Security Mitigation Research Articles

Review on the application of data security in the enterprise management

In recent decades, cybersecurity has become a crucial topic in business management, centred around the three core elements of Confidentiality, Integrity, and Availability (C.I.A.) of information technology. Cybersecurity aims to protect IT systems and networks from cyberattacks, with data as the primary focus. In business management, strategies for addressing potential threats vary depending on the size and type of business. This writing explores the preferences for implementing cybersecurity strategies in business management and trends in the development of data security mitigation. It analyses the mitigation strategies adopted by different enterprises, identifies patterns between data security strategies and enterprise attributes, and examines the preferences and constraints faced by enterprises in developing cybersecurity programs. The study highlights trends in the implementation of data security strategies, emphasizes the importance of cybersecurity, and provides insights into future trends in digital security within enterprise management and development. It also addresses the challenges faced by businesses, particularly small and medium-sized enterprises, in implementing effective cybersecurity measures due to limited resources.

Security Vulnerabilities in 5G Non-Stand-Alone Networks: A Systematic Analysis and Attack Taxonomy

5G networks, pivotal for our digital mobile societies, are transitioning from 4G to 5G Stand-Alone (SA) networks. However, during this transition, 5G Non-Stand-Alone (NSA) networks are widely used. This paper examines potential security vulnerabilities in 5G NSA networks. Through an extensive literature review, we identify known 4G attacks that can theoretically be applied to 5G NSA. We organize these attacks into a structured taxonomy. Our findings reveal that 5G NSA networks may offer a false sense of security, as most security and privacy improvements are concentrated in 5G SA networks. To underscore this concern, we implement three attacks with severe consequences and successfully validate them on various commercially available smartphones. Notably, one of these attacks, the IMSI Leak, consistently exposes user information with no apparent security mitigation in 5G NSA networks. This highlights the ease of tracking individuals on current 5G networks.

Cross-cutting concepts to transform agricultural research

Agriculture is an important link to many issues that challenge society today, including adaptation to and mitigation of climate change, food security, and communicable and non-communicable diseases in animals and humans. Transformation of agriculture and food systems has become a priority for a range of federal agencies and global organizations. It is imperative that food and agricultural researchers effectively harness the global convergence of priorities to overcome research “silos” through deep and sustained systemic change. Herein, we identify intersections in federal and global initiatives encompassing climate adaptation and mitigation; human health and nutrition; animal health and welfare; food safety and security; and equity and inclusion. Many agencies and organizations share these priorities, but efforts to address them remain uncoordinated and opportunities for collaboration untapped. Based on the interconnectedness of the identified priority areas, we present a research framework to catalyze agricultural transformation, beginning with the research enterprise. We propose that transformation in agricultural research should incorporate (1) innovation, (2) integration, (3) implementation, and (4) evaluation. This framework provides approaches for food and agricultural research to contribute to sustainable, flexible, and coordinated transformation in the agricultural sector.

Cyber Warfare and Cyber Terrorism Threats Targeting Critical Infrastructure: A HCPS-based Threat Modelling Intelligence Framework

Acts of cyber warfare and cyber terrorism (CWCT) that target a nation's critical infrastructure (CI) are quickly becoming a larger threat to national security than conventional kinetic warfare strategies. Adversaries or potential adversaries can target a nation's electrical grids, telecommunications, financial services, transportation, healthcare systems, and other forms of CI. These acts pose a major threat to a nation's CI and consequently exposes citizens to public health, safety, security, and economic development risks. Identifying cyber vulnerabilities and threats can help nations to improve their CI defence strategies. There is a crucial need for research that can aid in understanding the major types of CI threats and by what method they might occur. This paper conducts a systematic literature review to develop an initial threat intelligence framework of CWCT attacks on CI. Drawing from a Human–Cyber–Physical Systems (HCPS) lens, the threat intelligence framework classifies CWCT attacks according to the methods, weapons, vulnerabilities, targets and impact of the CWCT attack. The cyber warfare community can extend the proposed HCPS-based threat intelligence framework to develop more advanced cyber security mitigation strategies, training scenarios and simulations. Large-scale monitoring of CI threats requires in-depth threat intelligence analysis and a collaborative defence strategy. This calls for a higher degree of coordination and orchestration between the military, intelligence agencies, government departments, multinational allies, regulators, and commercial entities. Future research can customize the proposed HCPS-based threat intelligence framework to cater for the unique threats facing specific CI domains.

Browning of vegetation in efficient carbon sink regions of India during the past two decades is driven by climate change and anthropogenic intrusions

Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 2000–2019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.

Urban agriculture in walkable neighborhoods bore fruit for health and food system resilience during the COVID-19 pandemic

Urban agriculture is the key to creating healthy cities and developing resilient urban food systems in uncertain times. However, relevant empirical evidence is limited. This study quantitatively verified the association of access to local food through urban agriculture with subjective well-being, physical activity, and food security concerns of neighborhood communities in the context of the COVID-19 pandemic. The target was Tokyo, Japan, where small-scale local food systems are widespread in walkable neighborhoods. We found that diversity in local food access, ranging from self-cultivation to direct-to-consumer sales, was significantly associated with health and food security variables. In particular, the use of allotment farms was more strongly associated with subjective well-being than the use of urban parks, and it was more strongly associated with the mitigation of food security concerns than the use of food retailers. These findings provide robust evidence for the effectiveness of integrating urban agriculture into walkable neighborhoods.

Intent-Driven Secure System Design: Methodology and Implementation

Given the typical complexity of networked systems in terms of number of components and their interconnections, manually designing their architecture is inherently difficult, and the design process requires expert knowledge and skills. If we also consider the security requirements that networked systems must meet, the task becomes even more demanding, since the manual audit and security mitigation of the architecture are time and labor intensive. This led to research on automated system design, including ways to cover the related security aspects.In this paper we present a methodology for secure system design that uses an intent-based representation of the network service requirements as input, which is annotated with security requirements, and applies the Design Space Exploration (DSE) approach to generate the system design. Security is handled via a MITRE ATT&CK-based security knowledge base, and a set of security check functions, so that the resulting system design meets not only the functional and quantitative requirements, but also the specified security requirements. We implemented this methodology as the secure system designer SecureWeaver by extending the functionality of an existing intent-based system designer that targeted IT/NW services, named Weaver. A case study of a typical corporate network scenario is used to illustrate the feasibility of the methodology in producing a system design that mitigates the associated security threats. The performance evaluation we conducted for this scenario demonstrates that the added security check overhead does not have a significant impact on the overall performance characteristics of the framework.

Contours of Urban Violence and Insecurity: Digital Media Technologies and Mitigation of Urban Violence Among Undocumented African Migrants in South Africa

This is a qualitative exploratory investigation into the use of digital media technologies in the mitigation of security concerns among undocumented migrants in the informal settlements within urban South Africa. The article acknowledges that adaptation of migrants in new environments is generally compounded by a myriad of challenges most of which are linked to lack of access to government systems and social capital or survival networks. This is particularly applicable to the urban settings where rampant violent crime and insecurity concerns are often laced with outbursts of xenophobia, which contribute to further uncertainties among migrants. Extensive literature review and social media theory are used to explore the potential for digital media tools to mitigate violence and insecurity among migrants. A piloted in-depth interview was used to collect data from undocumented immigrants based on a snowballed sample to saturation levels. Data were analyzed thematically to generate themes from which study findings were derived. Study findings show that, undocumented migrants suffer inexplicable barrages of social injustice due to violent crime and other insecurity concerns largely because they are scared to report cases to law enforcement agents, who in turn arrest them for being illegal in the country. Subsequently, the advent of digital and social media platforms, particularly WhatsApp helps them to create a survival network that provides a sense of security and to foster collaborations that deal with their safety concerns, uncertainty, and support for victims of violent crime.

A Review on Software-Based and Hardware-Based Authentication Mechanisms for the Internet of Drones

During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work.

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software- and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.

A Threat Model for Vehicular Fog Computing

Vehicular Fog Computing (VFC) facilitates the deployment of distributed, latency-aware services, residing between smart vehicles and cloud services. However, VFC systems are exposed to manifold security threats, putting human life at risk. Knowledge on such threats is scattered and lacks empirical validation. We performed an extensive threat assessment by reviewing literature and conducting expert interviews, leading to a comprehensive threat model with 33 attacks and example security mitigation strategies, among others. We thereby synthesize and extend prior research; provide rich descriptions for threats; and raise awareness of physical attacks that underline importance of the cyber-physical manifestation of VFC.

Demystifying Cryptocurrency Mining Attacks: A Semi-supervised Learning Approach Based on Digital Forensics and Dynamic Network Characteristics

Cryptocurrencies have emerged as a new form of digital money that has not escaped the eyes of cyber-attackers. Traditionally, they have been maliciously used as a medium of exchange for proceeds of crime in the cyber dark-market by cyber-criminals. However, cyber-criminals have devised an exploitative technique of directly acquiring cryptocurrencies from benign users' CPUs without their knowledge through a process called crypto mining. The presence of crypto mining activities in a network is often an indicator of compromise of illegal usage of network resources for crypto mining purposes. Crypto mining has had a financial toll on victims such as corporate networks and individual home users. This paper addresses the detection of crypto mining attacks in a generic network environment using dynamic network characteristics. It tackles an in-depth overview of crypto mining operational details and proposes a semi-supervised machine learning approach to detection using various crypto mining features derived from complex network characteristics. The results demonstrate that the integration of semi-supervised learning with complex network theory modeling is effective at detecting crypto mining activities in a network environment. Such an approach is helpful during security mitigation by network

Security and Autonomic Management in System of Systems

A system of systems integrates systems that function independently but are networked together for a period of time to achieve a higher goal. These systems evolve over time and have emergent properties. Therefore, even with security controls in place, it is difficult to maintain a required level of security for the system of systems as a whole because uncertainties may arise at runtime. Uncertainties can occur from internal factors, such as malfunctions of a system, or from external factors, such as malicious attacks. Self-adaptation is an approach that allows a system to adapt in the face of such uncertainties without human intervention. This work outlines the progress made towards security mitigation in system of systems using a generic autonomic management system to assist engineers in developing self-adaptive systems. The manuscript describes the proposed system design, its implementation as part of the Eclipse Arrowhead framework, and its functionality in a smart agriculture use case. The system is designed and implemented in such a way that it can be reused and extended for a variety of use cases without requiring major changes.

Laser-Induced Fault Injection on Smartphone Bypassing the Secure Boot-Extended Version

This paper describes the outcome of a laser attack study on an Android smartphone targeting specifically the secure boot sequence. Laser fault injection has become a classical attack path in the secure chip industry to investigate potential security mitigation. The implementation of such attacks on a recent mobile phone remains relatively unexplored and represents different challenges, both at hardware and software levels. In this paper, we show how the device is crafted to get a direct access to the silicon and explain the corresponding experimental setup. By inserting our own software into the boot sequence, it was possible to achieve a fine characterization of the die sensitivity to laser emissions. With the knowledge of potential perturbations, several attack scenarios were built, allowing to malevolently get the highest level of privilege within the mobile phone.

Security Threats and Mitigation Approaches in IoT based Applications

Security Threats and Mitigation Approaches in IoT based Applications

Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration

Drought is a major risk in global agriculture. Building-up soil organic carbon (SOC) enhances soil fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC management demonstrates its benefit at various locations and is a promising means to achieve food security and climate mitigation at once. However, no global assessment of its potential and co-benefits gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global crop and soil datasets, we find that relatively small enhancement in topsoil (0–30 cm) organic carbon content (OCtop) could increase drought tolerance of the food production systems operating over 70% of the global harvested area (particularly drylands). By closing the gap between current and upper limit of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer’s economic output in drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple development goals can be leveraged by SOC enhancement in carbon (C)-poor soils in drier regions around the world.

Achievements and misses of the Indian national policy on biofuels 2009

The National Policy on Biofuels (NPB) 2009 was adopted on 24th December 2009 in India to ensure the minimum availability of biofuels at any given time to meet the demand. NPB 2009 laid down the vision, goals and strategy to biofuel development while proposing a framework of technological, financial and enabling mechanisms. The NPB 2009 undertook reforms and aimed to increase the biodiesel blending percentage in diesel and bioethanol blending percentage in petrol to 20% each by 2017. It promoted inclusive growth, development and contributed to energy security and climate change mitigation. The lack of assured supply of raw material feedstock for biofuel production has been the major slippage of NPB 2009. NPB 2018 was adopted on 16th May 2018 and built on the foundation laid upon by NPB 2009. The paper focuses on the importance of NPB 2009, its achievements and misses and touches upon NPB 2018.

From proof-of-concept to exploitable

Exploitability assessment of vulnerabilities is important for both defenders and attackers. The ultimate way to assess the exploitability is crafting a working exploit. However, it usually takes tremendous hours and significant manual efforts. To address this issue, automated techniques can be adopted. Existing solutions usually explore in depth the crashing paths, i.e., paths taken by proof-of-concept (PoC) inputs triggering vulnerabilities, and assess exploitability by finding exploitable states along the paths. However, exploitable states do not always exist in crashing paths. Moreover, existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit generation.In this paper, we propose a novel solution to generate exploit for userspace programs or facilitate the process of crafting a kernel UAF exploit. Technically, we utilize oriented fuzzing to explore diverging paths from vulnerability point. For userspace programs, we adopt a control-flow stitching solution to stitch crashing paths and diverging paths together to generate exploit. For kernel UAF, we leverage a lightweight symbolic execution to identify, analyze and evaluate the system calls valuable and useful for exploiting vulnerabilities.We have developed a prototype system and evaluated it on a set of 19 CTF (capture the flag) programs and 15 realworld Linux kernel UAF vulnerabilities. Experiment results showed it could generate exploit for most of the userspace test set, and it could also facilitate security mitigation bypassing and exploitability evaluation for kernel test set.

A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise

Cyber threat attribution identifies the source of a malicious cyber activity, which in turn informs cyber security mitigation responses and strategies. Such responses and strategies are crucial for deterring future attacks, particularly in the financial and critical infrastructure sectors. However, existing approaches generally rely on manual analysis of attack indicators obtained through approaches such as trace-back, firewalls, intrusion detection and honeypot deployments. These attack indicators, also known as low-level Indicators of Compromise (IOCs), are rarely re-used and can be easily modified and disguised resulting in a deceptive and biased cyber threat attribution. Cyber attackers, particularly financially-motivated actors, can use common high-level attack patterns that evolve less frequently as compared to the low-level IOCs. To attribute cyber threats effectively, it is necessary to identify them based on the high-level adversary’s attack patterns (e.g. tactics, techniques and procedures - TTPs, software tools and malware) employed in different phases of the cyber kill chain. Identification of high-level attack patterns is time-consuming, requiring forensic investigation of the victim network(s) and other resources. In the rare case that attack patterns are reported in cyber threat intelligence (CTI) reports, the format is textual and unstructured typically taking the form of lengthy incident reports prepared for human consumption (e.g. prepared for C-level and senior management executives), which cannot be directly interpreted by machines. Thus, in this paper we propose a framework to automate cyber threat attribution. Specifically, we profile cyber threat actors (CTAs) based on their attack patterns extracted from CTI reports, using the distributional semantics technique of Natural Language Processing. Using these profiles, we train and test five machine learning classifiers on 327 CTI reports collected from publicly available incident reports that cover events from May 2012 to February 2018. It is observed that the CTA profiles obtained attribute cyber threats with a high precision (i.e. 83% as compared to other publicly available CTA profiles, where the precision is 33%). The Deep Learning Neural Network (DLNN) based classifier also attributes cyber threats with a higher accuracy (i.e. 94% as compared to other classifiers).

Intertemporal soil management: revisiting the shape of the crop production function

Soil resources play a role in food security and climate change mitigation. Through their practices, farmers impact the physical, biological and chemical quality of their soil. However, farmers face a trade-off between the short-term objectives of production and profitability and the long-term objective of soil resource conservation. In this article, we investigate the conditions under which farmers have a private interest in preserving their soil quality. We use a simplified theoretical soil quality investment model, where farmers maximize their revenues under a soil quality dynamic constraint. Here, soil quality is an endogenous production factor of the crop production function. We show that the existence of an equilibrium depends on the cooperation between soil quality and productive inputs. The results are confronted to a statistical illustration in France. In this case, nitrogen fertilizers are not cooperating with soil organic carbon. Incentives to reduce nitrogen fertilizers would not trigger a negative feedback effect.