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Welk, A., Zielinska, O., Tembe, R., Xe, G., Hong, K. W., Murphy-Hill, E., Mayhorn, C. B..  In Press.  Will the “Phisher-men” Reel you in? Assessing Individual Differences in a Phishing Detection Task International Journal of Cyber Behavior, Psychology, and Learning. .

Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

Carrozzo, G., Siddiqui, M. S., Betzler, A., Bonnet, J., Perez, G. M., Ramos, A., Subramanya, T..  2020.  AI-driven Zero-touch Operations, Security and Trust in Multi-operator 5G Networks: a Conceptual Architecture. 2020 European Conference on Networks and Communications (EuCNC). :254—258.
The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.
Shimmi, Samiha S., Dorai, Gokila, Karabiyik, Umit, Aggarwal, Sudhir.  2020.  Analysis of iOS SQLite Schema Evolution for Updating Forensic Data Extraction Tools. 2020 8th International Symposium on Digital Forensics and Security (ISDFS). :1—7.
Files in the backup of iOS devices can be a potential source of evidentiary data. Particularly, the iOS backup (obtained through a logical acquisition technique) is widely used by many forensic tools to sift through the data. A significant challenge faced by several forensic tool developers is the changes in the data organization of the iOS backup. This is due to the fact that the iOS operating system is frequently updated by Apple Inc. Many iOS application developers release periodical updates to iOS mobile applications. Both these reasons can cause significant changes in the way user data gets stored in the iOS backup files. Moreover, approximately once every couple years, there could be a major iOS release which can cause the reorganization of files and folders in the iOS backup. Directories in the iOS backup contain SQLite databases, plist files, XML files, text files, and media files. Android/iOS devices generally use SQLite databases since it is a lightweight database. Our focus in this paper is to analyze the SQLite schema evolution specific to iOS and assist forensic tool developers in keeping their tools compatible with the latest iOS version. Our recommendations for updating the forensic data extraction tools is based on the observation of schema changes found in successive iOS versions.
Zanin, M., Menasalvas, E., González, A. Rodriguez, Smrz, P..  2020.  An Analytics Toolbox for Cyber-Physical Systems Data Analysis: Requirements and Challenges. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :271–276.
The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.
Behera, S., Prathuri, J. R..  2020.  Application of Homomorphic Encryption in Machine Learning. 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–2.
The linear regression is a machine learning algorithm used for prediction. But if the input data is in plaintext form then there is a high probability that the sensitive information will get leaked. To overcome this, here we are proposing a method where the input data is encrypted using Homomorphic encryption. The machine learning algorithm can be used on this encrypted data for prediction while maintaining the privacy and secrecy of the sensitive data. The output from this model will be an encrypted result. This encrypted result will be decrypted using a Homomorphic decryption technique to get the plain text. To determine the accuracy of our result, we will compare it with the result obtained after applying the linear regression algorithm on the plain text.
Korolev, D., Frolov, A., Babalova, I..  2020.  Classification of Websites Based on the Content and Features of Sites in Onion Space. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1680—1683.
This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.
Hossain, Md. Sajjad, Bushra Islam, Fabliha, Ifeanyi Nwakanma, Cosmas, Min Lee, Jae, Kim, Dong-Seong.  2020.  Decentralized Latency-aware Edge Node Grouping with Fault Tolerance for Internet of Battlefield Things. 2020 International Conference on Information and Communication Technology Convergence (ICTC). :420–423.
In this paper, our objective is to focus on the recent trend of military fields where they brought Internet of Things (IoT) to have better impact on the battlefield by improving the effectiveness and this is called Internet of Battlefield Things(IoBT). Due to the requirements of high computing capability and minimum response time with minimum fault tolerance this paper proposed a decentralized IoBT architecture. The proposed method can increase the reliability in the battlefield environment by searching the reliable nodes among all the edge nodes in the environment, and by adding the fault tolerance in the edge nodes will increase the effectiveness of overall battlefield scenario. This suggested fault tolerance approach is worth for decentralized mode to handle the issue of latency requirements and maintaining the task reliability of the battlefield. Our experimental results ensure the effectiveness of the proposed approach as well as enjoy the requirements of latency-aware military field while ensuring the overall reliability of the network.
Rana, M. S., Sung, A. H..  2020.  DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :70—75.
Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.
Sendhil, R., Amuthan, A..  2020.  A Descriptive Study on Homomorphic Encryption Schemes for Enhancing Security in Fog Computing. 2020 International Conference on Smart Electronics and Communication (ICOSEC). :738–743.
Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.
Patil, A. P., Karkal, G., Wadhwa, J., Sawood, M., Reddy, K. Dhanush.  2020.  Design and Implementation of a Consensus Algorithm to build Zero Trust Model. 2020 IEEE 17th India Council International Conference (INDICON). :1—5.
Zero Trust Model ensures each node is responsible for the approval of the transaction before it gets committed. The data owners can track their data while it’s shared amongst the various data custodians ensuring data security. The consensus algorithm enables the users to trust the network as malicious nodes fail to get approval from all nodes, thereby causing the transaction to be aborted. The use case chosen to demonstrate the proposed consensus algorithm is the college placement system. The algorithm has been extended to implement a diversified, decentralized, automated placement system, wherein the data owner i.e. the student, maintains an immutable certificate vault and the student’s data has been validated by a verifier network i.e. the academic department and placement department. The data transfer from student to companies is recorded as transactions in the distributed ledger or blockchain allowing the data to be tracked by the student.
Wang, Guodong, Tian, Dongbo, Gu, Fengqiang, Li, Jia, Lu, Yang.  2020.  Design of Terminal Security Access Scheme based on Trusted Computing in Ubiquitous Electric Internet of Things. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 9:188–192.
In the Ubiquitous Electric Internet of Things (UEIoT), the terminals are very easy to be accessed and attacked by attackers due to the lack of effective monitoring and safe isolation methods. Therefore, in the implementation of UEIoT, the security protection of terminals is particularly important. Therefore, this paper proposes a dual-system design scheme for terminal active immunity based on trusted computing. In this scheme, the terminal node in UEIoT is composed of two parts: computing part and trusted protection part. The computing component and the trusted protection component are logically independent of each other, forming a trusted computing active immune dual-system structure with both computing and protection functions. The Trusted Network Connection extends the trusted state of the terminal to the network, thus providing a solution for terminal secure access in the UEIoT.
Hallaji, Ehsan, Razavi-Far, Roozbeh, Saif, Mehrdad.  2020.  Detection of Malicious SCADA Communications via Multi-Subspace Feature Selection. 2020 International Joint Conference on Neural Networks (IJCNN). :1—8.
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.
Morales-Caporal, Roberto, Reyes-Galaviz, Adrián S., Federico Casco-Vásquez, J., Martínez-Hernández, Haydee P..  2020.  Development and Implementation of a Relay Switch Based on WiFi Technology. 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). :1—6.
This article presents the design and development of a relay switch (RS) to handle electrical loads up to 20A using WiFi technology. The hardware design and the implementation methodology are explained, both for the power supply and for the wireless communication that are embedded in the same small printed circuit board. In the same way, the design of the implemented firmware to operate the developed RS is shown. An ESP-12E module is used to achieve wireless communication of the RS, which can be manipulated through a web page using an MQTT protocol or via and iOS or Arduino app. The developed RS presents at least three differentiators in relation to other similar devices on the market: it can handle a higher electrical load, has a design in accordance with national and international security standards and can use different cybersecurity strategies for wireless communication with the purpose of safe and reliable use. Experimental results using a lamp and a single-phase motor as electrical loads demonstrate an excellent performance and reliability of the developed relay switch.
Nan, Satyaki, Brahma, Swastik, Kamhoua, Charles A., Njilla, Laurent L..  2020.  On Development of a Game‐Theoretic Model for Deception‐Based Security. Modeling and Design of Secure Internet of Things. :123–140.
This chapter presents a game‐theoretic model to analyze attack–defense scenarios that use fake nodes (computing devices) for deception under consideration of the system deploying defense resources to protect individual nodes in a cost‐effective manner. The developed model has important applications in the Internet of Battlefield Things (IoBT). Our game‐theoretic model illustrates how the concept of the Nash equilibrium can be used by the defender to intelligently choose which nodes should be used for performing a computation task while deceiving the attacker into expending resources for attacking fake nodes. Our model considers the fact that defense resources may become compromised under an attack and suggests that the defender, in a probabilistic manner, may utilize unprotected nodes for performing a computation while the attacker is deceived into attacking a node with defense resources installed. The chapter also presents a deception‐based strategy to protect a target node that can be accessed via a tree network. Numerical results provide insights into the strategic deception techniques presented in this chapter.
Cordeiro, Renato, Gajaria, Dhruv, Limaye, Ankur, Adegbija, Tosiron, Karimian, Nima, Tehranipoor, Fatemeh.  2020.  ECG-Based Authentication Using Timing-Aware Domain-Specific Architecture. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:3373–3384.
Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.
Younus, M. A., Hasan, T. M..  2020.  Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform. 2020 International Conference on Computer Science and Software Engineering (CSASE). :186—190.
DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.
Aljedaani, Bakheet, Ahmad, Aakash, Zahedi, Mansooreh, Babar, M. Ali.  2020.  An Empirical Study on Developing Secure Mobile Health Apps: The Developers' Perspective. 2020 27th Asia-Pacific Software Engineering Conference (APSEC). :208—217.
Mobile apps exploit embedded sensors and wireless connectivity of a device to empower users with portable computations, context-aware communication, and enhanced interaction. Specifically, mobile health apps (mHealth apps for short) are becoming integral part of mobile and pervasive computing to improve the availability and quality of healthcare services. Despite the offered benefits, mHealth apps face a critical challenge, i.e., security of health-critical data that is produced and consumed by the app. Several studies have revealed that security specific issues of mHealth apps have not been adequately addressed. The objectives of this study are to empirically (a) investigate the challenges that hinder development of secure mHealth apps, (b) identify practices to develop secure apps, and (c) explore motivating factors that influence secure development. We conducted this study by collecting responses of 97 developers from 25 countries - across 06 continents - working in diverse teams and roles to develop mHealth apps for Android, iOS, and Windows platform. Qualitative analysis of the survey data is based on (i) 8 critical challenges, (ii) taxonomy of best practices to ensure security, and (iii) 6 motivating factors that impact secure mHealth apps. This research provides empirical evidence as practitioners' view and guidelines to develop emerging and next generation of secure mHealth apps.
Ghaleb, Taher Ahmed, Aljasser, Khalid, AlTurki, Musab A..  2020.  Enhanced Visualization of Method Invocations by Extending Reverse-Engineered Sequence Diagrams. 2020 Working Conference on Software Visualization (VISSOFT). :49–60.
Mehraj, S., Banday, M. T..  2020.  Establishing a Zero Trust Strategy in Cloud Computing Environment. 2020 International Conference on Computer Communication and Informatics (ICCCI). :1—6.
The increased use of cloud services and its various security and privacy challenges such as identity theft, data breach, data integrity and data confidentiality has made trust management, which is one of the most multifaceted aspect in cloud computing, inevitable. The growing reputation of cloud computing technology makes it immensely important to be acquainted with the meaning of trust in the cloud, as well as identify how the customer and the cloud service providers establish that trust. The traditional trust management mechanisms represent a static trust relationship which falls deficit while meeting up the dynamic requirement of cloud services. In this paper, a conceptual zero trust strategy for the cloud environment has been proposed. The model offers a conceptual typology of perceptions and philosophies for establishing trust in cloud services. Further, importance of trust establishment and challenges of trust in cloud computing have also been explored and discussed.
Hynes, E., Flynn, R., Lee, B., Murray, N..  2020.  An Evaluation of Lower Facial Micro Expressions as an Implicit QoE Metric for an Augmented Reality Procedure Assistance Application. 2020 31st Irish Signals and Systems Conference (ISSC). :1–6.
Augmented reality (AR) has been identified as a key technology to enhance worker utility in the context of increasing automation of repeatable procedures. AR can achieve this by assisting the user in performing complex and frequently changing procedures. Crucial to the success of procedure assistance AR applications is user acceptability, which can be measured by user quality of experience (QoE). An active research topic in QoE is the identification of implicit metrics that can be used to continuously infer user QoE during a multimedia experience. A user's QoE is linked to their affective state. Affective state is reflected in facial expressions. Emotions shown in micro facial expressions resemble those expressed in normal expressions but are distinguished from them by their brief duration. The novelty of this work lies in the evaluation of micro facial expressions as a continuous QoE metric by means of correlation analysis to the more traditional and accepted post-experience self-reporting. In this work, an optimal Rubik's Cube solver AR application was used as a proof of concept for complex procedure assistance. This was compared with a paper-based procedure assistance control. QoE expressed by affect in normal and micro facial expressions was evaluated through correlation analysis with post-experience reports. The results show that the AR application yielded higher task success rates and shorter task durations. Micro facial expressions reflecting disgust correlated moderately to the questionnaire responses for instruction disinterest in the AR application.
Kotra, A., Eldosouky, A., Sengupta, S..  2020.  Every Anonymization Begins with k: A Game-Theoretic Approach for Optimized k Selection in k-Anonymization. 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE). :1–6.
Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.
Moormann, L., Mortel-Fronczak, J. M. van de, Fokkink, W. J., Rooda, J. E..  2020.  Exploiting Symmetry in Dependency Graphs for Model Reduction in Supervisor Synthesis. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). :659–666.
Supervisor synthesis enables the design of supervisory controllers for large cyber-physical systems, with high guarantees for functionality and safety. The complexity of the synthesis problem, however, increases exponentially with the number of system components in the cyber-physical system and the number of models of this system, often resulting in lengthy or even unsolvable synthesis procedures. In this paper, a new method is proposed for reducing the model of the system before synthesis to decrease the required computational time and effort. The method consists of three steps for model reduction, that are mainly based on symmetry in dependency graphs of the system. Dependency graphs visualize the components in the system and the relations between these components. The proposed method is applied in a case study on the design of a supervisory controller for a road tunnel. In this case study, the model reduction steps are described, and results are shown on the effectiveness of model reduction in terms of model size and synthesis time.
Nguyen, H. M., Derakhshani, R..  2020.  Eyebrow Recognition for Identifying Deepfake Videos. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.
Maunero, Nicoló, Prinetto, Paolo, Roascio, Gianluca, Varriale, Antonio.  2020.  A FPGA-based Control-Flow Integrity Solution for Securing Bare-Metal Embedded Systems. 2020 15th Design Technology of Integrated Systems in Nanoscale Era (DTIS). :1–10.
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).
Lakhdhar, Y., Rekhis, S., Sabir, E..  2020.  A Game Theoretic Approach For Deploying Forensic Ready Systems. 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). :1–6.
Cyber incidents are occurring every day using various attack strategies. Deploying security solutions with strong configurations will reduce the attack surface and improve the forensic readiness, but will increase the security overhead and cost. In contrast, using moderate or low security configurations will reduce that overhead, but will inevitably decrease the investigation readiness. To avoid the use of cost-prohibitive approaches in developing forensic-ready systems, we present in this paper a game theoretic approach for deploying an investigation-ready infrastructure. The proposed game is a non-cooperative two-player game between an adaptive cyber defender that uses a cognitive security solution to increase the investigation readiness and reduce the attackers' untraceability, and a cyber attacker that wants to execute non-provable attacks with a low cost. The cognitive security solution takes its strategic decision, mainly based on its ability to make forensic experts able to differentiate between provable identifiable, provable non-identifiable, and non-provable attack scenarios, starting from the expected evidences to be generated. We study the behavior of the two strategic players, looking for a mixed Nash equilibrium during competition and computing the probabilities of attacking and defending. A simulation is conducted to prove the efficiency of the proposed model in terms of the mean percentage of gained security cost, the number of stepping stones that an attacker creates and the rate of defender false decisions compared to two different approaches.