Biblio

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.

Trust is a fundamental factor in how users engage in interactions with Visual Analytics (VA) systems. While the importance of building trust to this end has been pointed out in research, the aspect that trust can also be misplaced is largely ignored in VA so far. This position paper addresses this aspect by putting trust calibration in focus – i.e., the process of aligning the user’s trust with the actual trustworthiness of the VA system. To this end, we present the trust continuum in the context of VA, dissect important trust issues in both VA systems and users, as well as discuss possible approaches that can build and calibrate trust.

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).

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.

Advanced Persistent Threat (APT) is a stealthy, continuous and sophisticated method of network attacks, which can cause serious privacy leakage and millions of dollars losses. In this paper, we introduce a new game-theoretic framework of the interaction between a defender who uses limited Security Resources(SRs) to harden network and an attacker who adopts a multi-stage plan to attack the network. The game model is derived from Stackelberg games called a Multi-stage Maze Network Game (M2NG) in which the characteristics of APT are fully considered. The possible plans of the attacker are compactly represented using attack graphs(AGs), but the compact representation of the attacker's strategies presents a computational challenge and reaching the Nash Equilibrium(NE) is NP-hard. We present a method that first translates AGs into Markov Decision Process(MDP) and then achieves the optimal SRs allocation using the policy hill-climbing(PHC) algorithm. Finally, we present an empirical evaluation of the model and analyze the scalability and sensitivity of the algorithm. Simulation results exhibit that our proposed reinforcement learning-based SRs allocation is feasible and efficient.

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.

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.

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.

In order to solve the problems of unreasonable fusion results of high conflict evidence and ineffectiveness of coefficient weighting in classical evidence theory, a method of evidence fusion based on evidence trust degree and exponential weighting is proposed. Firstly, the fusion factor is constructed based on probability distribution function and evidence trust degree, then the fusion factor is exponentially weighted by evidence weight, and then the evidence fusion rule based on fusion factor is constructed. The results show that this method can effectively solve the problems of unreasonable fusion results of high conflict evidence and ineffectiveness of coefficient weighting. It shows that the new fusion method are more reasonable, which provides a new idea and method for solving the problems in evidence theory.

Experimentation focused on assessing the value of complex visualisation approaches when compared with alternative methods for data analysis is challenging. The interaction between participant prior knowledge and experience, a diverse range of experimental or real-world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant experimentation results. This paper outlines a hybrid approach proposed for experimentation with complex interactive data analysis tools, specifically for computer network traffic analysis. The approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of software performance which is supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis.

Cloud Computing is considered as a business model for providing IT resources as services through the Internet based on pay-as-you-go principle. These IT resources are provided by Cloud Service Providers (CSPs) and requested by Cloud Service Consumers (CSCs). Selecting the proper CSP to deliver services is a critical and strategic process. According to the work in this paper, a framework for trust management in cloud computing has been introduced. The proposed framework consists of five stages; Filtrating, Trusting, Similarity, Ranking and Monitoring. In the Filtrating stage, the existing CSPs in the system will be filtered based on their parameters. The CSPs trust values are calculated in the Trusting stage. Then, the similarity between the CSC requirements and the CSPs data is calculated in the Similarity stage. The ranking of CSPs will be performed in Ranking stage. According to the Monitoring stage, after finishing the service, the CSC sends his feedbacks about the CSP who delivered the service to be used to monitor this CSP. To evaluate the performance of the proposed framework, a comparative study has been done for the Ranking and Monitoring stages using Armor dataset. According to the comparative results it is found that the proposed framework increases the reliability and performance of the cloud environment.

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.

Intelligent Transportation Systems (ITS) play a vital role in the development of smart cities. They enable various road safety and efficiency applications such as optimized traffic management, collision avoidance, and pollution control through the collection and evaluation of traffic data from Road Side Units (RSUs) and connected vehicles in real time. However, these systems are highly vulnerable to data corruption attacks which can seriously influence their decision-making abilities. Traditional attack detection schemes do not account for attackers' sophisticated and evolving strategies and ignore the ITS's constraints on security resources. In this paper, we devise a security game model that allows the defense mechanism deployed in the ITS to optimize the distribution of available resources for attack detection while considering mixed attack strategies, according to which the attacker targets multiple RSUs in a distributed fashion. In our security game, the utility of the ITS is quantified in terms of detection rate, attack damage, and the relevance of the information transmitted by the RSUs. The proposed approach will enable the ITS to mitigate the impact of attacks and increase its resiliency. The results show that our approach reduces the attack impact by at least 20% compared to the one that fairly allocates security resources to RSUs indifferently to attackers' strategies.

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.

There is a need for suitable approaches to trust assessment to cover the problems of human life. Trust assessment for the information communication related to the quality of service (QoS). The server sends data packets to the client(s) according to the trust assessment. The motivation of this paper is designing a proper approach for the trust assessment process. We propose two methods that are based on the fuzzy systems and genetic algorithm. We compare the results of proposed approaches that can guide to select the proper approaches.

Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.

Advancements in the AI field unfold tremendous opportunities for society. Simultaneously, it becomes increasingly important to address emerging ramifications. Thereby, the focus is often set on ethical and safe design forestalling unintentional failures. However, cybersecurity-oriented approaches to AI safety additionally consider instantiations of intentional malice – including unethical malevolent AI design. Recently, an analogous emphasis on malicious actors has been expressed regarding security and safety for virtual reality (VR). In this vein, while the intersection of AI and VR (AIVR) offers a wide array of beneficial cross-fertilization possibilities, it is responsible to anticipate future malicious AIVR design from the onset on given the potential socio-psycho-technological impacts. For a simplified illustration, this paper analyzes the conceivable use case of Generative AI (here deepfake techniques) utilized for disinformation in immersive journalism. In our view, defenses against such future AIVR safety risks related to falsehood in immersive settings should be transdisciplinarily conceived from an immersive co-creation stance. As a first step, we motivate a cybersecurity-oriented procedure to generate defenses via immersive design fictions. Overall, there may be no panacea but updatable transdisciplinary tools including AIVR itself could be used to incrementally defend against malicious actors in AIVR.

A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.

Implementations of Cyber-Physical Systems (CPS), like the Internet of Things, Smart Factories or Smart Grid gain more and more impact in their fields of application, as they extend the functionality and quality of the offered services significantly. However, the coupling of safety-critical embedded systems and services of the cyber-space domain introduce many new challenges for system engineers. Especially, the goal to achieve a high level of security throughout CPS presents a major challenge. However, it is necessary to develop and deploy secure CPS, as vulnerabilities and threats may lead to a non- or maliciously modified functionality of the CPS. This could ultimately cause harm to life of involved actors, or at least sensitive information can be leaked or lost. Therefore, it is essential that system engineers are aware of the level of security of the deployed CPS. For this purpose, security metrics and security evaluation frameworks can be utilized, as they are able to quantitatively express security, based on different measurements and rules. However, existing security scoring solutions may not be able to generate accurate security scores for CPS, as they insufficiently consider the typical CPS characteristics, like the communication of heterogeneous systems of physical- and cyber-space domain in an unpredictable manner. Therefore, we propose a security analysis framework, called Security Qualification Matrix (SQM). The SQM is capable to analyses multiple attacks on a System-of-Systems level simultaneously. With this approach, dependencies, potential side effects and the impact of mitigation concepts can quickly be identified and evaluated.

The emergence of Cyber-Physical Systems (CPSs) is a potential paradigm shift for the usage of Information and Communication Technologies (ICT). From predominantly a facilitator of information and communication services, the role of ICT in the present age has expanded to the management of objects and resources in the physical world. Thus, it is imperative to devise mechanisms to ensure the trustworthiness of data to secure vulnerable devices against security threats. This work presents an analytical framework based on non-cooperative game theory to evaluate the trustworthiness of individual sensor nodes that constitute the CPS. The proposed game-theoretic model captures the factors impacting the trustworthiness of CPS sensor nodes. Further, the model is used to estimate the Nash equilibrium solution of the game, to derive a trust threshold criterion. The trust threshold represents the minimum trust score required to be maintained by individual sensor nodes during CPS operation. Sensor nodes with trust scores below the threshold are potentially malicious and may be removed or isolated to ensure the secure operation of CPS.

The credibility of software systems is an important indicator in measuring the performance of software systems. Effective analysis of the credibility of systems is a controversial topic in the research of trusted software. In this paper, the trusted boot and integrity metrics of a software system are analyzed. The different trust chain models, chain and star, are obtained by using different methods for credibility detection of functional modules in the system operation. Finally, based on the operation of the system, trust and failure relation graphs are established to analyze and measure the credibility of the system.

Due to increased number of devices with limited resources in Internet of Things (IoT) has to serve time sensitive applications including health monitoring, emergency response, industrial applications and smart city etc. This has incurred the problem of solving the provisioning of limited computational resources of the devices to fulfill the requirement with reduced latency. With rapid increase of devices and heterogeneity characteristic the resource provisioning is crucial and leads to conflict of trusting among the devices requests. Trust is essential component in any context for communicating or sharing the resources in the network. The proposed work comprises of trusting and provisioning based on deadline. Trust quantity is measured with concept of game theory and optimal strategy decision among provider and customer and provision resources within deadline to execute the tasks is done by finding Nash equilibrium. Nash equilibrium (NE) is estimated by constructing the payoff matrix with choice of two player strategies. NE is obtained in the proposed work for the Trust- Respond (TR) strategy. The latency aware approach for avoiding resource contention due to limited resources of the edge devices, fog computing leverages the cloud services in a distributed way at the edge of the devices. The communication is established between edge devices-fog-cloud and provision of resources is performed based on scalar chain and Gang Plank theory of management to reduce latency and increase trust quantity. To test the performance of proposed work performance parameter considered are latency and computational time.

Mobile peer-to-peer network (MP2P) attracts increasing attentions due to the ubiquitous use of mobile communication and huge success of peer-to-peer (P2P) mode. However, open p2p mode makes nodes tend to be selfish, and the scarcity of resources in mobile nodes aggravates this problem, thus the nodes easily express a non-complete cooperative (NCC) attitude. Therefore, an identification of non-complete cooperative nodes and a corresponding trust routing scheme are proposed for MP2P in this paper. The concept of octant is firstly introduced to build a trust model which analyzes nodes from three dimensions, namely direct trust, internal state and recommendation reliability, and then the individual non-complete cooperative (INCC) nodes can be identified by the division of different octants. The direct trust monitors nodes' external behaviors, and the consideration of internal state and recommendation reliability contributes to differentiate the subjective and objective non-cooperation, and mitigate the attacks about direct trust values respectively. Thus, the trust model can identify various INCC nodes accurately. On the basis of identification of INCC nodes, cosine similarity method is applied to identify collusive non-complete cooperate (CNCC) nodes. Moreover, a trust routing scheme based on the identification of NCC nodes is presented to reasonably deal with different kinds of NCC nodes. Results from extensive simulation experiments demonstrate that this proposed identification and routing scheme have better performances, in terms of identification precision and packet delivery fraction than current schemes respectively.

In the face of increasingly serious firmware attacks, it is of great significance to analyze the vulnerability security of UEFI. This paper first introduces the commonly used trusted authentication mechanisms of UEFI. Then, aiming at the loopholes in the process of UEFI trust verification in the startup phase, combined with the state transition diagram, PageRank algorithm and Bayesian network theory, the analysis model of UEFI trust verification startup vulnerability is constructed. And according to the example to verify the analysis. Through the verification and analysis of the data obtained, the vulnerable attack paths and key vulnerable nodes are found. Finally, according to the analysis results, security enhancement measures for UEFI are proposed.

In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.