Flooding Distributed Denial of Service Attacks-A Review

Flaws either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched. Of the kinds of network attacks, denial-of-service flood attacks have caused the most severe impact. Approach: This study reviews recent researches on flood attacks and their mitigation, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are compared against criteria related to their characteristics, methods and impacts. Results: Denial-of-service flood attacks vary in their rates, traffic, targets, goals and impacts. However, they have general similarities that are the methods used are flooding and the main purpose is to achieve denial of service to the target. Conclusion/Recommendations: Mitigation of the denial-of-service flood attacks must correspond to the attack rates, traffic, targets, goals and impacts in order to achieve effective solution

Flooding Distributed Denial of Service Attacks-A Review

Problem statement: Flaws either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched. Of the kinds of network attacks, denial-of service flood attacks have caused the most severe impact. Approach: This study reviews recent researches on flood attacks and their mitigation, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are compared against criteria related to their characteristics, methods and impacts. Results: Denial-of service flood attacks vary in their rates, traffic, targets, goals and impacts. However, they have general similarities that are the methods used are flooding and the main purpose is to achieve denial of service to the target. Conclusion/Recommendations: Mitigation of the denial-of service flood attacks must correspond to the attack rates, traffic, targets, goals and impacts in order to achieve effective solution

NeuroNet: Towards an Intelligent Internet Infrastructure

Abstract -Malicious attacks against Internet infrastructure are one of the most damaging threats to modern society. Due to the inter-dependence between networks, attackers can paralyze or isolate the victim network without attacking it directly. Such kind of attacks could be a main weapon of cyber war in the foreseeable future, potentially detrimental to many national interests. Today's network security solutions designed under the end-to-end paradigm cannot address the malicious activities inside the core network effectively. This paper proposed to strengthen the network infrastructure by developing NeuroNet, a network neural system that plays a critical role similar to the role of the nervous system in the human body. A distributed information collection and processing mechanism is proposed, which coordinates the activities of core network devices, monitor for anomalies, construct alerts, and initiate countermeasures. Through intensive experiment of a distributed detection scheme against the low-rate TCP-targeted DDoS attacks, the effectiveness of the NeuroNet architecture is verified

IoT Networks: Using Machine Learning Algorithm for Service Denial Detection in Constrained Application Protocol

The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed

DDoS: DeepDefence and Machine Learning for identifying attacks

Distributed Denial of Service (DDoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through the network is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this work and developments have been made on proactive detection of attacks. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset published in year 2017,2018 and CICDDoS2019 and program has been developed in Matlab R17b, utilizing Wireshark for features extraction from the datasets. Network Intrusion attacks on critical oil and gas industrial installation become common nowadays, which in turn bring down the giant industrial sites to standstill and suffer financial impacts. This has made the production companies to started investing millions of dollars revenue to protect their critical infrastructure with such attacks with the active and passive solutions available. Our thesis constitutes a contribution to such domain, focusing mainly on security of industrial network, impersonation and attacking with DDoS

Likelihood ratios and recurrent random neural networks in detection of denial of service attacks

In a world that is becoming increasingly dependent on In- ternet communication, Denial of Service (DoS) attacks have evolved into a major security threat which is easy to launch but difficult to defend against. In order for DoS countermea- sures to be effective, the attack must be detected early and accurately. In this paper we propose a DoS detection tech- nique based on observation of the incoming traffic and a com- bination of traditional likelihood estimation with a recurrent random neural network (r-RNN) structure. We select input features that describe essential information on the incoming traffic and evaluate the likelihood ratios for each input, to fuse them with a r-RNN. We evaluate the performance of our method in terms of false alarm and correct detection rates with experiments on a large networking testbed, for a variety of input traffic

A Study of Very Short Intermittent DDoS Attacks on the Performance of Web Services in Clouds

Distributed Denial-of-Service (DDoS) attacks for web applications such as e-commerce are increasing in size, scale, and frequency. The emerging elastic cloud computing cannot defend against ever-evolving new types of DDoS attacks, since they exploit various newly discovered network or system vulnerabilities even in the cloud platform, bypassing not only the state-of-the-art defense mechanisms but also the elasticity mechanisms of cloud computing. In this dissertation, we focus on a new type of low-volume DDoS attack, Very Short Intermittent DDoS Attacks, which can hurt the performance of web applications deployed in the cloud via transiently saturating the critical bottleneck resource of the target systems by means of external attack HTTP requests outside the cloud or internal resource contention inside the cloud. We have explored external attacks by modeling the n-tier web applications with queuing network theory and implementing the attacking framework based-on feedback control theory. We have explored internal attacks by investigating and exploiting resource contention and performance interference to locate a target VM (virtual machine) and degrade its performance