Anomaly Detection by Monitoring Unintended DNS Traffic on Wireless Network Cybersecurity threats from malware attacks has become one of the most serious issues in the Internet nowadays. Most types of malware, after intruding an individual computer, attempt to connect the corresponding Command and Control (Cu0026C) servers using IP addresses or Fully Qualified Domain Name (FQDN) and receive further instructions (e.g. attacking target IP addresses and FQDNs) from them in order to conduct subsequent cyber attacks. In recent years, it has been clarified that DNS traffic has been used for communication between the malware infection computers and the Cu0026C servers. In this research, we focus on these peculiarities and propose a method for detecting malware infected computers by monitoring unintended DNS traffic on wireless networks by collaboration with DHCP (Dynamic Host Configuration Protocol) server. By deploying the proposed system on campus wireless networks, the computers within DHCP configured environment can be detected when they are infected by some types of malware and it attempts to communicate with the corresponding Cu0026C servers using DNS (Domain Name System) protocol. In this paper, we describe the detailed design of the proposed method and the future work includes prototype implementation as well as evaluations.

Discovering Suspicious APT Behaviors by Analyzing DNS Activities As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and realtime evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Preprocessing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.