"Our study yielded several noteworthy results and accomplishments," Mustapha explained. In addition, it can be easily deployed by internet service providers (ISPs), while protecting them against both standard and adversarial DDoS attacks. Most notably, it is robust and can detect DDoS attacks with high levels of accuracy, it is adaptable, and it could also be tailored to meet the unique needs of specific businesses or users. The DDoS detection tool proposed by this team of researchers has numerous advantages over other intrusion detection systems developed in the past. Depending on the outcome of this analysis, a corresponding set of rules and an alert system are employed." "Otherwise, it is then forwarded to the second model, which is responsible for identifying whether it constitutes a DDoS attack. "The first model is designed to determine whether the incoming network traffic is adversarial and block it if it is deemed fraudulent," Mustapha explained. "While previous studies have explored the use of deep learning algorithms to detect DDoS attacks, these approaches may still be vulnerable to attackers who utilize machine learning and deep learning techniques to create adversarial attack traffic capable of bypassing detection systems." "Our research paper was based on the problem of detecting DDoS attacks, a type of cyber-attacks that can cause significant damage to online services and network communication," Ali Mustapha, one of the researchers who carried out the study, told Tech Xplore. This method, introduced in a paper published in Computers & Security, is based on a long short-term memory (LSTM) model, a type of recurrent neural network (RNN) that can learn to detect long-term dependencies in event sequences. Researchers at Institut Polytechnique de Paris, Telecom Paris (INFRES) have recently developed a new computational method that could detect DDoS attacks more effectively and reliably. Yet detecting these attacks can be very challenging today, as they are often carried out using generative adversarial networks (GANs), machine learning techniques that can learn to realistically mimic the activity of real users and legitimate user requests.Īs a result, many existing anti-malware systems ultimately fail to secure users against them. To protect their website or servers from DDoS attacks, businesses and other users commonly use firewalls, anti-malware software or conventional intrusion detection systems. This type of attack involves a series of devices connected to the internet, which are collectively referred to as a "botnet." This "group" of connected devices is then used to flood a target server or website with "fake" traffic, disrupting its operation and making it inaccessible to legitimate users.
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