DDoS attacks and countermeasures in cyberspace

Zeb, Khan; Baig, Owais; Asif, Muhammad

doi:10.1109/wswan.2015.7210322

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…The unwanted flood packets, which can cause the failure of network. The attackers consume network bandwidth and CPU usage, as a result, the server cannot serve the users of internet [18]. In this paper, we identify and analyze making and filtering in DoS attacks with packet sniffing.…”

Section: Introductionmentioning

confidence: 99%

Denial of Service (DoS) attack identification and analyse using sniffing technique in the network environment

Fidele

Suryono

Syafei³

2020

E3S Web Conf.

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Network-based intruders such as (DoS) attacks have become one of the most significant internet interruptions. Some operations that rely on the internet, such as banking transactions, education, trade marketing, and social networking, have become the primary targets. The attacker is trying to surround and making it difficult for the system to defend. The research's objective is to recognize the characteristics and level of DoS attacks. In understanding the behavior of intruders against a target web server, Wireshark was used in all traffic networks—capturing the traffic in a networked environment. In this research, the user identifies the attack levels (TCP SYN, UDP, and HTTP protocol), ranging from low (Q1), medium (Q2), and high (Q4) attacks. The approach is to simulate the TCP, HTTP, and UDP flood attacks and analyze the attacks' effects on the network environment. In this work, normal scenarios and pattern attacks were compared. In this case, the intruder floods unwanted packets to the victim with a massive number of request packets; the SYN from the corresponding SYN-ACK replies are not achieved. This paper will identify the DoS attacks level and analyze the behavior of traffics.

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Section: Introductionmentioning

confidence: 99%

Denial of Service (DoS) attack identification and analyse using sniffing technique in the network environment

Fidele

Suryono

Syafei³

2020

E3S Web Conf.

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“…If the CIS is flooded with an overly large number of data requests and/or input throughout the network, the CIS will be unable to cope and will eventually stop functioning and become unavailable to its users. This kind of cyber attack is called Denial-of-Service (DoS) attack [2], [3]. DoS attacks are typically collaborative, and target specific victims (routers, servers, CIS, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…A DRDOS attack is an attack which is achieved through the use of master zombies, slave zombies, and reflectors. Proposed defense mechanisms for this kind of attacks are split into four basic categories, namely attack prevention, attack detection, attack source identification, and attack reaction [2].…”

Section: Introductionmentioning

confidence: 99%

Detection of collaborative cyber-attacks through correlation and time dependency analysis

Thoma

Hadjicostis

2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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In this paper, we consider the detection of suspiciously high correlation between malicious Internet users that are collaborating in order to cause a Denial of Service (DoS) Attack. The main goal is to recognise cyber incidents (more specifically, collaboration/dependency between different users) in order to ultimately isolate their behavior and avoid the consequenses of the DoS attack. The method relies on the analysis of the data traffic across the concerned network (with both incoming and outgoing traffic) in an effort to identify correlations between different users. The paper models normal and malicious behaviour via hidden Markov models, and analyses the performance of the method using both mathematical reasoning and simulations.

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“…At present, under the traditional network environment, methods for defense against DDoS attacks mainly include attack detection and attack response [7]. DDoS Attack detection is based on attack signatures, congestion patterns, protocols, and source addresses as an important basis for detecting attacks, thereby establishing an effective detection mechanism.…”

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confidence: 99%

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

Cheng¹,

Zhang

Tang

et al. 2018

Security and Communication Networks

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Correspondence should be addressed to Chen Zhang; 314848554@qq.com.Distributed denial of service (DDoS) attacks have caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the inter-class mean with a gradient ascent and reducing the intra-class variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple kernel learning (SMKL) models with two characteristics including inter-class mean squared difference growth (M-SMKL) and intra-class variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

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DDoS attacks and countermeasures in cyberspace

Cited by 15 publications

References 23 publications

Denial of Service (DoS) attack identification and analyse using sniffing technique in the network environment

Denial of Service (DoS) attack identification and analyse using sniffing technique in the network environment

Detection of collaborative cyber-attacks through correlation and time dependency analysis

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

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