Biblio

Digitization has been rapidly integrated with manufacturing industries and critical infrastructure to increase efficiency, productivity, and reduce wastefulness, a transition being labeled as Industry 4.0. However, this expansion, coupled with the poor cybersecurity posture of these Industrial Internet of Things (IIoT) devices, has made them prolific targets for exploitation. Moreover, modern Programmable Logic Controllers (PLC) used in the Operational Technology (OT) sector are adopting open-source operating systems such as Linux instead of proprietary software, making such devices susceptible to Linux-based malware. Traditional malware detection approaches cannot be applied directly or extended to such environments due to the unique restrictions of these PLC devices, such as limited computational power and real-time requirements. In this paper, we propose ORRIS, a novel lightweight and out-of-the-device framework that detects malware at both kernel and user-level by processing the information collected using the Joint Test Action Group (JTAG) interface. We evaluate ORRIS against in-the-wild Linux malware achieving maximum detection accuracy of ≈99.7% with very few false-positive occurrences, a result comparable to the state-of-the-art commercial products. Moreover, we also develop and demonstrate a real-time implementation of ORRIS for commercial PLCs.

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

SCADA systems are being constantly migrated to modern information and communication technologies (ICT) -based systems named cyber-physical systems. Unfortunately, this allows attackers to execute exploitation techniques into these architectures. In addition, ransomware insertion is nowadays the most popular attacking vector because it denies the availability of critical files and systems until attackers receive the demanded ransom. In this paper, it is analysed the risk impact of ransomware insertion into SCADA systems and it is suggested countermeasures addressed to the protection of SCADA systems and its components to reduce the impact of ransomware insertion.

Rootkits detecting in the Windows operating system is an important part of information security monitoring and audit system. Methods of hided process detection were analyzed. The software is developed which implements the four methods of hidden process detection in a user mode (PID based method, the descriptor based method, system call based method, opened windows based method) to use in the monitoring and audit systems.

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.

Due to a rapid revaluation in a virtualization environment, Virtual Machines (VMs) are target point for an attacker to gain privileged access of the virtual infrastructure. The Advanced Persistent Threats (APTs) such as malware, rootkit, spyware, etc. are more potent to bypass the existing defense mechanisms designed for VM. To address this issue, Virtual Machine Introspection (VMI) emerged as a promising approach that monitors run state of the VM externally from hypervisor. However, limitation of VMI lies with semantic gap. An open source tool called LibVMI address the semantic gap. Memory Forensic Analysis (MFA) tool such as Volatility can also be used to address the semantic gap. But, it needs to capture a memory dump (RAM) as input. Memory dump acquires time and its analysis time is highly crucial if Intrusion Detection System IDS (IDS) depends on the data supplied by FAM or VMI tool. In this work, live virtual machine RAM dump acquire time of LibVMI is measured. In addition, captured memory dump analysis time consumed by Volatility is measured and compared with other memory analyzer such as Rekall. It is observed through experimental results that, Rekall takes more execution time as compared to Volatility for most of the plugins. Further, Volatility and Rekall are compared with LibVMI. It is noticed that examining the volatile data through LibVMI is faster as it eliminates memory dump acquire time.