Historically, machine learning in computer security has prioritized defense: think intrusion detection systems, malware classification, and botnet traffic identification. Experimental results show that our approach can detect fake access points with an accuracy of 99% and 99.7% at an average of 24.64 and 7.78 milliseconds in open and closed networks, respectively. As a departure from existing works, our solution does not require root access for detection, and it is developed for portability and better performance. We present the detection in three networks, (i) open networks, (ii) closed networks and (iii) networks with captive portals.

Our approach considers the difference in security information and signal level of an access point (AP). This paper presents an Android-based client-side solution to detect the presence of fake access points in a perimeter using details collected from probe responses. Some approaches on Android-based devices demand root privilege, which is highly discouraged. Current approaches to detect spoofing attacks focus on personal computers and rely on the network hosts’ capacity, leaving guest users with mobile devices at risk. More unfortunately, mobile devices have weak security measures due to their limited computational powers. The trends pose a huge threat to users since they become the prime target of attackers. Similarly, there is a rapid growth of numbers in mobile devices in the working environments and on most official occasions. The trend is going high due to an increase in Internet users. Network spoofing is becoming a common attack in wireless networks. Using stock and flow diagrams, we build a simulation model on Stella platform, analyze it and suggest possible solutions that business enterprises can take to implement measures through policy change. Causal loops are used to model the relationships of key parameters that contribute to cyber-attacks through social engineering through the patterns and behavior of social media users. We used analytical method to identify the key factors and parameters that cause social engineering attacks in businesses. In this paper we identify the dynamic pattern and behavior of social engineering both from within and without the business enterprises. Others are still trapped in the social engineering racket. Enterprises that have adopted the use of social media and emails to supplement on their marketing to complete business deals have been attacked or have detected attempted attacks on their infrastructure.

The pervasive use of social media in small and medium enterprises has increased social engineering cyber-attacks.

Kali social engineering toolkit