SQL Injection Attack Detection using Machine Learning Algorithm

2021 ◽  
Author(s):  
A. Sivasangari ◽  
J. Jyotsna ◽  
K. Pravalika
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Sql Injection ◽  
Sql Injection Attack

scholarly journals Comparative Study on TCP SYN Flood DDoS Attack Detection: A Machine Learning Algorithm Based Approach

2021 ◽  
Vol 16 ◽  
pp. 584-591
Author(s):  
S. Sumathi ◽  
R. Rajesh
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Research Goal ◽  
Ddos Attack Detection ◽  
F Measure

A most common attack on the internet network is a Distributed Denial of Service (DDoS) attack, which involves occupying computational resources and bandwidth to suppress services to potential clients. The attack scenario is to massively flood the packets. The attack is called a denial of service (DoS) if the attack originates from a single server, and a distributed denial of service (DDoS) if the attack originates from multiple servers. Control and mitigation of DDoS attacks have been a research goal for many scholars for over a decade, and they have achieved in delivering a few major DDoS detection and protection techniques. In the current state of internet use, how quickly and early a DDoS attack can be detected in broadcasting network transactions remains a key research goal. After the development of a machine learning algorithm, many potential methods of DDoS attack detection have been developed. The work presents the results of various experiments carried out using data mining and machine learning algorithms as well as a combination of these algorithms on the commonly available dataset named CAIDA for TCP SYN flood attack detection. Also, this work analysis the various performance metrics such as false positive rate, precision, recall, F-measure and receiver operating characteristic (ROC) using various machine learning algorithm. One-R(OR) with an ideal FPR value of 0.05 and recall value of 0.95,decision stump(DS) with an ideal precision value of o.93,PART with an excellent F-measure value of 0.91 are some of the performance metric values while performing TCP SYN flood attack detection.

scholarly journals An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 53-57
Author(s):  
Yazeed Abdulmalik
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Semantic Features ◽  
Sql Injection ◽  
Detection Model ◽  
Learning Techniques ◽  
Sql Injection Attack

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.

scholarly journals A Hybrid Adaptive Development Algorithm and Machine Learning Based Method for Intrusion Detection and Prevention System

2021 ◽  
Vol 12 (5) ◽  
pp. 1226-1236
Author(s):  
K. NandhaKumar, Et. al.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Network Intrusion ◽  
Prevention System ◽  
Adaptive Development ◽  
Intrusion Detection And Prevention

Network Intrusion detection and prevention Systems (NIDPS) are employed in monitoring a network which safeguards user integrity, privacy thereby ensuring the data security and availability in a network. Such systems not only monitor the suspicious activities in a network but also used as control systems to eliminate the malicious users from the network. In this paper, a Hybrid Adaptive Development Algorithm and Machine Learning Algorithm (ADA-MLA) method is proposed to identify the malicious activities and eliminating them from the network. The deployment of honeypot-based intrusion is improved adaptive development algorithm. Machine learning algorithm has been employed in the Hybrid IDPS for learning the network data patterns which also identifies the maximum probable attacks in the network. The signatures for the DARPA 99 data set have been updated during the implementation of intrusion prevention system on a real-time basis. The hybrid method works on (i) classifying the attacks based on protocols and (ii) classifying the attacks on pre-determined threshold values. Hence, both known and unknown attacks can be easily captured in the proposed hybrid IDPS method which thereby achieves higher attack detection and prevention accuracy while compared to the conventional attack detection and prevention methodologies.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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