Journal ArticleDOI
An efficient algorithm to detect DDoS amplification attacks
Abdul Quadir,J. Christy Jackson,J. Prassanna,K. Sathyarajasekaran,Kamal Kumar,H. Sabireen,Shivam Ubarhande,Vijaya Kumar +7 more
TLDR
This paper discusses such intelligent method to detect the attack server from legitimate traffic that uses an algorithm that gets activated by excess traffic in the network.Abstract:
Domain name system (DNS) plays a critical part in the functioning of the Internet. But since DNS queries are sent using UDP, it is vulnerable to Distributed Denial of Service (DDoS) attacks. The attacker can take advantage of this and spoof the source IP address and direct the response towards the victim network. And since the network does not keep track of the number of requests going out and responses coming in, the attacker can flood the network with these unwanted DNS responses. Along with DNS, other protocols are also exploited to perform DDoS. Usage of Network Time Protocol (NTP) is to synchronize clocks on systems. Its monlist command replies with 600 entries of previous traffic records. This response is enormous compared to the request. This functionality is used by the attacker in DDoS. Since these attacks can cause colossal congestion, it is crucial to prevent or mitigate these types of attacks. It is obligatory to discover a way to drop the spoofed packets while entering the network to mitigate this type of attack. Intelligent cybersecurity systems are designed for the detection of these attacks. An Intelligent system has AI and ML algorithms to achieve its function. This paper discusses such intelligent method to detect the attack server from legitimate traffic. This method uses an algorithm that gets activated by excess traffic in the network. The excess traffic is determined by the speed or rate of the requests and responses and their ratio. The algorithm extracts the IP addresses of servers and detects which server is sending more packets than requested or which are not requested. This server can be later blocked using a firewall or Access Control List (ACL).read more
Citations
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Efficient Dynamic Phishing Safeguard System Using Neural Boost Phishing Protection
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A novel approach to detect fraud in Ethereum transactions using stacking
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Proceedings ArticleDOI
A Robust Pipeline Approach for DDoS Classification using Machine Learning
TL;DR: A robust pipeline for DDoS classification is proposed and the performance of the models are calculated against the metrics such as precision, recall and f1-scores and the XGboost algorithm works well on the data set with an accuracy score of 99% outperforming other models.
Proceedings ArticleDOI
Reliability of Smart-Wearables using PSO-GA Optimized Algorithm in Terms of Data Analysis
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References
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Journal ArticleDOI
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TL;DR: A systematic analysis of distributed denial-of-service attacks including motivations and evolution, analysis of different attacks so far, protection techniques and mitigation techniques, and possible limitations and challenges of existing research are provided.
Journal ArticleDOI
A reversible sketch-based method for detecting and mitigating amplification attacks
TL;DR: This work uses a Chinese Reminder Theorem based Reversible Sketch to directly collect network traffic and then monitors the abrupt changes in one-to-one mapping between request packets and response packets to identify amplification attack traffic, which enables the detection method to handle big-volume network traffic.
Journal ArticleDOI
Source-side detection of drdos attack request with traffic-aware adaptive threshold
TL;DR: A novel method to detect DRDoS attack request traffic on SDN(Software Defined Network)-enabled gateways in the source side of attack traffic is proposed and provides a traffic-aware adaptive threshold along with the margin based on analysing observed traffic behind gateways.