Biblio

Side Channel Attacks (SCAs), an attack that exploits the physical information generated when an encryption algorithm is executed on a device to recover the key, has become one of the key threats to the security of encrypted devices. Recently, with the development of deep learning, deep learning techniques have been applied to SCAs with good results on publicly available dataset experiences. In this paper, we propose a power traces decomposition method that divides the original power traces into two parts, where the data-influenced part is defined as data power traces (Tdata) and the other part is defined as device constant power traces, and use the Tdata for training the network model, which has more obvious advantages than using the original power traces for training the network model. To verify the effectiveness of the approach, we evaluated the ATXmega128D4 microcontroller by capturing the power traces generated when implementing AES-128. Experimental results show that network models trained using Tdata outperform network models trained using raw power traces (Traw ) in terms of classification accuracy, training time, cross-subkey recovery key, and cross-device recovery key.

Image watermarking techniques provides security, reliability copyright protection for various multimedia contents. In this paper Integer Wavelet Transform Schur decomposition and Singular value decomposition (SVD) based image watermarking scheme is suggested for the integrity protection of dicom images. In the proposed technique 3-level Integer wavelet transform (IWT) is subjected into the Dicom ultrasound image of liver cover image and in HH sub-band Schur decomposition is applied. The upper triangular matrix obtained from Schur decomposition of HH sub-band is further processed with SVD to attain the singular values. The X-ray watermark image is pre-processed before embedding into cover image by applying 3-level IWT is applied into it and singular matrix of LL sub-band is embedded. The watermarked image is encrypted using Arnold chaotic encryption for its integrity protection. The performance of suggested scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping, contrast enhancement, gamma correction) and noise (Gaussian, speckle, Salt & Pepper Noise). The proposed technique provides strong robustness against various attacks and chaotic encryption provides integrity to watermarked image.

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.

In the present era of the internet, image watermarking schemes are used to provide content authentication, security and reliability of various multimedia contents. In this paper image watermarking scheme which utilizes the properties of Integer Wavelet Transform (IWT), Schur decomposition and Singular value decomposition (SVD) based is proposed. In the suggested method, the cover image is subjected to a 3-level Integer wavelet transform (IWT), and the HH3 subband is subjected to Schur decomposition. In order to retrieve its singular values, the upper triangular matrix from the HH3 subband’s Schur decomposition is then subjected to SVD. The watermark image is first encrypted using a chaotic map, followed by the application of a 3-level IWT to the encrypted watermark and the usage of singular values of the LL-subband to embed by manipulating the singular values of the processed cover image. The proposed scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping) and noise (Gaussian, Salt & Pepper Noise). The suggested scheme provides strong robustness against numerous attacks and chaotic encryption provides security to watermark.

Cyber-Physical Systems (CPS) are systems that contain digital embedded devices while depending on environmental influences or external configurations. Identifying relevant influences of a CPS as well as modeling dependencies on external influences is difficult. We propose to learn these dependencies with decision trees in combination with clustering. The approach allows to automatically identify relevant influences and receive a data-related explanation of system behavior involving the system's use-case. Our paper presents a case study of our method for a Real-Time Localization System (RTLS) proving the usefulness of our approach, and discusses further applications of a learned decision tree.

Legacy programs are normally monolithic (that is, all code runs in a single process and is not partitioned), and a bug in a program may result in the entire program being vulnerable and therefore untrusted. Program partitioning can be used to separate a program into multiple partitions, so as to isolate sensitive data or privileged operations. Manual program partitioning requires programmers to rewrite the entire source code, which is cumbersome, error-prone, and not generic. Automatic program partitioning tools can separate programs according to the dependency graph constructed based on data or programs. However, programmers still need to manually implement remote service interfaces for inter-partition communication. Therefore, in this paper, we propose AutoSlicer, whose purpose is to partition a program more automatically, so that the programmer is only required to annotate sensitive data. AutoSlicer constructs accurate data dependency graphs (DDGs) by enabling execution flow graphs, and the DDG-based partitioning algorithm can compute partition information based on sensitive annotations. In addition, the code refactoring toolchain can automatically transform the source code into sensitive and insensitive partitions that can be deployed on the remote procedure call framework. The experimental evaluation shows that AutoSlicer can effectively improve the accuracy (13%-27%) of program partitioning by enabling EFG, and separate real-world programs with a relatively smaller performance overhead (0.26%-9.42%).

Third-party libraries with rich functionalities facilitate the fast development of JavaScript software, leading to the explosive growth of the NPM ecosystem. However, it also brings new security threats that vulnerabilities could be introduced through dependencies from third-party libraries. In particular, the threats could be excessively amplified by transitive dependencies. Existing research only considers direct dependencies or reasoning transitive dependencies based on reachability analysis, which neglects the NPM-specific dependency resolution rules as adapted during real installation, resulting in wrongly resolved dependencies. Consequently, further fine-grained analysis, such as precise vulnerability propagation and their evolution over time in dependencies, cannot be carried out precisely at a large scale, as well as deriving ecosystem-wide solutions for vulnerabilities in dependencies. To fill this gap, we propose a knowledge graph-based dependency resolution, which resolves the inner dependency relations of dependencies as trees (i.e., dependency trees), and investigates the security threats from vulnerabilities in dependency trees at a large scale. Specifically, we first construct a complete dependency-vulnerability knowledge graph (DVGraph) that captures the whole NPM ecosystem (over 10 million library versions and 60 million well-resolved dependency relations). Based on it, we propose a novel algorithm (DTResolver) to statically and precisely resolve dependency trees, as well as transitive vulnerability propagation paths, for each package by taking the official dependency resolution rules into account. Based on that, we carry out an ecosystem-wide empirical study on vulnerability propagation and its evolution in dependency trees. Our study unveils lots of useful findings, and we further discuss the lessons learned and solutions for different stakeholders to mitigate the vulnerability impact in NPM based on our findings. For example, we implement a dependency tree based vulnerability remediation method (DTReme) for NPM packages, and receive much better performance than the official tool (npm audit fix).

Steady advancement in Artificial Intelligence (AI) development over recent years has caused AI systems to become more readily adopted across industry and military use-cases globally. As powerful as these algorithms are, there are still gaping questions regarding their security and reliability. Beyond adversarial machine learning, software supply chain vulnerabilities and model backdoor injection exploits are emerging as potential threats to the physical safety of AI reliant CPS such as autonomous vehicles. In this work in progress paper, we introduce the concept of AI supply chain vulnerabilities with a provided proof of concept autonomous exploitation framework. We investigate the viability of algorithm backdoors and software third party library dependencies for applicability into modern AI attack kill chains. We leverage an autonomous vehicle case study for demonstrating the applicability of our offensive methodologies within a realistic AI CPS operating environment.

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection.

To solve the problem of an excessive number of component vulnerabilities and limited defense resources in industrial cyber physical systems, a method for analyzing security critical components of system is proposed. Firstly, the components and vulnerability information in the system are modeled based on SysML block definition diagram. Secondly, as SysML block definition diagram is challenging to support direct analysis, a block security dependency graph model is proposed. On this basis, the transformation rules from SysML block definition graph to block security dependency graph are established according to the structure of block definition graph and its vulnerability information. Then, the calculation method of component security importance is proposed, and a security critical component analysis tool is designed and implemented. Finally, an example of a Drone system is given to illustrate the effectiveness of the proposed method. The application of this method can provide theoretical and technical support for selecting key defense components in the industrial cyber physical system.

Cyber Attack is the most challenging issue all over the world. Nowadays, Cyber-attacks are increasing on digital systems and organizations. Innovation and utilization of new digital technology, infrastructure, connectivity, and dependency on digital strategies are transforming day by day. The cyber threat scope has extended significantly. Currently, attackers are becoming more sophisticated, well-organized, and professional in generating malware programs in Python, C Programming, C++ Programming, Java, SQL, PHP, JavaScript, Ruby etc. Accurate attack modeling techniques provide cyber-attack planning, which can be applied quickly during a different ongoing cyber-attack. This paper aims to create a new cyber-attack model that will extend the existing model, which provides a better understanding of the network’s vulnerabilities.Moreover, It helps protect the company or private network infrastructure from future cyber-attacks. The final goal is to handle cyber-attacks efficacious manner using attack modeling techniques. Nowadays, many organizations, companies, authorities, industries, and individuals have faced cybercrime. To execute attacks using our model where honeypot, the firewall, DMZ and any other security are available in any environment.

Deep learning models rely on single word features and location features of text to achieve good results in text relation extraction tasks. However, previous studies have failed to make full use of semantic information contained in sentence dependency syntax trees, and data sparseness and noise propagation still affect classification models. The BERT(Bidirectional Encoder Representations from Transformers) pretrained language model provides a better representation of natural language processing tasks. And entity enhancement methods have been proved to be effective in relation extraction tasks. Therefore, this paper proposes a combination of the shortest dependency path and entity-enhanced BERT pre-training language model for model construction to reduce the impact of noise terms on the classification model and obtain more semantically expressive feature representation. The algorithm is tested on SemEval-2010 Task 8 English relation extraction dataset, and the F1 value of the final experiment can reach 0. 881.

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.

Unsupervised cross-domain NER task aims to solve the issues when data in a new domain are fully-unlabeled. It leverages labeled data from source domain to predict entities in unlabeled target domain. Since training models on large domain corpus is time-consuming, in this paper, we consider an alternative way by introducing syntactic dependency structure. Such information is more accessible and can be shared between sentences from different domains. We propose a novel framework with dependency-aware GNN (DGNN) to learn these common structures from source domain and adapt them to target domain, alleviating the data scarcity issue and bridging the domain gap. Experimental results show that our method outperforms state-of-the-art methods.

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

This study explores the Critical Success Factors (CSFs) for Security Education, Training and Awareness (SETA) programmes. Data is gathered from 20 key informants (using semi-structured interviews) from various geographic locations including the Gulf nations, Middle East, USA, UK, and Ireland. The analysis of these key informant interviews produces eleven CSFs for SETA programmes. These CSFs are mapped along the phases of a SETA programme lifecycle (design, development, implementation, and evaluation).

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.

Large volumes of private data are gathered, processed, and stored on computers by governments, the military, organizations, financial institutions, colleges, and other enterprises. This data is then sent through networks to other computers. Urgent measures are required to safeguard sensitive personal and company data as well as national security due to the exponential development in number and complexity of cyber- attacks. The essay discusses the characteristics of the Internet and demonstrates how private and financial data can be transmitted over it while still being safeguarded. We show that robbery has spread throughout India and the rest of the world, endangering the global economy and security and giving rise to a variety of cyber-attacks.

In the 21st century, world-leading industries are under the accelerated development of digital transformation. Along with information and data resources becoming more transparent on the Internet, many new network technologies were introduced, but cyber-attack also became a severe problem in cyberspace. Over time, industrial control networks are also forced to join the nodes of the Internet. Therefore, cybersecurity is much more complicated than before, and suffering risk of browsing unknown websites also increases. To practice defenses against cyber-attack effectively, Cyber Range is the best platform to emulate all cyber-attacks and defenses. This article will use VMware virtual machine emulation technology, research cyber range systems under industrial control network architecture, and design and implement an industrial control cyber range system. Using the industrial cyber range to perform vulnerability analyses and exploits on web servers, web applications, and operating systems. The result demonstrates the consequences of the vulnerability attack and raises awareness of cyber security among government, enterprises, education, and other related fields, improving the practical ability to defend against cybersecurity threats.

At the end of the IT Security degree program a simulation game is conducted to repeat and consolidate the core skills of a Bachelor’s graduate. The focus is not on teaching content, but on the application of already learned skills. The scenario shows the students the risks of a completely networked world, which has come to a complete standstill due to a catastrophe. The participants occupy in groups the predefined companies, which are assigned with the reconstruction of the communication infrastructure (the internet). This paper describes the preparation, technical and organizational implementation of the. Also, the most important conclusions drawn by the authors.

There is a stark contrast between the state of cyber security of national infrastructure in Ireland and the efforts underway to support cyber security technologists to work in the country. Notable attacks have recently occurred against the national health service, universities, and various other state bodies, prompting an interest in changing the current situation. This paper presents an overview of the security projects, commercial establishments, and policy in Ireland.

Networked cyber-physical systems must balance the utility of communication for monitoring and control with the risks of revealing private information. Many of these networks, such as wireless communication, are vulnerable to eavesdrop-ping by illegitimate recipients. Obfuscation can hide information from eaves-droppers by ensuring their observations are ambiguous or misleading. At the same time, coordination with recipients can enable them to interpret obfuscated data. In this way, we propose an obfuscation framework for dynamic systems that ensures privacy against eavesdroppers while maintaining utility for legitimate recipients. We consider eavesdroppers unaware of obfuscation by requiring that their observations are consistent with the original system, as well as eaves-droppers aware of the goals of obfuscation by assuming they learn of the specific obfuscation implementation used. We present a method for bounded synthesis of solutions based upon distributed reactive synthesis and the synthesis of publicly-known obfuscators.

Embedded devices are becoming increasingly pervasive in safety-critical systems of the emerging cyber-physical world. While trusted execution environments (TEEs), such as ARM TrustZone, have been widely deployed in mobile platforms, little attention has been given to deployment on real-time cyber-physical systems, which present a different set of challenges compared to mobile applications. For safety-critical cyber-physical systems, such as autonomous drones or automobiles, the current TEE deployment paradigm, which focuses only on confidentiality and integrity, is insufficient. Computation in these systems also needs to be completed in a timely manner (e.g., before the car hits a pedestrian), putting a much stronger emphasis on availability.To bridge this gap, we present RT-TEE, a real-time trusted execution environment. There are three key research challenges. First, RT-TEE bootstraps the ability to ensure availability using a minimal set of hardware primitives on commodity embedded platforms. Second, to balance real-time performance and scheduler complexity, we designed a policy-based event-driven hierarchical scheduler. Third, to mitigate the risks of having device drivers in the secure environment, we designed an I/O reference monitor that leverages software sandboxing and driver debloating to provide fine-grained access control on peripherals while minimizing the trusted computing base (TCB).We implemented prototypes on both ARMv8-A and ARMv8-M platforms. The system is tested on both synthetic tasks and real-life CPS applications. We evaluated rover and plane in simulation and quadcopter both in simulation and with a real drone.

This work-in-progress paper proposes a design methodology that addresses the complexity and heterogeneity of cyber-physical systems (CPS) while simultaneously proving resilient control logic and security properties. The design methodology involves a formal methods-based approach by translating the complex control logic and security properties of a water flow CPS into timed automata. Timed automata are a formal model that describes system behaviors and properties using mathematics-based logic languages with precision. Due to the semantics that are used in developing the formal models, verification techniques, such as theorem proving and model checking, are used to mathematically prove the specifications and security properties of the CPS. This work-in-progress paper aims to highlight the need for formalizing plant models by creating a timed automata of the physical portions of the water flow CPS. Extending the time automata with control logic, network security, and privacy control processes is investigated. The final model will be formally verified to prove the design specifications of the water flow CPS to ensure efficacy and security.

Cyber-physical Systems can be defined as a complex networked control system, which normally develop by combining several physical components with the cyber space. Cyber Physical System are already a part of our daily life. As its already being a part of everyone life, CPS also have great potential security threats and can be vulnerable to various cyber-attacks without showing any sign directly to component failure. To protect user security and privacy is a fundamental concern of any kind of system; either it’s a simple web application or supplicated professional system. Digital Multifactor authentication is one of the best ways to make secure authentication. It covers many different areas of a Cyber-connected world, including online payments, communications, access right management, etc. Most of the time, Multifactor authentication is little complex as it requires extra step from users. This paper will discuss the evolution from single authentication to Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). This paper seeks to analyze and evaluate the most prominent authentication techniques based on accuracy, cost, and feasibility of implementation. We also suggest several authentication schemes which incorporate with Multifactor authentication for CPS.