Introduction

Security practitioners develop ways to detect cyber attacks that are of potential risk to Internet users. This is to secure computer system vulnerabilities, provide alerts to the community, as well as to learn on ‘the how to’ of such an attack occurring. One of the ways in detecting such malicious attack, is developing a luring agent that acts as a dummy for such attack. This dummy agent is known as the Honeypot.

CyberSecurity Malaysia through MyCERT, established a Honeynet project which is a collection of distributed honeypots to study on how exploits function as well as to collect malware binaries. Honeypot is a computer software mechanism setup to mimic a legitimate site to lure malicious software into believing the system is a legitimate site, vulnerable for attacks. Honeypot allow researchers to detect, monitor and counterattack malicious activity by understanding the activities during intrusion phase and from the payload attack.

Mid 2007, a major project overhaul of the Honeypot took place under the Cyber Early Warning System (CEWS) project and was known as LebahNET mini. In 2015, as more resources were invested in the project, it then successfully implemented a lightweight and passive honeypot at identified strategic locations. MyCERT Honeynet initiative later was changed to LebahNET 2.0, a Honeypot Based Distributed System for detecting and capturing attacks that evades traditional security devices. This allowed for vulnerabilities emulation of operating systems used in an enterprise to alert security administrator on source of attacks at LebahNET 2.0 sensors deployed by CyberSecurity Malaysia.

Objective

The aim of LebahNET 2.0 project is to provide valuable supporting information such as network trends and malicious activities for MyCERT incident handling and advisory activities. LebahNET 2.0 also serves as a research network for analysts to experiment with relevant security tools and techniques.

Components

LebahNET Sensor consists of 3 components for service emulations.

i. Glastopf – Web Application Honeypot

Glastopf is a Python web application honeypot implemented to discover attacks that are based upon vulnerability type emulation rather than vulnerability emulation. This means that Glastopf will determine and handle attacks based on type emulation to be ahead of the attackers.

ii. Cowrie– SSH and Telnet Honeypot

Cowrie is a medium-interaction SSH honeypot written in Python to log brute force attacks and the entire shell interaction performed by an attacker.

iii. Dionaea – Samba, MySQL, MSSQL, FTP Honeypot

Dionaea features a modular architecture, embedding Python as its scripting language in order to emulate protocols. It is able to detect shellcodes using LibEmu and supports IPv6 and TLS. Dionaea aims to trap malware-exploiting vulnerabilities exposed through network services in order to ultimately obtain a copy of the malware.

Q3 Statistics

Since March 2015, LebahNET 2.0 sensors received about 9,882,116 total attacks from about 212 countries. Threats origin mainly came from countries in United States and China while targeted attacks were more focused to SSH and Samba servers respectively. It was also observed that about 2,010 Unique Malware were used to perform that attacks.

 

The above Graph 1 shows the percentage of threat originating from the mentioned countries. Among the countries with the highest significant attacks were from United States(2,268,628), Russia(928,141),China(818,061), Ethiopia(633,588), United Kingdom(539,452) France(380,130), Vietnam(314,859), Malaysia(283,572), Taiwan(234,428) and total of other countries origin (2,114,412 ).

 

Graph 2 shows the percentage attack at targeted service in computer system. Through LebahNET 2.0, it was found that the highest targeted attacks in descending order were performed to SSH Server (3,239,426), Samba Server (1,484,243), uPnP(1,291,475) TFTP Server Handler(1277548), Web Server(1,121,928), Telnet Server (846055) and FTP Server.

 

The above Graph 3 meanwhile shows the monthly trend of attacks at system service since March 2016 up until October 2017. For 3rd quarter 2017, targeted service attack of UPNP and Samba Server was found to spike unusually. This may be due to new sensors deployment at institution with high network activity.

As for the latest trend in Quarter 3 2017, MyCERT sensors received about 3,250674 total attacks. It was found that significant increase of targeted attacks were directed to uPnP server (1,211,993), followed by Web Server (660,680),Samba Server (652,826), SSH Server (645,783) MySQL Server (53,667), MsSQL Server (24,031) and Ftp server (1524). Out of the attacks, a total number of 2010 unique malware were captured by LebahNET 2.0 sensors.

Compared to Quarter 2 of 2017, it was observed that there was a 230.02% increased of targeted service in Quarter 3 that can be inclined to the uPnP Server attacks. Graph 4.1 and Graph 4.2 below shows the percentage of targeted services attack for Quarter 2 and Quarter 3 2017.

 

Statistic Significant

The significant values of the statistic assist MyCERT to identify current trends of malware attacks within an organization. It will also allow for Researchers and Cyber Security Experts to forecast new emerging type of attacks that might be created in future cyber attacks. It also acts as a platform to ensure the capability of detecting threats within Malaysia thus making CyberSecurity Malaysia being a significant value to nation. Improvement also made from time to time to by supporting more network services and adding more vulnerabilities to the sensor to ensure more data can be collected.

Conclusion

LebahNET 2.0 developed by MyCERT assist the team members to identify the type of cyber attacks that are operating within the network of each organization the sensors are deployed. Identification of cyber threat trends within the cyber landscape will therefore allow MyCERT to alert and advise cyber threats issues pertaining to its constituency in order to mitigate successful cyber attacks in Malaysia.