Exploitation Of Vulnerabilities Research Articles

Smart contract vulnerability detection based on semantic graph and residual graph convolutional networks with edge attention

Smart contracts are becoming the forefront of blockchain technology, allowing the performance of credible transactions without third parties. However, smart contracts on blockchain are not immune to vulnerability exploitation and cannot be modified after being deployed on the blockchain. Therefore, it is imperative to assure the security of smart contracts via intelligent vulnerability detection tools with the exponential increase in the number of smart contracts. The remarkably developing deep learning technology provides a promising way to detect potential smart contract vulnerabilities. Nevertheless, existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts for vulnerability detection. In this paper, we tackle the problem of smart contract vulnerability detection at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using graph convolutional networks (GCNs) with residual blocks and edge attention. Our proposed method consists of three stages. In the first stage, we create the SG which contains rich syntax and semantic information including the data–data, instruction–instruction and instruction–data relationships, variables, operations, etc., by building an abstract syntax tree (AST) from the code of each function, removing the unimportant nodes in the AST, and adding edges between the nodes to represent the data flows and the execution sequence of the statements. In the second stage, we propose a new graph convolutional network model EA-RGCN to learn the content and semantic features of the code. EA-RGCN contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. In the third stage, we concatenate the code content features and the semantic features to obtain the global code feature and use a classifier to identify whether the function is vulnerable. We conduct experiments on the datasets constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can effectively improve the performance in terms of accuracy, precision, recall, and F1-score on smart contract vulnerability detection.

Trust and betrayal in older adult financial exploitation

Objectives Although the knowledge base regarding the financial exploitation of older adults is expanding, work to understand the subpopulations of older adult financial exploitation victims and their experiences is greatly needed. This study uses betrayal trauma theory (BTT) as the foundation for conceptualizing the harm that arises from elder family financial exploitation. Methods The study uses a cross-sectional design to investigate group differences among a total sample of 95 community-dwelling older adults, 32 of the participants (33.7%) were older adult victims of family financial exploitation and the remaining 63 (66.3%) were victims of financial exploitation perpetrated by strangers. Results The group of older adults who were victims of elder family financial exploitation had significantly lower functional ability scores, higher stress and financial exploitation vulnerability scores and lost more money on average than those victimized by strangers. Conclusion The present study provides support that BTT provides a valuable framework for understanding why older adult family financial exploitation victims are more vulnerable than victims of exploitation committed by strangers. Attention to this subgroup of financially exploited older adults will provide improved understanding of the unique challenges these victims face and inform prevention and intervention services.

Dynamic logic-based attack graph for risk assessment in complex computer systems

Nowadays, Information and Communication Technologies (ICT) play a significant role in our modern daily life. Computer networks breakdown can strongly impact everything in our life such as personal data, industrials, banks, oil pipelines, hospitals, nuclear reactors, military platforms, etc. Assessing their security is a necessity to reduce the risk of compromise by an attacker. Nevertheless, the actual solutions are rarely adapted to the high complexity of modern computer systems. They often rely on too much human work and the used algorithms do not scale well. Furthermore, the evolution of the system over time is rarely modeled and is therefore not considered in the evaluation of its security. This paper proposes a dynamic attack graph generation method allowing to model attack paths by considering the evolution of the system over time. We compute the probabilities of compromise of the system components by simulating several cyberattacks from the previously constructed dynamic attack graph. We tested our solution on a use case of several thousand of machines. The measured results demonstrate its ability to assess the threat in complex systems caused by combining exploitation of successive vulnerabilities.

CYBERSECURITY ASSURANCE IN SMART CITIES: A RISK MANAGEMENT PERSPECTIVE

ABSTRACT Smart cities are interconnected in a complex web of interdependent systems that are critical for functioning of smart services for better living. However, cybersecurity risks in smart cities are a growing concern for smart city councils and governments globally. As more city services are being brought online and connected with other services, the cybersecurity threat surface is also increasing. We experience a surge in exploitation of security vulnerabilities in smart service operations in cities on a global scale. Incidents of cyberattacks bringing down critical smart services like smart grids and other digital infrastructure are now occurring frequently. These cyberattacks are anticipated to increase further on a larger scale if smart city councils do not take a proactive approach toward cybersecurity risk management and assurance. Managing the cyber risks of smart cities is an understudied area of information systems and cybersecurity research domains that is necessary for the development of cyber-resilient societies. This paper attempts to address this gap. It is based on the premise that cybersecurity risks in smart cities cannot be avoided; instead, they must be proactively identified, assessed, and managed. This paper provides an overview of interdependent systems in smart cities, cybersecurity risks in the context of interdependent systems, and impact of recent cyberattacks on public services in cities across the globe. The significance of risk management and assurance in smart cities is introduced in this paper to address the cybersecurity risks arising from system of systems. Cybersecurity risk management utilizing NIST’s Risk Management Framework has been recommended in this paper for smart city councils through a step-wise approach, highlighting the necessary actions for effective operational cybersecurity risk assurance.

Targeting the deacetylase SIRT6 unveils spliceosome deregulation as exploitable vulnerability for aggressive myeloma.

Targeting the deacetylase SIRT6 unveils spliceosome deregulation as exploitable vulnerability for aggressive myeloma.

PLC Cybersecurity Test Platform Establishment and Cyberattack Practice

Programming logic controllers (PLCs) are vital components for conveyors in production lines, and the sensors and actuators controlled underneath the PLCs represent critical points in the manufacturing process. Attacks targeting the exploitation of PLC vulnerabilities have been on the rise recently. In this study, a PLC test platform aims to analyze the vulnerabilities of a typical industrial setup and perform cyberattack exercises to review the system cybersecurity challenges. The PLC test platform is a sorting machine consisting of an automatic conveyor belt, two Mitsubishi FX5U-32M PLCs, and accessories for material sorting, and Modbus is the selected protocol for data communication. The O.S. on the attacker is Kali ver. 2022.3, runs Nmap and Metasploit to exploit the target Modbus registers. On the other hand, the target host runs the O.S., Ubuntu 22.04 in the cyberattack exercises. The selected attack method for this study is packet reply which can halt operations sending custom data packets to the PLC. In summary, this study provides a basic step-by-step offensive strategy targeting register modification, and the testbed represents a typical industrial environment and its vulnerabilities against cyberattacks with common open-source tools.

A Quantitative Risk Assessment Framework for the Cybersecurity of Networked Medical Devices

Medical devices are increasingly the source of cybersecurity exposure in healthcare organizations. Research and media reports demonstrate that the exploitation of cybersecurity vulnerabilities can have significant adverse impacts ranging from the exposure of sensitive and personally identifiable patient information to compromising the integrity and availability of clinical care. The results can include identity theft and negative health consequences, including loss of life. Assessing the risk posed by medical devices can provide healthcare organizations with information to prioritize mitigation efforts. However, producing accurate risk assessments in environments with both sparse historical data and a lack of validation regarding the accuracy of forecasts is particularly challenging.
 We present a risk assessment framework for quantifying the risk posed by connected medical devices in trusted healthcare networks. Our framework is built upon prominent existing frameworks and guidance for general risk assessment and cybersecurity risk assessment. We add a method for quantifying risk, which to our knowledge is novel in the context of medical devices on trusted networks. The framework provides a structure for combining publicly available information along with expert elicitation about threats, vulnerabilities, and consequences. The goal is to provide healthcare organizations with actionable information for prioritizing and mitigating risks in medical devices.

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.

CD38-Induced Metabolic Dysfunction Primes Multiple Myeloma Cells for NAD+-Lowering Agents

Cancer cells fuel growth and energy demands by increasing their NAD+ biosynthesis dependency, which therefore represents an exploitable vulnerability for anti-cancer strategies. CD38 is a NAD+-degrading enzyme that has become crucial for anti-MM therapies since anti-CD38 monoclonal antibodies represent the backbone for treatment of newly diagnosed and relapsed multiple myeloma patients. Nevertheless, further steps are needed to enable a full exploitation of these strategies, including deeper insights of the mechanisms by which CD38 promotes tumorigenesis and its metabolic additions that could be selectively targeted by therapeutic strategies. Here, we present evidence that CD38 upregulation produces a pervasive intracellular-NAD+ depletion, which impairs mitochondrial fitness and enhances oxidative stress; as result, genetic or pharmacologic approaches that aim to modify CD38 surface-level prime MM cells to NAD+-lowering agents. The molecular mechanism underlying this event is an alteration in mitochondrial dynamics, which decreases mitochondria efficiency and triggers energetic remodeling. Overall, we found that CD38 handling represents an innovative strategy to improve the outcomes of NAD+-lowering agents and provides the rationale for testing these very promising agents in clinical studies involving MM patients.

Efficient feature extraction methodologies for unknown MP4-Malware detection using Machine learning algorithms

We are living in an era in which daily interaction between individuals and businesses involves sending, uploading, and sharing videos as a means of communication and advertising. However, many users are unaware of the risks associated with opening a malicious video file, it is thus no surprise that cyber-criminals have taken advantage of this situation and adopted this attack vector in recent years. MP4 is one of the most commonly used video formats, and its properties make it well-suited for software vulnerability exploitation across multiple platforms, which can ultimately lead to a cyberattack. Due to their deterministic, signature-based technique, antivirus software solutions are limited in their ability to detect unknown malware, let alone zero-day attacks. Machine learning (ML) algorithms have been effective in detecting known and unknown malware across various file formats, domains, and platforms. ML algorithms’ performance relies heavily on the feature extraction methodology. However, to the best of our knowledge, there is no designated and specialized feature extraction methodology for MP4 files which generates a set of features for the task of unknown MP4 file malware detection. In this paper, we present three innovative and efficient feature extraction methodologies for unknown MP4 file malware detection. Two of them are file structure-based and one is knowledge-based. The methodologies are evaluated in a series of five experiments using six ML algorithms and 177 different datasets which represent different configurations of feature extraction, representation, and selection. The datasets are based on a representative collection of 6,229 files − 5,066 benign (∼81.3 %) files and 1,163 malicious files (∼18.7 %). The first three experiments demonstrate the methodologies’ discrimination and generalization capabilities across multiple configurations, in terms of known and unknown MP4 file malware detection. The fourth experiment shows that applying principal component analysis (PCA) on the features suggested by the methodologies can improve time and space complexity and feature resilience while maintaining strong detection and generalization capabilities. In the fifth experiment, the methodologies’ best performing configuration is compared to state-of-the-art, generic feature extraction methodologies, such as n-grams, MinHash, and representation and transfer learning (using a CNN), in the task of unknown MP4 file malware detection. The results show that our best performing configuration outperforms all other state-of-the-art feature extraction methodologies with an AUC, TPR, and FPR of 0.9951, 0.976, and 0.0 respectively.

DroidRL: Feature selection for android malware detection with reinforcement learning

Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. Machine learning, leading to a great evolution in Android malware detection in recent years, is typically applied in the classification phase. Since the correlation between features is ignored in some traditional ranking-based feature selection algorithms, applying wrapper-based feature selection models is a topic worth investigating. Though considering the correlation between features, wrapper-based approaches are time-consuming for exploring all possible valid feature subsets when processing a large number of Android features. To reduce the computational expense of wrapper-based feature selection, a framework named DroidRL is proposed. The framework deploys DDQN algorithm to obtain a subset of features which can be used for effective malware classification. To select a valid subset of features over a larger range, the exploration-exploitation policy is applied in the model training phase. The recurrent neural network (RNN) is used as the decision network of DDQN to give the framework the ability to sequentially select features. Word embedding is applied for feature representation to enhance the framework’s ability to find the semantic relevance of features. The framework’s feature selection exhibits high performance without any human intervention and can be ported to other feature selection tasks with minor changes. The experiment results show a significant effect when using the Random Forest as DroidRL’s classifier, which reaches 95.6% accuracy with only 24 features selected.

Biallelic TET2 mutations confer sensitivity to 5'-azacitidine in acute myeloid leukemia.

Precision medicine can significantly improve outcomes for patients with cancer, but implementation requires comprehensive characterization of tumor cells to identify therapeutically exploitable vulnerabilities. Here, we describe somatic biallelic TET2 mutations in an elderly patient with acute myeloid leukemia (AML) that was chemoresistant to anthracycline and cytarabine but acutely sensitive to 5'-azacitidine (5'-Aza) hypomethylating monotherapy, resulting in long-term morphological remission. Given the role of TET2 as a regulator of genomic methylation, we hypothesized that mutant TET2 allele dosage affects response to 5'-Aza. Using an isogenic cell model system and an orthotopic mouse xenograft, we demonstrate that biallelic TET2 mutations confer sensitivity to 5'-Aza compared with cells with monoallelic mutations. Our data argue in favor of using hypomethylating agents for chemoresistant disease or as first-line therapy in patients with biallelic TET2-mutated AML and demonstrate the importance of considering mutant allele dosage in the implementation of precision medicine for patients with cancer.

Trustworthiness models to categorize and prioritize code for security improvement

The exploitation of software security vulnerabilities can have severe consequences. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing costs and shortening the time to market. In this paper, we propose an approach that uses security evidences (e.g., software metrics, bad smells) to feed a set of trustworthiness models, which allow characterizing code from a security perspective. In practice, the goal is to identify the code units that are more prone to be vulnerable (i.e., are less trustworthy from a security perspective), thus helping developers to improve their code. A clustering-based approach is used to categorize the code units based on the combination of the scores provided by several trustworthiness models and taking into account the criticality of the code. To instantiate our proposal, we use a dataset of software metrics (e.g., CountLine, Cyclomatic Complexity, Coupling Between Objects) for files and functions of the Linux Kernel and Mozilla Firefox projects, and a set of machine learning algorithms (i.e., Random Forest, Decision Tree, SVM Linear, SVM Radial, and Xboost) to build the trustworthiness models. Results show that code that is more prone to be vulnerable can be effectively distinguished, thus demonstrating the applicability and usefulness of the proposed approach in diverse scenarios.

Proposals of Processes and Organizational Preventive Measures against Malfunctioning of Drones and User Negligence

Drones have been included in more and more activities in various domains, such as military, commercial and personal use. The existing legislative framework insufficiently addresses the responsibility and preventive measures angles in case of vulnerability exploitation and negligence in drone usage. Such aspects can be addressed by the industry in technological processes and standardization. These are especially important aspects given the high impact that misuse of drones can have on individuals, property and buildings within the flight zone when the drone is misused. The aim of this research paper is to investigate how these elements are viewed in existing legislation and by individuals, while taking into account the technical specifics and the stakeholder ecosystem of drone usage. In this respect, we use a complex questionnaire which was sent to a final number of 233 respondents pertaining to firms specialized in IT, legal and cybersecurity. The responses have been analyzed from a qualitative and quantitative perspective. Our results highlight the areas of improvement in the existing standardization and find the followings: (1) stakeholders across the drone ecosystem are viewed as having a shared liability in certain use cases, (2) preventive measure implementation should be dispersed across the stakeholders of drone usage and (3) automation of prevention measures is considered more useful in case of malfunctioning or misuse of drones rather than user manual intervention. In addition, we make proposals to accommodate new policy requirements for the above use cases. The results of this research paper assist policy makers in improving existing standardization framework and technological processes concerning drone usage, but also stakeholders of the drone ecosystem in generating increased trust of the drone users. Further, this research paper can also assist drone software and hardware producers in calibrating their products to ensure trust of the users. In addition, trust in the use of drones for commercial and personal purposes is increased through standardization and proper approaches for situations that may cause damages to drones and to third parties.

Quantifying Satisfaction of Security Requirements of Cloud Software Systems

The satisfaction of a software requirement is commonly stated as a Boolean value, that is, a security requirement is either satisfied (true) or not (false). However, a discrete Boolean value to measure the satisfaction level of a security requirement by deployed mechanisms is not very useful. Rather, it would be more effective if we could quantify the level of satisfaction of security requirements on a continuous scale. We propose an approach to achieve this for cloud software systems based on relationships between defense strength, exploitability of vulnerabilities, and attack severity. We extend the concept of entailment relationship from the field of requirements engineering with the satisfiability aspects of security requirements. The proposed approach enables us to systematically structure security concepts into three sets of related descriptions to quantify the satisfaction level of security requirements with the deployed security solutions. To demonstrate the feasibility of the proposed approach, we evaluate the approach in a case study. As a result, security administrators are able to deploy more effective and appropriate security solutions based on their assessment.

Cyber security in (automated) vehicles and liability: the EU legal framework and (a lack of) compensation

This contribution will identify several shortcomings in the current EU legal framework on liability for damage caused by hacked vehicles. It is argued that the Motor Insurance Directive and the Product Liability Directive are not sufficiently contributing to the compensation of the party injured by a hacked automated vehicle. Clarification on the interpretation of the notion of ‘product’, a key notion in the Product Liability Directive, is required to provide legal certainty on the compensation for damage caused by a hacked automated vehicle. Moreover, two defences within the Product Liability Directive need rethinking. In addition, the exploitation of a cyber security vulnerability of a vehicle might fall outside of the scope of the Motor Insurance Directive, risking a difference in compensation of damage caused by a conventional vehicle and damage caused by a hacked automated vehicle.

Ethical hacking for IoT: Security issues, challenges, solutions and recommendations

In recent years, attacks against various Internet-of-Things systems, networks, servers, devices, and applications witnessed a sharp increase, especially with the presence of 35.82 billion IoT devices since 2021; a number that could reach up to 75.44 billion by 2025. As a result, security-related attacks against the IoT domain are expected to increase further and their impact risks to seriously affect the underlying IoT systems, networks, devices, and applications. The adoption of standard security (counter) measures is not always effective, especially with the presence of resource-constrained IoT devices. Hence, there is a need to conduct penetration testing at the level of IoT systems. However, the main issue is the fact that IoT consists of a large variety of IoT devices, firmware, hardware, software, application/web-servers, networks, and communication protocols. Therefore, to reduce the effect of these attacks on IoT systems, periodic penetration testing and ethical hacking simulations are highly recommended at different levels (end-devices, infrastructure, and users) for IoT, and can be considered as a suitable solution. Therefore, the focus of this paper is to explain, analyze and assess both technical and non-technical aspects of security vulnerabilities within IoT systems via ethical hacking methods and tools. This would offer practical security solutions that can be adopted based on the assessed risks. This process can be considered as a simulated attack(s) with the goal of identifying any exploitable vulnerability or/and a security gap in any IoT entity (end devices, gateway, or servers) or firmware.

Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths. The proposed method consists of three primary building blocks: KG construction, relation path representation, and query relation reasoning. A security-oriented ontology combines exploit modeling, which provides a guideline for the integration of the scattered knowledge while constructing the KG. We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning. In order to acquire a high-quality representation, the entity and relation embeddings take advantage of their local structure and related semantics. Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity. In particular, similarity calculation is introduced into a critical path selection algorithm, which improves search and reasoning performance. Meanwhile, the proposed algorithm avoids redundant paths between the given pairs of entities. Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer.

On the Way to Automatic Exploitation of Vulnerabilities and Validation of Systems Security through Security Chaos Engineering

Software is behind the technological solutions that deliver many services to our society, which means that software security should not be considered a desirable feature anymore but more of a necessity. Protection of software is an endless labor that includes the improvement of security controls but also the understanding of the sources that induce incidents, which in many cases are due to bad implementation or assumptions of controls. As traditional methods may not be efficient in detecting those security assumptions, novel alternatives must be attempted. In this sense, Security Chaos Engineering (SCE) becomes an innovative methodology based on the definition of a steady state, a hypothesis, experiments, and metrics, which allow to identify failing components and ultimately protect assets under cyber risk scenarios. As an extension of a previous work, this paper presents ChaosXploit, an SCE-powered framework that employs a knowledge database, composed of attack trees, to expose vulnerabilities that exist in a software solution that has been previously defined as a target. The use of ChaosXploit may be part of a defensive security strategy to detect and correct software misconfigurations at an early stage. Finally, different experiments are described and executed to validate the feasibility of ChaosXploit in terms of auditing the security of cloud-managed services, i.e., Amazon buckets, which may be prone to misconfigurations and, consequently, targeted by potential cyberattacks.