Abstract
Wireless networking is a main part of our daily life during these days, each one wants to be connected. Nevertheless, the massive progress in the Wi-Fi trends and technologies leads most people to give no attention to the security issues. Also detecting a fake access point is a hard security issue over the wireless network. All the currently used methods are either in need of hardware installation, changing the protocol or needs analyzing frames. Moreover, these solutions mainly focus on a single digital attack identification. In this paper, we proposed an admin side way of detection of a not real access point. That works on multiple cyber-attacks especially the phishing attack. We shed the light on detecting WI-phishing or Evil Twin, DE authentication attack, KARMA attack, advanced WI-phishing attack and differentiate them from the normal packets. By performing the frame type analysis in real time and analyzing different static and dynamic parameters as any change in the static features will be considered as an evil twin attack. Also, providing that the value of the dynamic parameters surpasses the threshold, it reflects Evil Twin. The detector has been tested experimentally and it reflects average accuracy of 94.40%, 87.08% average precision and an average specificity of 96.39% for the five types of attack.
Highlights
AND BACKGROUNDCurrently, the wireless techniques help users who are using terminals, phones, and tablets to use the internet services, in addition to being integrated in many interfaces and used implementation over the field of (IOT) Internet of Things. [1] Despite the growth of wi-fi technologies, users still do not care for security issues
As WI-phishing [2] or Evil Twin, DE authentication attack [8], and KARMA attack [9] are considered types of phishing, we focus on detecting these types
It helps in detecting WI-phishing [2] or Evil Twin, DE authentication attack, KARMA attack [9], advanced WI-phishing attack and differentiate them from the normal packets in real time and making a long-timed database that is used for forensics for detecting more sophisticated beacon-based attacks via a python language and SCAPY library
Summary
AND BACKGROUNDCurrently, the wireless techniques help users who are using terminals, phones, and tablets to use the internet services, in addition to being integrated in many interfaces and used implementation over the field of (IOT) Internet of Things. [1] Despite the growth of wi-fi technologies, users still do not care for security issues. As clients used to be online most of the time, this gives a higher availability of being victimized with many of the cyber security attacks. All these communications are done over the channel used for sending or receiving wireless waves in-between the access point (AP) and the user. The attacker is in no need to physically access the victim’s network. While getting benefits from this technology, these vast numbers of non-smart connected cyber-physical devices have several properties that led to critical security issues, such as nodes mobility, wireless communications, lack of local security features, scale, and diversity. IoT botnets attack is considered a critical attack that affects IoT network infrastructure that launches a distributed denial of service (DDoS) [3]
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