In various network attacks, the Distributed Denial-of-Service DDoS attack is a severe threat. In order to deal with this kind of attack in time, it is necessary to establish a special type of defense system to change strategy dynamically against attacks. In this paper, we introduce an adaptive approach, which is used for defending against DDoS attacks, based on normal traffic analysis. The appr...

Recently, many attack detection methods adopts machine learning algorithm to improve attack detection accuracy and automatically react to the attacks. However, the previous mechanisms based on machine learning have some disadvantages such as high false positive rate and computing overhead. In this paper, we propose a new DDoS detection model based on multiple SVMs (Support Vector Machine) in or...

These days, Internet is the most essential medium for communication which is used by many users across the Network. Together, its commercial nature is causing enhance vulnerability to increase cyber crimes and there has been an immeasurable raise in the number of Distributed Denial of Service (DDOS) attacks on the internet over the past decade. Resources of network such as web servers, network ...

Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks are typically explicit attempts to exhaust victim’s bandwidth or disrupt legitimate users’ access to services. Traditional architecture of internet is vulnerable to DDoS attacks and it provides an opportunity to an attacker to gain access to a large number of compromised computers by exploiting their vulnerabilities to set ...

Denial of Service (DoS) attacks constitute one of the major threats and among the hardest security problems in today s Internet. Of particular concern are Distributed Denial of Service (DDoS) attacks, whose impact can be proportionally severe. With little or no advance warning, a DDoS attack can easily exhaust the computing and communication resources of its victim within a short period of time...

We present techniques for attributing amplification DDoS attacks to the booter services that launched the attack. Our k-Nearest Neighbor (k -NN) classification algorithm is based on features that are characteristic for a DDoS service, such as the set of reflectors used by that service. This allows us to attribute DDoS attacks based on observations from honeypot amplifiers, augmented with traini...

In this paper we present a low cost yet robust DDoS detection method to identify all classes of DDoS attacks. Our method attempts to detect DDoS attack by monitoring the deviation of the count of unique source IPs and the count of source IPs whose transmission rate is higher than a given threshold value. Unlike other similar existing methods, our method does not need to maintain a list of sourc...

The signalling protocol vulnerability opens DDoS problem in Mobile WiMAX networks. This letter proposes an authentication method that uses the unrevealed upper 64 bits of Cipher-based MAC as a solution. It runs for MSs in idle mode and reduces the calculation complexity by 59% under DDoS attack while incurring 1% overhead under normal condition. key words: SAI, protocol vulnerability, DDoS, aut...

Flooding-based DDoS attacks is a very common way to attack a victim machine by directly or indirectly sending a large amount of malicious traffic to it. Stochastic Fairness Queueing (SFQ) is a typical implementation of Fair Queueing. This paper focuses on exploring the feasibility of mitigating flooding-based DDoS attacks by queueing disciplines. A comparative study is made between SFQ and FCFS...