Mitigating Address Spoofing Attacks in Hybrid SDN

Ubaid, Fahad; Amin, Rashid; Ubaid, Faisal Bin; Iqbal, Muhammad Muwar

doi:10.14569/ijacsa.2017.080474

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…In [23], the authors demonstrated the feasibility of attacking controllers from a data layer, by implementing and testing the "fingerprint" technique of the SDN controller, with the primary goal of emphasizing the need for high controller security. By introducing open source SDN interfaces and known protocols to simplify programming, the network functionality allows attacker location detection presented in [24], where in a hybrid SDN the attacker is detected by analyzing the ARP request from the source. A graph-based switching mechanism is also used to detect the location of the attacker by checking legitimate users.…”

Section: Security Features and Attack Vectors In Software Defined Networkmentioning

confidence: 99%

Security Features in a Hybrid Software-Defined Network

Fosić¹,

Žagar

2021

Teh. vjesn.

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The paper presents a novel paradigm of software-defined network that is significantly different from previous traditional networks and enables new opportunities in the architecture and implementation of security solutions. The analysis of network environments will compare traditional networks and software-defined networks and emphasize significant differences. A survey of the existing research includes vector attacks and troubleshooting using the capabilities of SDN with an emphasis on access control, detection, and prevention of attacks. This paper uses previous research and results to obtain information that will be used in improving critical system network protection and compares it with the existing conventional approach as well as implements it through a hybrid software-defined network.

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Section: Security Features and Attack Vectors In Software Defined Networkmentioning

confidence: 99%

Security Features in a Hybrid Software-Defined Network

Fosić¹,

Žagar

2021

Teh. vjesn.

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“…Ubaid et al, [6] presented a graph based mechanism to tackle the ARP spoofing attacks. The graph based traversal mechanism detects the location of the forged or compromised hosts through the pattern of the request packets.…”

Section: Sm-arp: Stochastic Markovian Gamementioning

confidence: 99%

“…Using the likelihood, cost and reward functions, the utility functions of source and relay nodes can be computed using Eq. (6) Adding the likelihood of forwarding source packets, , to the Eq. 5and (6) provides utility functions for the flexible relay method.…”

Section: Wherementioning

confidence: 99%

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SM ARP Stochastic Markovian Game Model for Packet Forwarding Based ARP Spoofing Attacks Detection

Divya¹,

Christopher²

2019

IJEAT

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Address Resolution Protocol (ARP) spoofing attacks have become the most pivotal attacks in deteriorating the performance of computer networks. The objective of this paper is to develop SM-ARP, Stochastic Markovian game model based ARP spoofing attack detection scheme.Although many recent techniques have been developed to detect and protect against ARP spoofing attacks, the practical challenges has led to ineffective utilization. The major challenge is that the attackers employing ARP spoofing tend to alter the attack strategy at each point and increases the difficulty in detection and security implementations. The packet forwarding relaying is one suchattack strategy which is harder to detect using traditionally proven methodologies. This paper tackles the packet forwarding relay strategy based ARP spoofing attack strategy by using the proposed SM-ARPto eliminate the attack in a practically feasible manner. The proposed model utilizes a stationary Markov model for optimizing the packet forwarding behaviour of the networks. When an ARP spoofing attack is initiated, the SM-ARP model tracks the changes in the packet forwarding patterns through cache table and detects the misbehaviours. As a security measure, these misbehaved nodes are entitled to recovery and repair process to restore the network to stabilized state. Experiments are conducted to evaluate the performance of SM-ARP in an application for student marks management system. The results prove that the proposed SM-ARP model improves the detection of ARP spoofing attacks with accuracy of 88.2% and also reduces the complexity and errors.

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“…A cloud might be restricted to a single firm (private cloud) or be open to different firms (public cloud). Several attacks are being launched on cloud systems using modern tools and software [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Cloud-based email phishing attack using machine and deep learning algorithm

Butt

Amin

Aldabbas

et al. 2022

Complex Intell. Syst.

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Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively.

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Mitigating Address Spoofing Attacks in Hybrid SDN

Cited by 12 publications

References 13 publications

Security Features in a Hybrid Software-Defined Network

Security Features in a Hybrid Software-Defined Network

SM ARP Stochastic Markovian Game Model for Packet Forwarding Based ARP Spoofing Attacks Detection

Cloud-based email phishing attack using machine and deep learning algorithm

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