A new botnet named HinataBot, which is based on the Golang programming language, has been found exploiting known vulnerabilities to compromise routers and servers for launching distributed denial-of-service (DDoS) attacks. According to a technical report by Akamai, the botnet’s name is inspired by a character from the anime series Naruto, with filenames like “Hinata-<OS>-<Architecture>.” The malware is distributed through the exploitation of exposed Hadoop YARN servers, Realtek SDK devices (CVE-2014-8361), and Huawei HG532 routers (CVE-2017-17215, CVSS score: 8.8).
HinataBot’s threat actors have been active since December 2022, initially using a generic Go-based Mirai variant before developing their custom malware. The malware is still evolving, with newer artifacts found in Akamai’s HTTP and SSH honeypots. HinataBot can contact a command-and-control (C2) server for instructions and initiate attacks on target IP addresses.
The latest version of HinataBot uses HTTP and UDP protocols for DDoS attacks. Tests by Akamai showed that an HTTP flood generated 20,430 HTTP requests, while a UDP flood created 6,733 packets. In a hypothetical real-world attack with 10,000 bots, a UDP flood would peak at over 3.3 terabit per second (Tbps), and an HTTP flood would generate about 27 gigabit per second (Gbps).
The use of Golang in malware, like HinataBot, GoBruteforcer, and KmsdBot, complicates reverse engineering due to its high performance, multi-threading, cross-compilation support, and complexity when compiled. Microsoft’s Azure Network Security Team has highlighted the importance of being proactive and developing a DDoS response strategy as DDoS attacks become more frequent and sophisticated.
Original source: The Hacker News
Malware is a term used to describe any malicious software that is designed to harm or exploit a computer system, network, or user. The impact of malware can be devastating, resulting in the theft of sensitive data, the disruption of essential services, and even the complete destruction of computer systems. As a result, it’s essential to have an effective malware analysis process to identify, isolate, and neutralize any malware that may be present on your systems. In this article, we’ll explore the common methods of malware analysis and how they work.
Static analysis is a type of malware analysis that involves examining the code or file structure of a piece of software without actually running it. This method is often used to detect known malware variants and identify patterns of behavior that are characteristic of malware. Some common techniques used in static analysis include:
- Signature-based analysis: This technique involves comparing the code or file structure of a suspect file with a database of known malware signatures. If a match is found, the file is identified as malware.
- Heuristic analysis: Heuristic analysis involves looking for suspicious behavior patterns in code or file structure that may indicate the presence of malware. For example, if a file has a high level of obfuscation or uses unusual system calls, it may be flagged as suspicious.
- Sandbox analysis: Sandbox analysis involves executing a file in a controlled virtual environment to observe its behavior without risking damage to the host system. This technique can be used to detect hidden or encrypted code, as well as identify command-and-control servers used by malware.
Dynamic analysis is a type of malware analysis that involves running the suspect software in a controlled environment to observe its behavior. This method is often used to detect new or unknown malware variants that may not be identified by static analysis. Some common techniques used in dynamic analysis include:
- Debugging: Debugging involves running a piece of software in a debugger to monitor its behavior and identify any vulnerabilities or malicious behavior.
- Emulation: Emulation involves running a piece of software in an emulator to simulate the behavior of the target system. This technique can be used to detect malware that targets specific operating systems or hardware platforms.
- Memory analysis: Memory analysis involves examining the memory state of a running program to identify any malicious code or behavior. This technique can be used to detect rootkits and other types of malware that attempt to hide their presence from traditional detection methods.
Post-mortem analysis is a type of malware analysis that involves examining a system after it has been compromised by malware. This technique is often used to identify the source of a malware infection and determine the extent of the damage caused. Some common techniques used in post-mortem analysis include:
- Forensic analysis: Forensic analysis involves examining the hard drive, memory, and other system resources to identify the source of a malware infection. This technique can be used to identify the attacker’s IP address, the date and time of the attack, and any other relevant information.
- Incident response: Incident response involves following a set of protocols and procedures to isolate and contain a malware infection. This technique can help to minimize the damage caused by the malware and prevent further infections.
In conclusion, malware analysis is a critical process that can help to identify and mitigate the risks associated with malware infections. Whether you’re a security professional or an everyday computer user, understanding the common methods of malware analysis can help you to protect yourself and your systems from the devastating effects of malware. By combining static and dynamic analysis techniques with post-mortem analysis, you can gain a comprehensive understanding of the nature and behavior of any malware that may be present on your systems, and take the necessary steps to remove it and prevent future infections.