Distributed Denial of Service Attacks: A Threat or Challenge

Chahal, Jasmeen Kaur; Bhandari, Abhinav; Behal, Sunny

doi:10.1080/13614576.2019.1611468

Cited by 25 publications

(7 citation statements)

References 76 publications

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“…These devices are infected with malware to manipulate them from a remote region outside the control of their valid proprietors. These botnet devices are used for illicit purposes, such as for junk mail or DDoS attacks [2]. DDoS can likewise be applied as a smokescreen for malicious activities and attack safety apparatuses, attacking the target's safety.…”

Section: Zombies In the Ddos Contextmentioning

confidence: 99%

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Qamar

2022

ijcsm

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A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. A website may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannot handle real ones. A DDoS attack affects a large number of computers. Attackers employ a zombie network, which is a collection of infected machines on which the attacker has hidden the denial-of-service attacking application to carry out a DDoS attack. The MATLAB 2018a simulator was used in this study for training. Additionally, during design, the knowledge discovery dataset (KDD) was cleaned and the values of attacks were incorporated. A neural network model was subsequently developed, and the KDD was trained using a recursive artificial neural network. This network was developed using five distinct training algorithms: 1) Fletcher–Powell conjugate gradient, 2) Polak–Ribiére conjugate gradient of, 3) resilient backpropagation, 4) gradient conjugation with Powell/Beale restarts, and 5) gradient descent algorithm with variable learning rate. The artificial neural network toolset in MATLAB was used to investigate the detection of DDoS attacks. The conjugate gradient with Powell/Beale restart algorithm had a success rate of 99.9% and a training time of 00:53. This inquiry uses the KDD-CUP99 dataset. Has a better level of accuracy, according to the results

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Section: Zombies In the Ddos Contextmentioning

confidence: 99%

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Qamar

2022

ijcsm

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“…Cloud computing is a new level of networking since it offers limitless processing capacity and storage, which poses security concerns [20]. Cyber-attacks such as distributed denial of service (DDoS) attacks that deliver harmful packets [26] and phishing attempts that trick users on banking and shopping sites have increased dramatically. Furthermore, attackers are increasingly deploying malicious attack software (viruses, worms, trojans, spyware, and ransomware) that is installed on a user's computer without their knowledge or agreement [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for Crime Detection and Diminution in Digital Forensics (CD3F)

Singh¹,

Singh²,

Vege³

et al. 2022

IJACSA

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Cyber-attacks have become one of the world's most serious issues. Every day, they wreak serious financial harm to governments and people. As cyber-attacks become more common, so does cyber-crime. Identifying cyber-crime perpetrators and understanding attack tactics are critical in the battle against crime and criminals. Cyber-attack detection and prevention are difficult undertakings. Researchers have lately developed security models and made forecasts using artificial intelligence technologies to solve these concerns. In the literature, the authors explained numerous ways of predicting crime. They, on the other hand, have a problem forecasting cyber-crime and cyber-attack strategies. Here, in this paper author proposed a digital forensic investigation procedure that deals with cybercrime. In this investigation, the process author explains digital forensics techniques for ensuring that digital evidence is located, collected, preserved, evaluated, and reported in such a way that the evidence's integrity is preserved. These sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks. Digital forensics investigation is a technique for ensuring that digital evidence is handled in such a way that the evidence's integrity is preserved. Sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks.

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“…The main goal of a reflector attack is to mask the identity of the real attacker through the use of third-party "reflectors" and then to benefit from their resources [31]. The attacker initiates an attack from zombie machines and instructs them to send traffic toward the target through third parties with the victim's IP address [32]. All the reflectors reply to the victim, while the original request comes from the attacking source, thus causing a huge amount of malicious traffic that consumes the target network resources [33].…”

Section: Reflector Attacksmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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Distributed Denial of Service Attacks: A Threat or Challenge

Cited by 25 publications

References 76 publications

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

A Framework for Crime Detection and Diminution in Digital Forensics (CD3F)

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

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