Biblio

The code length and rate of length-compatible polar codes can be adaptively adjusted and changed because of the special coding structure. In this paper, we propose a method to construct length-compatible polar codes by employing physical layer encryption technology. The deletion way of frozen bits and generator matrix are random, which makes polar codes more flexible and safe. Simulation analysis shows that the proposed algorithm can not only effectively improve the performance of length-compatible polar codes but also realize the physical layer security encryption of the system.

Context-based adaptive binary arithmetic coding (CABAC) is the only entropy coding method in HEVC. According to statistics, CABAC encoders account for more than 25% of the high efficiency video coding (HEVC) coding time. Therefore, the improved CABAC algorithm can effectively improve the coding speed of HEVC. On this basis, a selective encryption scheme based on the improved CABAC algorithm is proposed. Firstly, the improved CABAC algorithm is used to optimize the regular mode encoding, and then the cryptographic algorithm is used to selectively encrypt the syntax elements in bypass mode encoding. The experimental results show that the encoding time is reduced by nearly 10% when there is great interference to the video information. The scheme is both safe and effective.

The increasing using of IoT technologies in the industrial sector creates new challenges for the information security of such systems. Using IoT-devices for building SCADA systems cause standard protocols and public networks for data transmitting. Commercial off-the-shelf devices and systems are a new base for industrial control systems, which have high-security risks. There are some useful models are exist for security analysis of information systems, but they do not take into account IoT architecture. The nested attributed metagraph model for the security of IoT-based solutions is proposed and discussed.

Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.

An open source and low-cost Supervisory Control and Data Acquisition System based on Node-RED and Arduino microcontrollers is presented in this paper. The system is designed for monitoring, supervision, and remotely controlling motors and sensors deployed for oil and gas facilities. The Internet of Things (IoT) based SCADA system consists of a host computer on which a server is deployed using the Node-RED programming tool and two terminal units connected to it: Arduino Uno and Arduino Mega. The Arduino Uno collects and communicates the data acquired from the temperature, flowrate, and water level sensors to the Node-Red on the computer through the serial port. It also uses a local liquid crystal display (LCD) to display the temperature. Node-RED on the computer retrieves the data from the voltage, current, rotary, accelerometer, and distance sensors through the Arduino Mega. Also, a web-based graphical user interface (GUI) is created using Node-RED and hosted on the local server for parsing the collected data. Finally, an HTTP basic access authentication is implemented using Nginx to control the clients' access from the Internet to the local server and to enhance its security and reliability.

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.

A Supervisory Control and Data Acquisition (SCADA) system used in controlling or monitoring purpose in industrial process automation system is the process of collecting data from instruments and sensors located at remote sites and transmitting data at a central site. Most of the existing works on SCADA system focused on simulation-based study which cannot always mimic the real world situations. We propose a novel methodology that analyzes SCADA logs on offline basis and helps to detect process-related threats. This threat takes place when an attacker performs malicious actions after gaining user access. We conduct our experiments on a real-life SCADA system of a Power transmission utility. Our proposed methodology will automate the analysis of SCADA logs and systemically identify undesired events. Moreover, it will help to analyse process-related threats caused by user activity. Several test study suggest that our approach is powerful in detecting undesired events that might caused by possible malicious occurrence.

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.

Supervisory Control and Data Acquisition (SCADA) systems have been a frequent target of cyberattacks in Industrial Control Systems (ICS). As such systems are a frequent target of highly motivated attackers, researchers often resort to intrusion detection through machine learning techniques to detect new kinds of threats. However, current research initiatives, in general, pursue higher detection accuracies, neglecting the detection of new kind of threats and their proposal detection scope. This paper proposes a novel, reliable host-based intrusion detection for SCADA systems through the Operating System (OS) diversity. Our proposal evaluates, at the OS level, the SCADA communication over time and, opportunistically, detects, and chooses the most appropriate OS to be used in intrusion detection for reliability purposes. Experiments, performed through a variety of SCADA OSs front-end, shows that OS diversity provides higher intrusion detection scope, improving detection accuracy by up to 8 new attack categories. Besides, our proposal can opportunistically detect the most reliable OS that should be used for the current environment behavior, improving by up to 8%, on average, the system accuracy when compared to a single OS approach, in the best case.

With the increase of the information level of SCADA system in recent years, the attacks against SCADA system are also increasing. Therefore, more and more scholars are beginning to study the safety of SCADA systems. Game theory is a balanced decision involving the main body of all parties. In recent years, domestic and foreign scholars have applied game theory to SCADA systems to achieve active defense. However, their research often focuses on the entire SCADA system, and the game theory is solved for the entire SCADA system, which is not flexible enough, and the calculation cost is also high. In this paper, a dynamic local game model (DLGM) for power SCADA system is proposed. This model first obtains normal data to form a whitelist, then dynamically detects each attack of the attacker's SCADA system, and through white list to determine the node location of the SCADA system attacked by the attacker, then obtains the smallest system attacked by SCADA system, and finally performs a local dynamic game algorithm to find the best defense path. Experiments show that DLGM model can find the best defense path more effectively than other game strategies.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

This paper shows how the Xtensa architecture can be attacked with Return-Oriented-Programming (ROP). The presented techniques include possibilities for both supported Application Binary Interfaces (ABIs). Especially for the windowed ABI a powerful mechanism is presented that not only allows to jump to gadgets but also to manipulate registers without relying on specific gadgets. This paper purely focuses on how the properties of the architecture itself can be exploited to chain gadgets and not on specific attacks or a gadget catalog.

Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension for MCUs, is an emerging security technique fortifying MCU based IoT devices. This paper presents the first security analysis of potential software security issues in TrustZone-M enabled MCUs. We explore the stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against Non-secure Callable (NSC) functions in the context of TrustZone-M. We validate these attacks using the Microchip SAM L11 MCU, which uses the ARM Cortex-M23 processor with the TrustZone-M technology. Strategies to mitigate these software attacks are also discussed.

Return-oriented programming (ROP) is an effective code-reuse attack in which short code sequences (i.e., gadgets) ending in a ret instruction are found within existing binaries and then executed by taking control of the call stack. The shadow stack, control flow integrity (CFI) and code (re)randomization are three popular techniques for protecting programs against return address overwrites. However, existing runtime rerandomization techniques operate on concrete return addresses, requiring expensive pointer tracking. By adding one level of indirection, we introduce BarRA, the first shadow stack mechanism that applies continuous runtime rerandomization to abstract return addresses for protecting their corresponding concrete return addresses (protected also by CFI), thus avoiding expensive pointer tracking. As a nice side-effect, BarRA naturally combines the shadow stack, CFI and runtime rerandomization in the same framework. The key novelty of BarRA, however, is that once some abstract return addresses are leaked, BarRA will enforce the burn-after-reading property by rerandomizing the mapping from the abstract to the concrete return address space in the order of microseconds instead of seconds required for rerandomizing a concrete return address space. As a result, BarRA can be used as a superior replacement for the shadow stack, as demonstrated by comparing both using the 19 C/C++ benchmarks in SPEC CPU2006 (totalling 2,047,447 LOC) and analyzing a proof-of-concept attack, provided that we can tolerate some slight binary code size increases (by an average of 29.44%) and are willing to use 8MB of dedicated memory for holding up to 220 return addresses (on a 64-bit platform). Under an information leakage attack (for some return addresses), the shadow stack is always vulnerable but BarRA is significantly more resilient (by reducing an attacker's success rate to [1/(220)] on average). In terms of the average performance overhead introduced, both are comparable: 6.09% (BarRA) vs. 5.38% (the shadow stack).

The sophistication and complexity of recent exploitation techniques, which rely on memory disclosure and whole-function reuse to bypass address space layout randomization and control flow integrity, is indicative of the effect that the combination of exploit mitigations has in challenging the construction of reliable exploits. In addition to software diversification and control flow enforcement, recent efforts have focused on the complementary approach of code and API specialization to restrict further the critical operations that an attacker can perform as part of a code reuse exploit. In this paper we propose Saffire, a compiler-level defense against code reuse attacks. For each calling context of a critical function, Saffire creates a specialized and hardened replica of the function with a restricted interface that can accommodate only that particular invocation. This is achieved by applying staticargumentbinding, to eliminate arguments with static values and concretize them within the function body, and dynamicargumentbinding, which applies a narrow-scope form of data flow integrity to restrict the acceptable values of arguments that cannot be statically derived. We have implemented Saffire on top of LLVM, and applied it to a set of 11 applications, including Nginx, Firefox, and Chrome. The results of our experimental evaluation with a set of 17 real-world ROP exploits and three whole-function reuse exploits demonstrate the effectiveness of Saffire in preventing these attacks while incurring a negligible runtime overhead.

Memory corruption vulnerabilities, mainly present in C and C++ applications, may enable attackers to maliciously take control over the program running on a target machine by forcing it to execute an unintended sequence of instructions present in memory. This is the principle of modern Code-Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI-based solutions have been proposed, resorting to both hardware and software implementations. However, many of these solutions are hardly applicable to microcontroller systems, often very resource-limited. The paper presents a generic, portable, and lightweight CFI solution for bare-metal embedded systems, i.e., systems that execute firmware directly from their Flash memory, without any Operating System. The proposed defense mixes software and hardware instrumentation and is based on monitoring the Control-Flow Graph (CFG) with an FPGA connected to the CPU. The solution, applicable in principle to any architecture which disposes of an FPGA, forces all control-flow transfers to be compliant with the CFG, and preserves the execution context from possible corruption when entering unpredictable code such as Interrupt Services Routines (ISR).

Return-Oriented Programming (ROP) is one of the most common techniques to exploit software vulnerabilities. Although many solutions to defend against ROP attacks have been proposed, they still have various drawbacks, such as requiring additional information (source code, debug symbols, etc.), increasing program running cost, and causing program instability. In this paper, we propose a method: using static analysis and binary patch technology to defend against ROP attacks based on return instruction. According to this method, we implemented the AT- ROP tool in a Linux 64-bit system environment. Compared to existing tools, it clears the parameter registers when the function returns. As a result, it makes the binary to defend against ROP attacks based on return instruction without having to obtain the source code of the binary. We use the binary challenges in the CTF competition and the binary programs commonly used in the Linux environment to experiment. It turns out that AT-ROP can make the binary program have the ability to defend against ROP attacks based on return instruction with a small increase in the size of the binary program and without affecting its normal execution.

Code randomization is considered as the basis of mitigation against code reuse attacks, fundamentally supporting some recent proposals such as execute-only memory (XOM) that aims at dynamic return-oriented programming (ROP) attacks. However, existing code randomization methods are hard to achieve a good balance between high-randomization entropy and semantic consistency. In particular, they always ignore code semantic consistency, incurring performance loss and incompatibility with current security schemes, e.g., control flow integrity (CFI). In this paper, we present an enhanced code randomization method termed as HCRESC, which can improve the randomization entropy significantly, meanwhile ensure the semantic consistency between variants and the original code. HCRESC reschedules instructions within the range of functions rather than basic blocks, thus producing more variants of the original code and preserving the code's semantic. We implement HCRESC on Linux platform of x86-64 architecture and demonstrate that HCRESC can increase the randomization entropy of x86-64 code over than 120% compared with existing methods while ensuring control flow and size of the code unaltered.

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today’s challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.

Since remote ages, queues and delays have been a rather exasperating reality of human daily life. Today, they pursue us everywhere: in technical, social, socio-technical, and even control systems, dramatically deteriorating their performance. In this variety, it is the computer systems that are sure to cause the growing anxiety in our digital era. Although for our everyday Internet surfing, experiencing long-lasting and annoying delays is an unpleasant but not dangerous situation, for industrial control systems, especially those dealing with critical infrastructures, such behavior is unacceptable. The article presents a deterministic approach to solving some digital control system problems associated with delays and backlogs. Being based on Network calculus, in contrast to statistical methods of Queuing theory, it provides worst-case results, which are eminently desirable for critical infrastructures. The article covers the basics of a theory of deterministic queuing systems Network calculus, its evolution regarding the relationship between backlog bound and delay, and a technique for handling empirical data. The problems being solved by the deterministic approach: standard calculation of network performance measures, estimation of database maximum updating time, and cybersecurity assessment including such issues as the CIA triad representation, operational technology influence, and availability understanding focusing on its correlation with a delay are thoroughly discussed as well.

Social Internet of Things (SIoT) is an extension of Internet of Things (IoT) that converges with Social networking concepts to create Social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human-to-human (H2H) and thing-to-thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “Social objects” that intelligently mimic the social behavior of human in the daily life. These social objects are equipped with social functionalities capable of discovering other social objects in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this paper, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state of the art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting social objects.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.