Biblio

Recent cyberattacks targeting Supervisory Control and Data Acquisition (SCADA)/Industrial Control System(ICS) exploit weaknesses of host system software environment and take over the control of host processes in the host of the station network. We analyze the attack path of these attacks, which features how the attack hijacks the host in the network and compromises the operations of field device controllers. The paper proposes a host-based protection method, which can prevent malware penetration into the process memory by code injection attacks. The method consists of two protection schemes. One is to prevent file-based code injection such as DLL injection. The other is to prevent fileless code injection. The method traces changes in memory regions and determine whether the newly allocated memory is written with malicious codes. For this method, we show how a machine learning method can be adopted.

Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.

This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pre-trained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware's executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. Signatures generated using TrustSign well represent the real malware behavior during runtime. By leveraging the cloud's virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure. Additionally, by removing the dependency on the malware's executable, our method is capable of signing fileless malware. Thus, we focus our research on in-browser cryptojacking attacks, which current antivirus solutions have difficulty to detect. However, TrustSign is not limited to cryptojacking attacks, as our evaluation included various ransomware samples. TrustSign's signature generation process does not require feature engineering or any additional model training, and it is done in a completely unsupervised manner, obviating the need for a human expert. Therefore, our method has the advantage of dramatically reducing signature generation and distribution time. The results of our experimental evaluation demonstrate TrustSign's ability to generate signatures invariant to the process state over time. By using the signatures generated by TrustSign as input for various supervised classifiers, we achieved 99.5% classification accuracy.

Advanced persistent threat (APT) has been considered globally as a serious social problem since the 2010s. Adversaries of this threat, at first, try to penetrate into targeting organizations by using a backdoor which is opened with drive-by-download attacks, malicious e-mail attachments, etc. After adversaries' intruding, they usually execute benign applications (e.g, OS built-in commands, management tools published by OS vendors, etc.) for investigating networks of targeting organizations. Therefore, if they penetrate into networks once, it is difficult to rapidly detect these malicious activities only by using anti-virus software or network-based intrusion systems. Meanwhile, enterprise networks are managed well in general. That means network administrators have a good grasp of installed applications and routinely used applications for employees' daily works. Thereby, in order to find anomaly behaviors on well-managed networks, it is effective to observe changes executing their applications. In this paper, we propose a lightweight host-based intrusion detection system by using process generation patterns. Our system periodically collects lists of active processes from each host, then the system constructs process trees from the lists. In addition, the system detects anomaly processes from the process trees considering parent-child relationships, execution sequences and lifetime of processes. Moreover, we evaluated the system in our organization. The system collected 2, 403, 230 process paths in total from 498 hosts for two months, then the system could extract 38 anomaly processes. Among them, one PowerShell process was also detected by using an anti-virus software running on our organization. Furthermore, our system could filter out the other 18 PowerShell processes, which were used for maintenance of our network.