VIRTUAL PLC PLATFORM FOR SECURITY AND FORENSICS OF INDUSTRIAL CONTROL SYSTEMS

Industrial Control Systems (ICS) are vital in managing critical infrastructures, including nuclear power plants and electric grids. With the advent of the Industrial Internet of Things (IIoT), these systems have been integrated into broader networks, enhancing efficiency but also becoming targets for cyberattacks. Central to ICS are Programmable Logic Controllers (PLCs), which bridge the physical and cyber worlds and are often exploited by attackers. There\u27s a critical need for tools to analyze cyberattacks on PLCs, uncover vulnerabilities, and improve ICS security. Existing tools are hindered by the proprietary nature of PLC software, limiting scalability and efficiency. To overcome these challenges, I developed a Virtual PLC Platform (VPP) for forensic analyses of ICS attacks and vulnerability identification. The VPP employs the packet replay technique, using network traffic to create a PLC template. This template guides the virtual PLC in network communication, mimicking real PLCs. A Protocol Reverse Engineering Engine (PREE) module assists in reverse-engineering ICS protocols and discovering vulnerabilities. The VPP is automated, supporting PLCs from various vendors, and eliminates manual reverse engineering. This dissertation highlights the architecture and applications of the VPP in forensic analysis, reverse engineering, vulnerability discovery, and threat intelligence gathering, all crucial to bolstering the security and integrity of critical infrastructure

A Survey on Industrial Control System Testbeds and Datasets for Security Research

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-by-design concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community. In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs

A Blockchain-Based Mutual Authentication Method to Secure the Electric Vehicles’ TPMS

Despite the widespread use of Radio Frequency Identification (RFID) and wireless connectivity such as Near Field Communication (NFC) in electric vehicles, their security and privacy implications in Ad-Hoc networks have not been well explored. This paper provides a data protection assessment of radio frequency electronic system in the Tire Pressure Monitoring System (TPMS). It is demonstrated that eavesdropping is completely feasible from a passing car, at an approximate distance up to 50 meters. Furthermore, our reverse analysis shows that the static n -bit signatures and messaging can be eavesdropped from a relatively far distance, raising privacy concerns as a vehicles' movements can be tracked by using the unique IDs of tire pressure sensors. Unfortunately, current protocols do not use authentication, and automobile technologies hardly follow routine message confirmation so sensor messages may be spoofed remotely. To improve the security of TPMS, we suggest a novel ultra-lightweight mutual authentication for the TPMS registry process in the automotive network. Our experimental results confirm the effectiveness and security of the proposed method in TPMS.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

RADIC Voice Authentication: Replay Attack Detection using Image Classification for Voice Authentication Systems

Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks. This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay attacks

Security in 5G-Enabled Internet of Things Communication: Issues: Challenges, and Future Research Roadmap

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work

Detecting Impersonation Attacks in a Static WSN

The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks. A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device. The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system