A survey of defense mechanisms against application layer distributed denial of service attacks

Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

“…e Smart Detection system has reached high accuracy and low false-positive rate. Experiments were conducted using two Virtual Linux boxes, Define all the descriptor database variables as the current variables; (5) while True do (6) Split dataset in training and test partitions; (7) Create and train the model using training data partition; (8) Select the most important variables from the trained model; (9) Calculate the cumulative importance of variables from the trained model; (10) if max (cumulative importance of variables) < Variable importance threshold then (11) Exit loop; (12) end (13) Train the model using only the most important variables; (14) Test the trained model and calculate the accuracy; (15) if Calculated accuracy < Accuracy threshold then (16) Exit loop; (17) end (18) Add current model to optimized model set; (19) Define the most important variables from the trained model as the current variables; (20) end (21) end (22) Group the models by number of variables; (23) Remove outliers from the grouped model set; (24) Select the group of models with the highest frequency and their number of variables "N"; (25) Rank the variables by the mean of the importance calculated in step 7; (26) Return the "N" most important variables; [2004][2005] have been used by the researchers to evaluate the performance of their proposed intrusion detection and prevention approaches. However, many such datasets are out of date and unreliable to use [25].…”

“…have been under study in both the scientific community and industry for several years. e related literature reveals that several studies have undertaken to propose solutions to deal with this problem in a general way [6,[11][12][13][14][15]. Another group of works dedicated themselves to presenting specific solutions for high-volume and low-volume DDoS attacks [8,13,16].…”

A survey and meta‐analysis of application‐layer distributed denial‐of‐service attack

“…They reported in their studies that hybrid techniques received a great deal of attention from researchers in detecting the APDDoS attack. Wang et al 38 further review various APDDoS detection methods and classified them into two categories with the perspective of differentiating between the two categories based on four aspects, namely the deployment difficulty, anomaly detection rate, false acceptance rate, and false rejection rate. This study, to the best of our knowledge, is the first to provide a detailed review of APDDoS attack detection based on different procedures, namely techniques/methods, attack strategy, status, and features exploration.…”

Ameaças de Segurança, Defesas e Análise de Dados em IoT Baseada em SDN

“…O ataque de negação de serviço distribuído, comumente referenciado como DDoS (Distributed Denial of Service), é uma variação do ataque tradicional de negação de serviço. Esta variação do ataque emprega de forma coordenada uma grande quantidade de dispositivos previamente infectados para intensificar o poder disruptivo do ataque e dificultar a identificação do atacante [Wang et al 2015, Paxson 2001]. Usualmente, os atacantes utilizam dispositivos interconectados através de infraestruturas conhecidas como botnets para executar ataques DDoS.…”

“…Existem quatro principais tipos de ataques DDoS baseados em geração de carga HTTP, inundação de sessão, inundação de requisições, assimétricos e com requisições e/ou respostas lentos [Wang et al 2015]. Nos ataques DDoS de inundação de sessão as taxas de requisição de conexão de sessão dos atacantes são maiores que as requisições dos usuários legítimos.…”