. 114-120
Author(s):  
Isna Fatimatuz Zahra ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha ◽  
Hayder J Hassaballah
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Respiration Rate ◽  
Heart Attack ◽  
Detection System ◽  
Warning System ◽  
Piezoelectric Sensor ◽  
Attack Detection ◽  
Sensor Data ◽  
Frequency Module

Acute myocardial infarction, commonly referred to as a heart attack, is the most common cause of sudden death where a monitoring tool is needed that is equipped with a system that can notify doctors to take immediate action. The purpose of this study was to design a heart attack detection device through indicators of vital human signs. The contribution of this research is that the system works in real-time, has more parameters, uses wireless, and is equipped with a system to detect indications of a heart attack. In order for wireless monitoring to be carried out in real-time and supported by a detection system, this design uses a radio frequency module as data transmission and uses a warning system that is used for detection. Respiration rate was measured using the piezoelectric sensor, and body temperature was measured using the DS18B20 temperature sensor. Processing of sensor data is done with ESP32, which is displayed wirelessly by the HC-12 module on the PC. If an indication of a heart attack is detected in the parameter value, the tool will activate a notification on the PC. In every indication of a heart attack, it was found that this design can provide notification properly. The results showed that the largest respiratory error value was 4%, and the largest body temperature error value was 0.55%. The results of this study can be implemented in patients who have been diagnosed with heart attack disease so that it can facilitate monitoring the patient's condition.

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