BARTD: Bio-inspired anomaly based real time detection of under rated App-DDoS attack on web

Prasad, KDV; Reddy, A. Rama Mohan; Rao, K. Venugopal

doi:10.1016/j.jksuci.2017.07.004

Cited by 24 publications

(15 citation statements)

References 23 publications

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“…According to Shinde and Parvat [26], using the NSL-KDD dataset, we apply the hybrid form of PSO and ABC on SVM for solving feature selection and classification problems to achieve high DR and low FAR. Prasad et al [27] analyzed metaheuristic anomaly-based algorithms for realtime detection of application layer distributed denial of service attack successfully detected by using the hybrid combination of cuckoo search, bat, and firefly algorithm and proved to be an efficient technique by improving the parameters such as accuracy, efficiency, and performance analysis. Jadidi et al [28] proposed multilevel perceptron (MLP) based on the anomaly attack detection method in a high-speed network.…”

Section: Important Steps Of Classification and Machine Learningmentioning

confidence: 99%

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

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Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

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Section: Important Steps Of Classification and Machine Learningmentioning

confidence: 99%

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The experimental evaluation of this technique conducted upon NSL-KDD dataset and decision tree classifier is utilized to estimate fitness. Prasad et al [26] introduced the DDoS detection model based on bio-inspired anomaly for the detection of App-DDoS attacks on the web. The results acquired from the proposed approach have demonstrated the importance and strength of the model to achieve the objectives deliberated for the solution.…”

Section: Related Workmentioning

confidence: 99%

Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques

Dwivedi

Vardhan

Tripathi

2020

Open Computer Science

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Distributed denial-of-service (DDoS) attacks on the Internet of Things (IoT) pose a serious threat to several web-based networks. The intruder’s ability to deal with the power of various cooperating devices to instigate an attack makes its administration even more multifaceted. This complexity can be further increased while lots of intruders attempt to overload an attack against a device. To counter and defend against modern DDoS attacks, several effective and powerful techniques have been used in the literature, such as data mining and artificial intelligence for the intrusion detection system (IDS), but they have some limitations. To overcome the existing limitations, in this study, we propose an intrusion detection mechanism that is an integration of a filter-based selection technique and a machine learning algorithm, called information gain-based intrusion detection system (IGIDS). In addition, IGIDS selects the most relevant features from the original IDS datasets that can help to distinguish typical low-speed DDoS attacks and, then, the selected features are passed on to the classifiers, i.e. support vector machine (SVM), decision tree (C4.5), naïve Bayes (NB) and multilayer perceptron (MLP) to detect attacks. The publicly available datasets as KDD Cup 99, CAIDA DDOS Attack 2007, CONFICKER worm, and UNINA traffic traces, are used for our experimental study. From the results of the simulation, it is clear that IGIDS with C4.5 acquires high detection and accuracy with a low false-positive rate.

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“…The detection techniques may impact the detection performance and also the quality for the low rate of application layer attacks under different loads. Prasad et al proposed a bioinspired approach (cuckoo search) for the detection of application layer attacks with minimal complexity [41]. It cannot detect the suspicious source of attacks and flash crowds.…”

Section: Application Layer Attacksmentioning

confidence: 99%

HAP: detection of HTTP flooding attacks in cloud using diffusion map and affinity propagation clustering

Sree

Bhanu

2019

IET Information Security

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BARTD: Bio-inspired anomaly based real time detection of under rated App-DDoS attack on web

Cited by 24 publications

References 23 publications

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Distributed Denial-of-Service Prediction on IoT Framework by Learning Techniques

HAP: detection of HTTP flooding attacks in cloud using diffusion map and affinity propagation clustering

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