Biblio

Recent advancements in Deep Neural Networks (DNNs) have enabled widespread deployment in multiple security-sensitive domains. The need for resource-intensive training and the use of valuable domain-specific training data have made these models the top intellectual property (IP) for model owners. One of the major threats to DNN privacy is model extraction attacks where adversaries attempt to steal sensitive information in DNN models. In this work, we propose an advanced model extraction framework DeepSteal that steals DNN weights remotely for the first time with the aid of a memory side-channel attack. Our proposed DeepSteal comprises two key stages. Firstly, we develop a new weight bit information extraction method, called HammerLeak, through adopting the rowhammer-based fault technique as the information leakage vector. HammerLeak leverages several novel system-level techniques tailored for DNN applications to enable fast and efficient weight stealing. Secondly, we propose a novel substitute model training algorithm with Mean Clustering weight penalty, which leverages the partial leaked bit information effectively and generates a substitute prototype of the target victim model. We evaluate the proposed model extraction framework on three popular image datasets (e.g., CIFAR-10/100/GTSRB) and four DNN architectures (e.g., ResNet-18/34/Wide-ResNetNGG-11). The extracted substitute model has successfully achieved more than 90% test accuracy on deep residual networks for the CIFAR-10 dataset. Moreover, our extracted substitute model could also generate effective adversarial input samples to fool the victim model. Notably, it achieves similar performance (i.e., 1-2% test accuracy under attack) as white-box adversarial input attack (e.g., PGD/Trades).

When we setup a computer network, we need to know if an attacker can get into the system. We need to do a series of test that shows the vulnerabilities of the network setup. These series of tests are commonly known Penetration Test. The need for penetration testing was not well known before. This paper highlights how penetration started and how it became as popular as it has today. The internet played a big part into the push to getting the idea of penetration testing started. The styles of penetration testing can vary from physical to network or virtual based testing which either can be a benefit to how a company becomes more secure. This paper presents the steps of penetration testing that a company or organization needs to carry out, to find out their own security flaws.

In order to investigate the effect of island divertor on the peak heat load reduction in a tokamak, a new island divertor was developed and installed in J-TEXT tokamak. The engineering design takes into account the complexity of the device based on the physical design, and also needs to ensure the insulation performance of the coil. Before installing the coil, electromagnetic forces on conductors and thermal conditions were simulated, the electromagnetic force on the magnetic island divertor coil will not cause damage to the coil, and there will be no thermal failure behavior.

The development of industrial robots, as a carrier of artificial intelligence, has played an important role in promoting the popularisation of artificial intelligence super automation technology. The paper introduces the system structure, hardware structure, and software system of the mobile robot climber based on computer big data technology, based on this research background. At the same time, the paper focuses on the climber robot's mechanism compound method and obstacle avoidance control algorithm. Smart home computing focuses on “home” and brings together related peripheral industries to promote smart home services such as smart appliances, home entertainment, home health care, and security monitoring in order to create a safe, secure, energy-efficient, sustainable, and comfortable residential living environment. It's been twenty years. There is still no clear definition of “intelligence at home,” according to Philips Inc., a leading consumer electronics manufacturer, which once stated that intelligence should comprise sensing, connectedness, learning, adaption, and ease of interaction. S mart applications and services are still in the early stages of development, and not all of them can yet exhibit these five intelligent traits.

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values.

The new architecture of transformer networks proposed in the work can be used to create an intelligent chat bot that can learn the process of communication and immediately model responses based on what has been said. The essence of the new mechanism is to divide the information flow into two branches containing the history of the dialogue with different levels of granularity. Such a mechanism makes it possible to build and develop the personality of a dialogue agent in the process of dialogue, that is, to accurately imitate the natural behavior of a person. This gives the interlocutor (client) the feeling of talking to a real person. In addition, making modifications to the structure of such a network makes it possible to identify a likely attack using social engineering methods. The results obtained after training the created system showed the fundamental possibility of using a neural network of a new architecture to generate responses close to natural ones. Possible options for using such neural network dialogue agents in various fields, and, in particular, in information security systems, are considered. Possible options for using such neural network dialogue agents in various fields, and, in particular, in information security systems, are considered. The new technology can be used in social engineering attack detection systems, which is a big problem at present. The novelty and prospects of the proposed architecture of the neural network also lies in the possibility of creating on its basis dialogue systems with a high level of biological plausibility.

Smart cities deploy large numbers of sensors and collect a tremendous amount of data from them. For example, Advanced Metering Infrastructures (AMIs), which consist of physical meters that collect usage data about public utilities such as power and water, are an important building block in a smart city. In a typical sensor network, the measurement devices are connected through a computer network, which exposes them to cyber attacks. Furthermore, the data is centrally managed at the operator’s servers, making it vulnerable to insider threats.Our goal is to protect the integrity of data collected by large-scale sensor networks and the firmware in measurement devices from cyber attacks and insider threats. To this end, we first develop a comprehensive threat model for attacks against data and firmware integrity, which can target any of the stakeholders in the operation of the sensor network. Next, we use our threat model to analyze existing defense mechanisms, including signature checks, remote firmware attestation, anomaly detection, and blockchain-based secure logs. However, the large size of the Trusted Computing Base and a lack of scalability limit the applicability of these existing mechanisms. We propose the Feather-Light Blockchain Infrastructure (FLBI) framework to address these limitations. Our framework leverages a two-layer architecture and cryptographic threshold signature chains to support large networks of low-capacity devices such as meters and data aggregators. We have fully implemented the FLBI’s end-to-end functionality on the Hyperledger Fabric and private Ethereum blockchain platforms. Our experiments show that the FLBI is able to support millions of end devices.

A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. Such rendering bugs critically harm the usability of web browsers as well as web applications. The unique aspect of rendering bugs is that they affect the presented visual appearance of web pages, but those web pages have no pre-defined correct appearance. Therefore, it is challenging to automatically detect errors in their appearance. In practice, web browser vendors rely on non-trivial and time-prohibitive manual analysis to detect and handle rendering regressions. This paper proposes R2Z2, an automated tool to find rendering regressions. R2Z2 uses the differential fuzz testing approach, which repeatedly compares the rendering results of two different versions of a browser while providing the same HTML as input. If the rendering results are different, R2Z2 further performs cross browser compatibility testing to check if the rendering difference is indeed a rendering regression. After identifying a rendering regression, R2Z2 will perform an in-depth analysis to aid in fixing the regression. Specifically, R2Z2 performs a delta-debugging-like analysis to pinpoint the exact browser source code commit causing the regression, as well as inspecting the rendering pipeline stages to pinpoint which pipeline stage is responsible. We implemented a prototype of R2Z2 particularly targeting the Chrome browser. So far, R2Z2 found 11 previously undiscovered rendering regressions in Chrome, all of which were confirmed by the Chrome developers. Importantly, in each case, R2Z2 correctly reported the culprit commit. Moreover, R2Z2 correctly pin-pointed the culprit rendering pipeline stage in all but one case.

HTTP flood DDoS (Distributed Denial of Service) attacks send illegitimate HTTP requests to the targeted site or server. These kinds of attacks corrupt the networks with the help of massive attacking nodes thus blocking incoming traffic. Computer network connected devices are the major source to distributed denial of service attacks (or) botnet attacks. The computer manufacturers rapidly increase the network devices as per the requirement increases in the different environmental needs. Generally the manufacturers cannot ship computer network products with high level security. Those network products require additional security to prevent the DDoS attacks. The present technology is filled with 4G that will impact DDoS attacks. The million DDoS attacks had experienced in every year by companies or individuals. DDoS attack in a network would lead to loss of assets, data and other resources. Purchasing the new equipment and repair of the DDoS attacked network is financially becomes high in the value. The prevention mechanisms like CAPTCHA are now outdated to the bots and which are solved easily by the advanced bots. In the proposed work a secured botnet prevention mechanism provides network security by prevent and mitigate the http flooding based DDoS attack and allow genuine incoming traffic to the application or server in a network environment with the help of integrating invisible challenge and Resource Request Rate algorithms to the application. It offers double security layer to handle malicious bots to prevent and mitigate.

Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create. This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations. SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software.

The purpose of this research work is to develop an approach based on risk management with a view to provide managers and decision-makers with assistance and appropriate guidelines to combine Lean and Green in a successful and integrated way. Risk cannot be managed if not well-identified; hence, a classification of supply chain risks in a Lean Green context was provided. Subsequently to risk identification an approach based on Weighted Product Method (WPM) was proposed; for risk assessment and prioritization, for its ease of use, flexibility and board adaptability. The output of this analysis provides visibility about organization's position toward desired performance and underlines crucial risks to be addressed which marks the starting point of the way to performance improvement. A case study was introduced to demonstrate the applicability and relevance of the developed framework.

5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results.

The increase of autonomy in autonomous surface vehicles development brings along modified and new risks and potential hazards, this in turn, introduces the need for processes and methods for ensuring that systems are acceptable for their intended use with respect to dependability and safety concerns. One approach for evaluating software requirements for claims of safety is to employ an assurance case. Much like a legal case, the assurance case lays out an argument and supporting evidence to provide assurance on the software requirements. This paper analyses safety and security requirements relating to autonomous vessels, and regulations in the automotive industry and the marine industry before proposing a generic cybersecurity and safety assurance case that takes a general graphical approach of Goal Structuring Notation (GSN).

As the nature of the website, the EJBCA digital signatures may have vulnerabilities. The list of web-based vulnerabilities can be found in OWASP's Top 10 2021. Anticipating the attack with an effective and efficient forensics application is necessary. The concept of digital forensic readiness can be applied as a pre-incident plan with a digital forensic lifecycle pipeline to establish an efficient forensic process. Managing digital evidence in the pre-incident plan includes data collection, examination, analysis, and findings report. Based on this concept, we implemented it in designing an information system that carries out the entire flow, provides attack evidence collection, visualization of attack statistics in executive summary, mitigation recommendation, and forensic report generation in a physical form when needed. This research offers an information system that can help the digital forensic process and maintain the integrity of the EJBCA digital signature server web.

The use of digital images is increasingly widespread currently. There is a need for security in digital photos. Cryptography is a technique that can be applied to secure data. In addition to safety, data integrity also needs to be considered to anticipate the image being manipulated. The hash function is a technique that can be used to determine data authentication. In this study, the Rail Fence Cipher and Myszkowski algorithms were used for the encryption and decryption of digital images, as the Secure Hash Algorithm (SHA-256) algorithm. Rail Fence Cipher Algorithm is a transposition algorithm that is quite simple but still vulnerable. It is combined with the Myszkowski Algorithm, which has a high level of complexity with a simple key. Secure Hash Algorithm (SHA-256) is a hash function that accepts an input limit of fewer than 2∧64 bits and produces a fixed hash value of 256 bits. The tested images vary based on image resolution and can be encrypted and decrypted well, with an average MSE value of 4171.16 and an average PSNR value of 11.96 dB. The hash value created is also unique. Keywords—Cryptography, Hash Function, Rail Fence Cipher, Myszkowski, SHA-256, Digital image.

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear.

Mobile small cells that are enabled with Network Coding (NC) are seen as a potentially useful technique for Fifth Generation (5G) networks, since they can cover an entire city and can be put up on demand anywhere, any time, and on any device. Despite numerous advantages, significant security issues arise as a result of the fact that the NC-enabled mobile small cells are vulnerable to attacks. Intrusions are a severe security threat that exploits the inherent vulnerabilities of NC. In order to make NC-enabled mobile small cells to realize their full potential, it is essential to implement intrusion detection systems. When compared to homomorphic signature or hashing systems, homomorphic message authentication codes (MACs) provide safe network coding techniques with relatively smaller overheads. A number of research studies have been conducted with the goal of developing mobile small cells that are enabled with secure network coding and coming up with integrity protocols that are appropriate for such crowded situations. However, the intermediate nodes alter packets while they are in transit and hence the integrity of the data cannot be confirmed by using MACs and checksums. This research study has analyzed numerous intrusion detection models for NC enabled small cells. This research helps the scholars to get a brief idea about various intrusion detection models.

Tactical Data Link (TDL) is one of the important elements in Network Centric Warfare (NCW). TDL provides the means for rapid exchange of tactical information between air, ground, sea units and command centers. In military operations, TDL has high demands for resilience, responsiveness, reliability, availability and security. MANET has characteristics that are suitable for the combat environment, namely the ability to self-form and self-healing so that this network may be applied to the TDL system. To produce high performance in MANET adapted for TDL system, an efficient MAC Protocol method is needed. This paper provides a survey of several MAC Protocol methods on a tactical MANET. In this paper also suggests some improvements to the MANET MAC protocol to improve TDL system performance.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.

Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems.

Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC).

Information exchange occurs all the time in today’s internet era. Some of the data are public, and some are private. Asymmetric cryptography plays a critical role in securing private data transfer. However, technological advances caused private data at risk due to the presence of quantum computers. Therefore, we need a new method for securing private data. This paper proposes combining DNA cryptography methods based on the NTRU cryptosystem to enhance security data confidentiality. This method is compared with conventional public key cryptography methods. The comparison shows that the proposed method has a slow encryption and decryption time compared to other methods except for RSA. However, the key generation time of the proposed method is much faster than other methods tested except for ECC. The proposed method is superior in key generation time and considerably different from other tested methods. Meanwhile, the encryption and decryption time is slower than other methods besides RSA. The test results can get different results based on the programming language used.

Software-Defined Networking (SDN) can be a good option to support Industry 4.0 (4IR) and 5G wireless networks. SDN can also be a secure networking solution that improves the security, capability, and programmability in the networks. In this paper, we present and analyze an SDN-based security architecture for 4IR with 5G. SDN is used for increasing the level of security and reliability of the network by suitably dividing the whole network into data, control, and applications planes. The SDN control layer plays a beneficial role in 4IR with 5G scenarios by managing the data flow properly. We also evaluate the performance of the proposed architecture in terms of key parameters such as data transmission rate and response time.

The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG's roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

Security is a critical aspect in the process of designing, developing, and testing software systems. Due to the increasing need for security-related skills within software systems, there is a growing demand for these skills to be taught in computer science. A series of security modules was developed not only to meet the demand but also to assess the impact of these modules on teaching critical cyber security topics in computer science courses. This full paper in the innovative practice category presents the outcomes of six security modules in a freshman-level course at two institutions. The study adopts a Model-Eliciting Activity (MEA) as a project for students to demonstrate an understanding of the security concepts. Two experimental studies were conducted: 1) Teaching effectiveness of implementing cyber security modules and MEA project, 2) Students’ experiences in conceptual modeling tasks in problem-solving. In measuring the effectiveness of teaching security concepts with the MEA project, students’ performance, attitudes, and interests as well as the instructor’s effectiveness were assessed. For the conceptual modeling tasks in problem-solving, the results of student outcomes were analyzed. After implementing the security modules with the MEA project, students showed a great understanding of cyber security concepts and an increased interest in broader computer science concepts. The instructor’s beliefs about teaching, learning, and assessment shifted from teacher-centered to student-centered during their experience with the security modules and MEA project. Although 64.29% of students’ solutions do not seem suitable for real-world implementation, 76.9% of the developed solutions showed a sufficient degree of creativity.