Biblio

Smart metering is a mechanism through which fine-grained electricity usage data of consumers is collected periodically in a smart grid. However, a growing concern in this regard is that the leakage of consumers' consumption data may reveal their daily life patterns as the state-of-the-art metering strategies lack adequate security and privacy measures. Many proposed solutions have demonstrated how the aggregated metering information can be transformed to obscure individual consumption patterns without affecting the intended semantics of smart grid operations. In this paper, we expose a complete break of such an existing privacy preserving metering scheme [10] by determining individual consumption patterns efficiently, thus compromising its privacy guarantees. The underlying methodol-ogy of this scheme allows us to - i) retrieve the lower bounds of the privacy parameters and ii) establish a relationship between the privacy preserved output readings and the initial input readings. Subsequently, we present a rigorous experimental validation of our proposed attacking methodology using real-life dataset to highlight its efficacy. In summary, the present paper queries: Is the Whole lesser than its Parts? for such privacy aware metering algorithms which attempt to reduce the information leakage of aggregated consumption patterns of the individuals.

Satisfying the growing demand for electricity is a huge challenge for electricity providers without a robust and good infrastructure. For effective electricity management, the infrastructure has to be strengthened from the generation stage to the transmission and distribution stages. In the current electrical infrastructure, the evolution of smart grids provides a significant solution to the problems that exist in the conventional system. Enhanced management visibility and better monitoring and control are achieved by the integration of wireless sensor network technology in communication systems. However, to implement these solutions in the existing grids, the infrastructural constraints impose a major challenge. Along with the choice of technology, it is also crucial to avoid exorbitant implementation costs. This paper presents a self-stabilizing hierarchical algorithm for the existing electrical network. Neighborhood Area Networks (NAN) and Home Area Networks (HAN) layers are used in the proposed architecture. The Home Node (HN), Simple Node (SN) and Cluster Head (CH) are the three types of nodes used in the model. Fraudulent users in the system are identified efficiently using the proposed model based on the observations made through simulation on OMNeT++ simulator.

In view of the problems that the existing power grid risk assessment mainly depends on the data fusion of decision-making level, which has strong subjectivity and less effective information, this paper proposes a risk assessment method of microgrid system based on random matrix theory. Firstly, the time series data of multiple sensors are constructed into a high-dimensional matrix according to the different parameter types and nodes; Then, based on random matrix theory and sliding time window processing, the average spectral radius sequence is calculated to characterize the state of microgrid system. Finally, an example is given to verify the effectiveness of the method.

In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.

This paper belongs to a sequence of manuscripts that discuss generic and easy-to-apply security metrics for Strong PUFs. These metrics cannot and shall not fully replace in-depth machine learning (ML) studies in the security assessment of Strong PUF candidates. But they can complement the latter, serve in initial PUF complexity analyses, and are much easier and more efficient to apply: They do not require detailed knowledge of various ML methods, substantial computation times, or the availability of an internal parametric model of the studied PUF. Our metrics also can be standardized particularly easily. This avoids the sometimes inconclusive or contradictory findings of existing ML-based security test, which may result from the usage of different or non-optimized ML algorithms and hyperparameters, differing hardware resources, or varying numbers of challenge-response pairs in the training phase.This first manuscript within the abovementioned sequence treats one of the conceptually most straightforward security metrics on that path: It investigates the effects that small perturbations in the PUF-challenges have on the resulting PUF-responses. We first develop and implement several sub-metrics that realize this approach in practice. We then empirically show that these metrics have surprising predictive power, and compare our obtained test scores with the known real-world security of several popular Strong PUF designs. The latter include (XOR) Arbiter PUFs, Feed-Forward Arbiter PUFs, and (XOR) Bistable Ring PUFs. Along the way, our manuscript also suggests techniques for representing the results of our metrics graphically, and for interpreting them in a meaningful manner.

Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on "trial and error" experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture relationships between configurations, permissions (e.g., CAP\_SYS\_ADMIN), and security profiles (e.g., AppArmor and SecComp). Such an approach allows us to suggest alternative and safer configurations, support administrators in the study of what-if scenarios, and scale the analysis to large scale deployments. We present an initial validation and illustrate the approach with three real vulnerabilities from known sources.

Simulations have gained paramount importance in terms of software development for wireless sensor networks and have been a vital focus of the scientific community in this decade to provide efficient, secure, and safe communication in smart cities. Network Simulators are widely used for the development of safe and secure communication architectures in smart city. Therefore, in this technical survey report, we have conducted experimental comparisons among ten different simulation environments that can be used to simulate smart-city operations. We comprehensively analyze and compare simulators COOJA, NS-2 with framework Mannasim, NS-3, OMNeT++ with framework Castalia, WSNet, TOSSIM, J-Sim, GloMoSim, SENSE, and Avrora. These simulators have been run eight times each and comparison among them is critically scrutinized. The main objective behind this research paper is to assist developers and researchers in selecting the appropriate simulator against the scenario to provide safe and secure wired and wireless networks. In addition, we have discussed the supportive simulation environments, functions, and operating modes, wireless channel models, energy consumption models, physical, MAC, and network-layer protocols in detail. The selection of these simulation frameworks is based on features, literature, and important characteristics. Lastly, we conclude our work by providing a detailed comparison and describing the pros and cons of each simulator.

Currently, many organizations are moving to new digital management systems, which is accompanied not only by the introduction of new approaches based on the use of information technology, but also by a change in the organizational and management environment. Risk management is a process necessary to maintain the competitive advantage of an organization, but it can also become involved in the course of digitalization itself, which means that risk management also needs to change to meet modern conditions and ensure the effectiveness of the organization. This article discusses the risk management process in the digital environment. The main approach to the organization of this process is outlined, taking into account the use of information tools, together with the stages of this process, which directly affect the efficiency of the company. The risks that are specific to a digital organization are taken into account. Modern requirements for risk management for organizations are studied, ways of their implementation are outlined. The result is a risk management process that functions in a digital organization.

The Personnel Management Information System is managed by the Personnel and Human Resources Development Agency on local government office to provide personnel services. The existence of a system and information technology can help ongoing business processes but can have an impact or risk if the proper mitigation is not carried out. It is known that the problems are damage to databases, servers, and computer equipment due to bad weather, network connections being lost due to power outages, data loss due to not having backup data, and human error. This resulted in PMIS being inaccessible for some time, thus hampering ongoing business processes and causing financial losses. This study aims to identify risks, conduct a risk assessment using the failure mode and effects analysis (FMEA) method, and provide mitigation recommendations based on the ISO/IEC 27002:2013 standard. The analysis results obtained 50 failure modes categorized into five asset categories, and six failure modes have a high level. Then provide mitigation recommendations based on the ISO/IEC 27002:2013 Standard, which has been adapted to the needs of Human Resources Development Agency. Thus, the results of this study are expected to assist and serve as material for local office government's consideration in making improvements and security controls to avoid emerging threats to information assets.

As a new industry integrated by computing, communication, networking, electronics, and automation technology, the Internet of Vehicles (IoV) has been widely concerned and highly valued at home and abroad. With the rapid growth of the number of intelligent connected vehicles, the data security risks of the IoV have become increasingly prominent, and various attacks on data security emerge in an endless stream. This paper firstly introduces the latest progress on the data security policies, regulations, standards, technical routes in major countries and regions, and international standardization organizations. Secondly, the characteristics of the IoV data are comprehensively analyzed in terms of quantity, standard, timeliness, type, and cross-border transmission. Based on the characteristics, this paper elaborates the security risks such as privacy data disclosure, inadequate access control, lack of identity authentication, transmission design defects, cross-border flow security risks, excessive collection and abuse, source identification, and blame determination. And finally, we put forward the measures and suggestions for the security development of IoV data in China.

Companies store increasing amounts of data, requiring the implementation of mechanisms to protect them from malicious people. There are techniques and procedures that aim to increase the security of computer systems, such as network protection services, firewalls. They are intended to filter packets that enter and leave a network. Its settings depend on security policies, which consist of documents that describe what is allowed to travel on the network and what is prohibited. The transcription of security policies into rules, written in native firewall language, that represent them, is the main source of errors in firewall configurations. In this work, concepts related to security between networks and firewalls are presented. Related works on security policies and their translations into firewall rules are also referenced. Furthermore, the developed tool, named Fireasy, is presented, which allows the modeling of security policies through graphic elements, and the maintenance of rules written in native firewall language, also representing them in graphic elements. Finally, a controlled experiment was conducted to validate the approach, which indicated, in addition to the correct functioning of the tool, an improvement in the translation of security policies into firewall rules using the tool. In the task of understanding firewall rules, there was a homogenization of the participants' performance when they used the tool.

The purpose of this article is to consider one of the options for automating the process of collecting information from open sources when conducting penetration testing in an organization's information security audit using the capabilities of the Python programming language. Possible primary vectors for collecting information about the organization, personnel, software, and hardware are shown. The basic principles of operation of the software product are presented in a visual form, which allows automated analysis of information from open sources about the object under study.

Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different processes, and such information from different processes are stored in the same structure and thus accessible to each other. This leaves the attackers with the opportunities for malicious training and malicious perception. Physical or logical isolation mechanisms such as using dedicated tables and flushing during context-switch can provide security but incur non-trivial costs in space and/or execution time. Randomization mechanisms incurs the performance cost in a different way: those with higher securities add latency to the critical path of the pipeline, while the simpler alternatives leave vulnerabilities to more sophisticated attacks.This paper proposes HyBP, a practical hybrid protection and effective mechanism for building secure branch predictors. The design applies the physical isolation and randomization in the right component to achieve the best of both worlds. We propose to protect the smaller tables with physically isolation based on (thread, privilege) combination; and protect the large tables with randomization. Surprisingly, the physical isolation also significantly enhances the security of the last-level tables by naturally filtering out accesses, reducing the information flow to these bigger tables. As a result, key changes can happen less frequently and be performed conveniently at context switches. Moreover, we propose a latency hiding design for a strong cipher by precomputing the "code book" with a validated, cryptographically strong cipher. Overall, our design incurs a performance penalty of 0.5% compared to 5.1% of physical isolation under the default context switching interval in Linux.

Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches.

Dynamic Host Control Protocol (DHCP) is a protocol which provides IP addresses and network configuration parameters to the hosts present in the network. This protocol is deployed in small, medium, and large size organizations which removes the burden from network administrator to manually assign network parameters to every host in the network for establishing communication. Every vendor who plans to incorporate DHCP service in its device follows the working flow defined in Request for Comments (RFC). DHCP Starvation and DHCP Flooding attack are Denial of Service (DoS) attacks to prevents provision of IP addresses by DHCP. Port Security and DHCP snooping are built-in security features which prevents these DoS attacks. However, novel techniques have been devised to bypass these security features which uses ARP and ICMP protocol to perform the attack. The purpose of this research is to analyze implementation of DHCP in multiple devices to verify the involvement of both ARP and ICMP in the address acquisition process of DHCP as per RFC and to validate the results of prior research which assumes ARP or ICMP are used by default in all of devices.

This paper introduces lronMask, a new versatile verification tool for masking security. lronMask is the first to offer the verification of standard simulation-based security notions in the probing model as well as recent composition and expandability notions in the random probing model. It supports any masking gadgets with linear randomness (e.g. addition, copy and refresh gadgets) as well as quadratic gadgets (e.g. multiplication gadgets) that might include non-linear randomness (e.g. by refreshing their inputs), while providing complete verification results for both types of gadgets. We achieve this complete verifiability by introducing a new algebraic characterization for such quadratic gadgets and exhibiting a complete method to determine the sets of input shares which are necessary and sufficient to perform a perfect simulation of any set of probes. We report various benchmarks which show that lronMask is competitive with state-of-the-art verification tools in the probing model (maskVerif, scVerif, SILVEH, matverif). lronMask is also several orders of magnitude faster than VHAPS -the only previous tool verifying random probing composability and expandability- as well as SILVEH -the only previous tool providing complete verification for quadratic gadgets with nonlinear randomness. Thanks to this completeness and increased performance, we obtain better bounds for the tolerated leakage probability of state-of-the-art random probing secure compilers.

In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 [4] for skin lesion classification, KiTS-19 [17] for kidney tumor segmentation, and EAD-2019 [1] for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over stateof-the-art methods in attacking MIA models and bypassing backdoor defense. Source code will be available at code.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

As the effects of climate change are becoming more and more evident, the importance of improved situation awareness is also gaining more attention, both in the context of preventive environmental monitoring and in the context of acute crisis response. One important aspect of situation awareness is the correct and thorough monitoring of air pollutants. The monitoring is threatened by sensor faults, power or network failures, or other hazards leading to missing or incorrect data transmission. For this reason, in this work we propose two complementary approaches for predicting missing sensor data and a combined technique for detecting outliers. The proposed solution can enhance the performance of low-cost sensor systems, closing the gap of missing measurements due to network unavailability, detecting drift and outliers thus paving the way to its use as an alert system for reportable events. The techniques have been deployed and tested also in a low power microcontroller environment, verifying the suitability of such a computing power to perform the inference locally, leading the way to an edge implementation of a virtual sensor digital twin.

Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.

MQTT is widely adopted by IoT devices because it allows for the most efficient data transfer over a variety of communication lines. The security of MQTT has received increasing attention in recent years, and several studies have demonstrated the configurations of many MQTT brokers are insecure. Adversaries are allowed to exploit vulnerable brokers and publish malicious messages to subscribers. However, little has been done to understanding the security issues on the device side when devices handle unauthorized MQTT messages. To fill this research gap, we propose a fuzzing framework named ShadowFuzzer to find client-side vulnerabilities when processing incoming MQTT messages. To avoiding ethical issues, ShadowFuzzer redirects traffic destined for the actual broker to a shadow broker under the control to monitor vulnerabilities. We select 15 IoT devices communicating with vulnerable brokers and leverage ShadowFuzzer to find vulnerabilities when they parse MQTT messages. For these devices, ShadowFuzzer reports 34 zero-day vulnerabilities in 11 devices. We evaluated the exploitability of these vulnerabilities and received a total of 44,000 USD bug bounty rewards. And 16 CVE/CNVD/CN-NVD numbers have been assigned to us.

Forensic Science comprises a set of technical-scientific knowledge used to solve illicit acts. The increasing use of mobile devices as the main computing platform, in particular smartphones, makes existing information valuable for forensics. However, the blocking mechanisms imposed by the manufacturers and the variety of models and technologies make the task of reconstructing the data for analysis challenging. It is worth mentioning that the conclusion of a case requires more than the simple identification of evidence, as it is extremely important to correlate all the data and sources obtained, to confirm a suspicion or to seek new evidence. This work carries out a systematic review of the literature, identifying the different types of existing image acquisition and the main extraction and encryption methods used in smartphones with the Android operating system.

This paper proposes a cybersecurity maturity model to assess the capabilities of medical organizations to identify their level of maturity, prioritizing privacy and personal data protection. There are problems such as data breaches, the lack of security measures in health information, and the poor capacity of organizations to handle cybersecurity threats that generate concern in the health sector as they seek to mitigate risks in cyberspace. The proposal, based upon C2M2 (Cybersecurity Capability Maturity Model), incorporates practices and controls which allow organizations to identify security gaps generated through cyberattacks on sensitive health patient data. This model seeks to integrate the best practices related to privacy and protection of personal data in the Peruvian legal framework through the Administrative Directive No. 294-MINSA and the personal data protection Act No. 29733. The model consists of 3 evaluation phases. 1. Assessment planning; 2. Execution of the evaluation; 3. Implementation of improvements. The model was validated and tested in a public sector medical organization in Lima, Peru. The preliminary results showed that the organization is at Level 1 with 14% of compliance with established controls, 34% in risk, threat and vulnerability management practices and 19% in supply chain management. These the 3 highest percentages of the 10 evaluated domains.

Due to the increasing complexity of modern hetero-geneous System-on-Chips (SoC) and the growing vulnerabilities, security risk assessment and quantification is required to measure the trustworthiness of a SoC. This paper describes a systematic approach to model the security risk of a system for malicious hardware attacks. The proposed method uses graph analysis to assess the impact of an attack and the Common Vulnerability Scoring System (CVSS) is used to quantify the security level of the system. To demonstrate the applicability of the proposed metric, we consider two open source SoC benchmarks with different architectures. The overall risk is calculated using the proposed metric by computing the exploitability and impact of attack on critical components of a SoC.

Vendor cybersecurity risk assessment is of critical importance to smart city infrastructure and sustainability of the autonomous mobility ecosystem. Lack of engagement in cybersecurity policies and process implementation by the tier companies providing hardware or services to OEMs within this ecosystem poses a significant risk to not only the individual companies but to the ecosystem overall. The proposed quantitative method of estimating cybersecurity risk allows vendors to have visibility to the financial risk associated with potential threats and to consequently allocate adequate resources to cybersecurity. It facilitates faster implementation of defense measures and provides a useful tool in the vendor selection process. The paper focuses on cybersecurity risk assessment as a critical part of the overall company mission to create a sustainable structure for maintaining cybersecurity health. Compound cybersecurity risk and impact on company operations as outputs of this quantitative analysis present a unique opportunity to strategically plan and make informed decisions towards acquiring a reputable position in a sustainable ecosystem. This method provides attack trees and assigns a risk factor to each vendor thus offering a competitive advantage and an insight into the supply chain risk map. This is an innovative way to look at vendor cybersecurity posture. Through a selection of unique industry specific parameters and a modular approach, this risk assessment model can be employed as a tool to navigate the supply base and prevent significant financial cost. It generates synergies within the connected vehicle ecosystem leading to a safe and sustainable economy.