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Relevant bibliographies by topics / Detection of slow attacks

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Journal articles

Academic literature on the topic 'Detection of slow attacks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Detection of slow attacks"

1

Baldev Singh, Dr, Dr S.N. Panda, and Dr Gurpinder Singh Samra. "Slow flooding attack detection in cloud using change point detection approach." International Journal of Engineering & Technology 7, no. 2.30 (2018): 33. http://dx.doi.org/10.14419/ijet.v7i2.30.13459.

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Cloud computing is one of the high-demand services and prone to numerous types of attacks due to its Internet based backbone. Flooding based attack is one such type of attack over the cloud that exhausts the numerous resources and services of an individual or an enterprise by way of sending useless huge traffic. The nature of this traffic may be of slow or fast type. Flooding attacks are caused by way of sending massive volume of packets of TCP, UDP, ICMP traffic and HTTP Posts. The legitimate volume of traffic is suppressed and lost in traffic flooding traffics. Early detection of such attack

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2

Dhanapal, A., and P. Nithyanandam. "The Slow HTTP Distributed Denial of Service Attack Detection in Cloud." Scalable Computing: Practice and Experience 20, no. 2 (2019): 285–98. http://dx.doi.org/10.12694/scpe.v20i2.1501.

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Cloud computing became popular due to nature as it provides the flexibility to add or remove the resources on-demand basis. This also reduces the cost of investments for the enterprises significantly. The adoption of cloud computing is very high for enterprises running their online applications. The availability of online services is critical for businesses like financial services, e-commerce applications, etc. Though cloud provides availability, still these applications are having potential threats of going down due to the slow HTTP Distributed Denial of Service (DDoS) attack in the cloud. Th

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3

Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. T

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4

Dhanapal, A., and P. Nithyanandam. "The Slow HTTP DDOS Attacks: Detection, Mitigation and Prevention in the Cloud Environment." Scalable Computing: Practice and Experience 20, no. 4 (2019): 669–85. http://dx.doi.org/10.12694/scpe.v20i4.1569.

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Cloud computing is the latest buzzword and cutting-edge technology. The cost-efficiency, easy to operate, on-demand services, availability, makes the cloud so popular. The online web applications based on the internet such as E-Healthcare, E-Commerce are moving to the cloud to reduce the operating investment cost. These applications are vulnerable to slow HTTP Distributed Denial of Service (DDoS) attack in the cloud. This kind of attacks aims to consume the resources of the application as well as the hosting system so that to bring down the services. The various forms of the slow HTTP DDoS are

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5

Sikora, Marek, Radek Fujdiak, Karel Kuchar, Eva Holasova, and Jiri Misurec. "Generator of Slow Denial-of-Service Cyber Attacks." Sensors 21, no. 16 (2021): 5473. http://dx.doi.org/10.3390/s21165473.

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In today’s world, the volume of cyber attacks grows every year. These attacks can cause many people or companies high financial losses or loss of private data. One of the most common types of attack on the Internet is a DoS (denial-of-service) attack, which, despite its simplicity, can cause catastrophic consequences. A slow DoS attack attempts to make the Internet service unavailable to users. Due to the small data flows, these attacks are very similar to legitimate users with a slow Internet connection. Accurate detection of these attacks is one of the biggest challenges in cybersecurity. In

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6

Prathivi, Rastri, and Vensy Vydia. "ANALISA PENDETEKSIAN WORM dan TROJAN PADA JARINGAN INTERNET UNIVERSITAS SEMARANG MENGGUNAKAN METODE KALSIFIKASI PADA DATA MINING C45 dan BAYESIAN NETWORK." Jurnal Transformatika 14, no. 2 (2017): 77. http://dx.doi.org/10.26623/transformatika.v14i2.440.

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<p>Worm attacks become a dangerous threat and cause damage in the Internet network. If the Internet network worms and trojan attacks the very disruption of traffic data as well as create bandwidth capacity has increased and wasted making the Internet connection is slow. Detecting worms and trojan on the Internet network, especially new variants of worms and trojans and worms and trojans hidden is still a challenging problem. Worm and trojan attacks generally occur in computer networks or the Internet which has a low level of security and vulnerable to infection. The detection and analysi

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7

Savchenko, Vitalii. "Detection of Slow DDoS Attacks based on User’s Behavior Forecasting." International Journal of Emerging Trends in Engineering Research 8, no. 5 (2020): 2019–25. http://dx.doi.org/10.30534/ijeter/2020/90852020.

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8

Zhang, Wei, Shaohua Teng, Haibin Zhu, and Dongning Liu. "A Cooperative Intrusion Detection Model Based on Granular Computing and Agent Technologies." International Journal of Agent Technologies and Systems 5, no. 3 (2013): 54–74. http://dx.doi.org/10.4018/ijats.2013070104.

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This paper initially analyzes the methods of four attack types, including Probing, DoS (Denial of Service), R2L (Remote to Local) and U2R (User to Root). It then categorizes attacks into four cases which are, respectively, one host-one host, one host-many hosts, many hosts-one host and many hosts-many hosts. Categorization is based on resource and destination addresses of network packages. Granular computing methodology is then applied to intrusion detection. With the support of the granular computing methodology and agent technologies, a cooperative intrusion detection model is proposed. Furt

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9

Satterfield, Kelly, Vincent F. Mancuso, Adam Strang, Eric Greenlee, Brent Miller, and Gregory J. Funke. "When actions speak louder than words: Using changes in operator behavior and system efficiency measures to detect the presence of a cyber attack." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 270–71. http://dx.doi.org/10.1177/1541931218621062.

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Increases in cyber incidents have required substantial investments in cyber defense for national security. However, adversaries have begun moving away from traditional cyber tactics in order to escape detection by network defenders. The aim of some of these new types of attacks is not to steal information, but rather to create subtle inefficiencies that, when aggregated across a whole system, result in decreased system effectiveness. The aim of such attacks is to evade detection for long durations, allowing them to cause as much harm as possible. As a result, such attacks are sometimes referre

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10

Chen, Li Ming, Shun-Wen Hsiao, Meng Chang Chen, and Wanjiun Liao. "Slow-Paced Persistent Network Attacks Analysis and Detection Using Spectrum Analysis." IEEE Systems Journal 10, no. 4 (2016): 1326–37. http://dx.doi.org/10.1109/jsyst.2014.2348567.

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