scholarly journals SDN-Based Intrusion Detection System for Early Detection and Mitigation of DDoS Attacks

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 106 ◽  
Author(s):  
Pedro Manso ◽  
José Moura ◽  
Carlos Serrão
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Performance ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Normal Operation ◽  
Ddos Attacks ◽  
Network Infrastructure ◽  
Sdn Controller

The current paper addresses relevant network security vulnerabilities introduced by network devices within the emerging paradigm of Internet of Things (IoT) as well as the urgent need to mitigate the negative effects of some types of Distributed Denial of Service (DDoS) attacks that try to explore those security weaknesses. We design and implement a Software-Defined Intrusion Detection System (IDS) that reactively impairs the attacks at its origin, ensuring the “normal operation” of the network infrastructure. Our proposal includes an IDS that automatically detects several DDoS attacks, and then as an attack is detected, it notifies a Software Defined Networking (SDN) controller. The current proposal also downloads some convenient traffic forwarding decisions from the SDN controller to network devices. The evaluation results suggest that our proposal timely detects several types of cyber-attacks based on DDoS, mitigates their negative impacts on the network performance, and ensures the correct data delivery of normal traffic. Our work sheds light on the programming relevance over an abstracted view of the network infrastructure to timely detect a Botnet exploitation, mitigate malicious traffic at its source, and protect benign traffic.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

scholarly journals DDOS ATTACK DETECTION AND MITIGATION AT SDN ENVIROMENT

2021 ◽  
Vol 4 (1) ◽  
pp. 1-9
Author(s):  
Huda S. Abdulkarem ◽  
Ammar D. Alethawy
Keyword(s):  
Network Performance ◽  
Denial Of Service ◽  
Attack Detection ◽  
Normal Operation ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Negative Effects ◽  
Network Infrastructure ◽  
Ddos Attack ◽  
Negative Impacts

Abstract- Software-Defined Networking (SDN) is a promising sample that allows the programming behind the network’s operation with some abstraction level from the underlying networking devices .the insistence to detect and mitigate Distributed Denial of Service (DDoS) which introduced by network devices tries to discover network security weaknesses and the negative effects of some types of Distributed Denial of Service (DDoS) attacks. An SDN-based generic solution to mitigate DDoS attacks when and where they originate. Briefly, it compares at runtime the expected trend of normal traffic against the trend of abnormal traffic; if big deviation on the traffic trend is detected, then an event is created; as an event associated to a DDoS attack is produced, an SDN (OpenDayLight) controller creates flow rules for blocking the malign traffic, By designing and implementing an application that reactively impairs the attacks at its origin, ensuring the “normal operation” of the network infrastructure. The evaluation results suggest that the proposal timely detect the characteristics of a flooding DDoS attacks, and mitigates their negative impacts on the network performance, and ensures the correct data delivery of normal traffic. The work sheds light on the programming relevance over an abstracted view of the network infrastructure.

scholarly journals Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Ahmad Azhari ◽  
Arif Wirawan Muhammad ◽  
Cik Feresa Mohd Foozy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Policy Development ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
Detection Techniques

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.

scholarly journals Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1257
Author(s):  
Mohamed Amine Ferrag ◽  
Lei Shu ◽  
Hamouda Djallel ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Future Generation ◽  
Cyber Attacks ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Service Attack

Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.

scholarly journals Hybrid Intrusion Detection System for DDoS Attacks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Özge Cepheli ◽  
Saliha Büyükçorak ◽  
Güneş Karabulut Kurt
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Hybrid Detection ◽  
Security Problem

Distributed denial-of-service (DDoS) attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS), for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.

scholarly journals Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 220-230
Author(s):  
Shubhra Dwivedi ◽  
Manu Vardhan ◽  
Sarsij Tripathi
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Information Gain ◽  
Learning Algorithm ◽  
Detection System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Support Vector ◽  
Distributed Denial Of Service ◽  
Ddos Attacks

AbstractDistributed denial-of-service (DDoS) attacks on the Internet of Things (IoT) pose a serious threat to several web-based networks. The intruder’s ability to deal with the power of various cooperating devices to instigate an attack makes its administration even more multifaceted. This complexity can be further increased while lots of intruders attempt to overload an attack against a device. To counter and defend against modern DDoS attacks, several effective and powerful techniques have been used in the literature, such as data mining and artificial intelligence for the intrusion detection system (IDS), but they have some limitations. To overcome the existing limitations, in this study, we propose an intrusion detection mechanism that is an integration of a filter-based selection technique and a machine learning algorithm, called information gain-based intrusion detection system (IGIDS). In addition, IGIDS selects the most relevant features from the original IDS datasets that can help to distinguish typical low-speed DDoS attacks and, then, the selected features are passed on to the classifiers, i.e. support vector machine (SVM), decision tree (C4.5), naïve Bayes (NB) and multilayer perceptron (MLP) to detect attacks. The publicly available datasets as KDD Cup 99, CAIDA DDOS Attack 2007, CONFICKER worm, and UNINA traffic traces, are used for our experimental study. From the results of the simulation, it is clear that IGIDS with C4.5 acquires high detection and accuracy with a low false-positive rate.

Implementasi Iptables Firewall dan Intrusion Detection System Untuk Mencegah Serangan DDoS Pada Linux Server

2021 ◽  
pp. 19-23
Author(s):  
Theodorus Kristian Widianto ◽  
Wiwin Sulistyo
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Ddos Attacks ◽  
Server Side ◽  
Ddos Attack ◽  
Internet Users ◽  
Data Theft ◽  
Server Computer

Security on computer networks is currently a matter that must be considered especially for internet users because many risks must be borne if this is negligent of attention. Data theft, system destruction, and so on are threats to users, especially on the server-side. DDoS is a method of attack that is quite popular and is often used to bring down servers. This method runs by consuming resources on the server computer so that it can no longer serve requests from the user side. With this problem, security is needed to prevent the DDoS attack, one of which is using iptables that has been provided by Linux. Implementing iptables can prevent or stop external DDoS attacks aimed at the server.

scholarly journals Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ivandro Ortet Lopes ◽  
Deqing Zou ◽  
Francis A Ruambo ◽  
Saeed Akbar ◽  
Bin Yuan
Keyword(s):  
Deep Learning ◽  
Intrusion Detection System ◽  
State Of The Art ◽  
Detection System ◽  
Denial Of Service ◽  
Model Performance ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Validation Metrics ◽  
High Processing

Distributed Denial of Service (DDoS) is a predominant threat to the availability of online services due to their size and frequency. However, developing an effective security mechanism to protect a network from this threat is a big challenge because DDoS uses various attack approaches coupled with several possible combinations. Furthermore, most of the existing deep learning- (DL-) based models pose a high processing overhead or may not perform well to detect the recently reported DDoS attacks as these models use outdated datasets for training and evaluation. To address the issues mentioned earlier, we propose CyDDoS, an integrated intrusion detection system (IDS) framework, which combines an ensemble of feature engineering algorithms with the deep neural network. The ensemble feature selection is based on five machine learning classifiers used to identify and extract the most relevant features used by the predictive model. This approach improves the model performance by processing only a subset of relevant features while reducing the computation requirement. We evaluate the model performance based on CICDDoS2019, a modern and realistic dataset consisting of normal and DDoS attack traffic. The evaluation considers different validation metrics such as accuracy, precision, F1-Score, and recall to argue the effectiveness of the proposed framework against state-of-the-art IDSs.

Intrusion Detection System for 5G with a Focus on DOS/DDOS Attacks

2021 ◽  
Author(s):  
Giorgi Iashvili ◽  
Maksim Iavich ◽  
Razvan Bocu ◽  
Roman Odarchenko ◽  
Sergiy Gnatyuk
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Ddos Attacks
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