Biblio

A reliable database of Indicators of Compromise (IoC’s) is a cornerstone of almost every malware detection system. Building the database and keeping it up-to-date is a lengthy and often manual process where each IoC should be manually reviewed and labeled by an analyst. In this paper, we focus on an automatic way of identifying IoC’s intended to save analysts’ time and scale to the volume of network data. We leverage relations of each IoC to other entities on the internet to build a heterogeneous graph. We formulate a classification task on this graph and apply graph neural networks (GNNs) in order to identify malicious domains. Our experiments show that the presented approach provides promising results on the task of identifying high-risk malware as well as legitimate domains classification.

Smart contracts are an attractive aspect of blockchain technology. A smart contract is a piece of executable code that runs on top of the blockchain and is used to facilitate, execute, and enforce agreements between untrustworthy parties without the need for a third party. This paper offers a review of the literature on smart contract applications in intellectual property management. The goal is to look at technology advancements and smart contract deployment in this area. The theoretical foundation of many papers published in recent years is used as a source of theoretical and implementation research for this purpose. According to the literature review we conducted, smart contracts function automatically, control, or document legally significant events and activities in line with the contract agreement's terms. This is a relatively new technology that is projected to deliver solutions for trust, security, and transparency across a variety of areas. An exploratory strategy was used to perform this literature review.

Imbalanced class distribution can cause information loss and missed/false alarms for deep learning and machine-learning algorithms. The detection performance of traditional intrusion detection systems tend to degenerate due to skewed class distribution caused by the uneven allocation of observations in different kinds of attacks. To combat class imbalance and improve network intrusion detection performance, we adopt the conditional generative adversarial network (CTGAN) that enables the generation of samples of specific classes of interest. CTGAN builds on the generative adversarial networks (GAN) architecture to model tabular data and generate high quality synthetic data by conditionally sampling rows from the generated model. Oversampling using CTGAN adds instances to the minority class such that both data in the majority and the minority class are of equal distribution. The generated security alerts are used for training classifiers that realize critical alert detection. The proposed scheme is evaluated on a real-world dataset collected from security operation center of a large enterprise. The experiment results show that detection accuracy can be substantially improved when CTGAN is adopted to produce a balanced security-alert dataset. We believe the proposed CTGAN-based approach can cast new light on building effective systems for critical alert detection with reduced missed/false alarms.

This article proposes a method for determining the intruder type in the systems of physical protection of objects. An intruder trying to enter the territory, buildings or premises of the facility has to overcome typical engineering reinforcement elements of building structures. Elements of building structures are equipped with addressable alarm sensors. The intruder type is proposed to be determined according to its equipment by comparing the time of actually overcoming the building structure elements with the expert estimates. The time to overcome the elements of building structures is estimated by the time between successive responses of the security alarm address sensors. The intruder's awareness of the protection object is proposed to be assessed by tracking the route of its movement on the object using address sensors. Determining the intruder type according to the data of the security alarm systems can be used for the in-process tactics control of the security group actions.

Cyber-Physical Power System (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical Systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks.

With the recent advancements in automated communication technology, many traditional businesses that rely on face-to-face communication have shifted to online portals. However, these online platforms often lack the personal touch essential for customer service. Research has shown that face-to- face communication is essential for building trust and empathy with customers. A multimodal embodied conversation agent (ECA) can fill this void in commercial applications. Such a platform provides tools to understand the user’s mental state by analyzing their verbal and non-verbal behaviour and allows a human-like avatar to take necessary action based on the context of the conversation and as per social norms. However, the literature to understand the impact of ECA agents on commercial applications is limited because of the issues related to platform and scalability. In our work, we discuss some existing work that tries to solve the issues related to scalability and infrastructure. We also provide an overview of the components required for developing ECAs and their deployment in various applications.

Audit trails are critical components in enterprise business applications, typically used for storing, tracking, and auditing data. Entities in the audit trail applications have weak trust boundaries, which expose them to various security risks and attacks. To harden the security and develop secure by design applications, blockchain technology has been recently introduced in the audit trails. Blockchains take a consensus-driven clean slate approach to equip audit trails with secure and transparent data processing, without a trusted intermediary. On a downside, blockchains significantly increase the space-time complexity of the audit trails, leading to high storage costs and low transaction throughput. In this article, we introduce BlockTrail, a novel blockchain architecture that fragments the legacy blockchain systems into layers of codependent hierarchies, thereby reducing the space-time complexity and increasing the throughput. BlockTrail is prototyped on the “practical Byzantine fault tolerance” protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is secure and efficient, with low storage footprint.

CP-ABE (Ciphertext-policy attribute based encryption) is considered as a secure access control for data sharing. However, the SK(secret key) in most CP-ABE scheme is generated by Centralized authority(CA). It could lead to the high cost of building trust and single point of failure. Because of the characters of blockchain, some schemes based on blockchain have been proposed to prevent the disclosure and protect privacy of users' attribute. Thus, a new CP-ABE identity-attribute management(IAM) data sharing scheme is proposed based on blockchain, i.e. IAM-BDSS, to guarantee privacy through the hidden policy and attribute. Meanwhile, we define a transaction structure to ensure the auditability of parameter transmission on blockchain system. The experimental results and security analysis show that our IAM-BDSS is effective and feasible.

Traditional risk assessment process based on knowledge of threat occurrence probability against every system’s asset. One should consider asset placement, applied security measures on asset and network levels, adversary capabilities and so on: all of that has significant influence on probability value. We can measure threat probability by modelling complex attack process. Such process requires creating an attack tree, which consist of elementary attacks against different assets and relations between elementary attacks and impact on influenced assets. However, different attack path may lead to targeted impact – so task of finding optimal attack chain on a given system topology emerges. In this paper method for complex attack graph creation presented, allowing automatic building various attack scenarios for a given system. Assuming that exploits of particular vulnerabilities represent by independent events, we can compute the overall success probability of a complex attack as the product of the success probabilities of exploiting individual vulnerabilities. This assumption makes it possible to use algorithms for finding the shortest paths on a directed graph to find the optimal chain of attacks for a given adversary’s target.

In present research, an empirical model for building and maintaining students' intrinsic motivation to learn is proposed. Unlike many other models of motivation, this model is not based on psychological theories but is derived directly from empirical observations made by experienced learners and educators. Thanks to empirical nature of the proposed model, its application to educational practice may be more straightforward in comparison with assumptions-based motivation theories. Interestingly, the structure of the proposed model resembles to some extent the structure of the oscillator circuit containing an amplifier and a positive feedback loop.

With the development of 5G networking technology on the Internet of Vehicle (IoV), there are new opportunities for numerous cyber-attacks, such as in-vehicle attacks like hijacking occurrences and data theft. While numerous attempts have been made to protect against the potential attacks, there are still many unsolved problems such as developing a fine-grained access control system. This is reflected by the granularity of security as well as the related data that are hosted on these platforms. Among the most notable trends is the increased usage of smart devices, IoV, cloud services, emerging technologies aim at accessing, storing and processing data. Most popular authentication protocols rely on knowledge-factor for authentication that is infamously known to be vulnerable to subversions. Recently, the zero-trust framework has drawn huge attention; there is an urgent need to develop further the existing Continuous Authentication (CA) technique to achieve the zero-trustiness framework. In this paper, firstly, we develop the static authentication process and propose a secured protocol to generate the smart key for user to unlock the vehicle. Then, we proposed a novel and secure continuous authentication system for IoVs. We present the proof-of-concept of our CA scheme by building a prototype that leverages the commodity fingerprint sensors, NFC, and smartphone. Our evaluations in real-world settings demonstrate the appropriateness of CA scheme and security analysis of our proposed protocol for digital key suggests its enhanced security against the known attack-vector.

Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding ethical bias in models rather than fixing it. After finding bias, the next step is mitigation. Prior researchers mainly tried to use supervised approaches to achieve fairness. However, in the real world, getting data with trustworthy ground truth is challenging and also ground truth can contain human bias. Semi-supervised learning is a technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of data (and then all that data is used for model training). In this work, we apply four popular semi-supervised techniques as pseudo-labelers to create fair classification models. Our framework, Fair-SSL, takes a very small amount (10%) of labeled data as input and generates pseudo-labels for the unlabeled data. We then synthetically generate new data points to balance the training data based on class and protected attribute as proposed by Chakraborty et al. in FSE 2021. Finally, classification model is trained on the balanced pseudo-labeled data and validated on test data. After experimenting on ten datasets and three learners, we find that Fair-SSL achieves similar performance as three state-of-the-art bias mitigation algorithms. That said, the clear advantage of Fair-SSL is that it requires only 10% of the labeled training data. To the best of our knowledge, this is the first SE work where semi-supervised techniques are used to fight against ethical bias in SE ML models. To facilitate open science and replication, all our source code and datasets are publicly available at https://github.com/joymallyac/FairSSL. CCS CONCEPTS • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning. ACM Reference Format: Joymallya Chakraborty, Suvodeep Majumder, and Huy Tu. 2022. Fair-SSL: Building fair ML Software with less data. In International Workshop on Equitable Data and Technology (FairWare ‘22), May 9, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3524491.3527305

Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JUCIFY approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JUCIFY builds on the Soot internal intermediate representation. We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are “unreachable” in apps' callgraphs, both in goodware and malware. Using JUCIFY, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JUCIFY'S model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JUCIFY we can find sensitive data leaks that pass through native code.

With the gradual construction and implementation of cloud computing, the information security problem of the smart grid has surfaced. Therefore, in the construction of the smart grid cloud computing platform, information security needs to be considered in planning, infrastructure, and management at the same time, and it is imminent to build an information network that is secure from terminal to the platform to data. This paper introduces the concept of cloud security technology and the latest development of cloud security technology and discusses the main strategies of cloud security construction in electric power enterprises.

We continue to tackle the problem of poorly defined security metrics by building on and improving our previous work on designing sound security metrics. We reformulate the previous method into a set of conditions that are clearer and more widely applicable for deriving sound security metrics. We also modify and enhance some concepts that led to an unforeseen weakness in the previous method that was subsequently found by users, thereby eliminating this weakness from the conditions. We present examples showing how the conditions can be used to obtain sound security metrics. To demonstrate the conditions' versatility, we apply them to show that an aggregate security metric made up of sound security metrics is also sound. This is useful where the use of an aggregate measure may be preferred, to more easily understand the security of a system.

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.

Electrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model’s behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.

This paper continues analysis of approaches of IT risk assessment and management in modern IT management frameworks. Building on systematicity principles and the review of concepts of risk and methods of risk analysis in the frameworks, we discuss applicability of the methods for business decision-making in the real world and propose ways to their improvement.

Windows Communication Foundation (WCF) is a communication framework for building connected, service-oriented applications, initially released by Microsoft as part of.NET Framework, but now open source. The WCF message-based communication is a very popular solution used for sending asynchronous messages from one service endpoint to another. Because WCF provides many functionalities it has a large-consuming development model and often the security measures implemented in applications are not proper. In this study we propose a methodology for offensive security analysis of an WCF endpoint or service, from red team perspective. A step by step approach, empirical information, and detailed analysis report of WCF vulnerabilities are presented. We conclude by proposing recommendations for mitigating attacks and securing endpoints.

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform “Casper” to address the issues on centralized identity systems. “Casper ” is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers’ mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms’ Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.

With the construction of power information network, the power grid has built a security protection system based on boundary protection. However, with the continuous advancement of the construction of the power Internet of Things, a large number of power Internet of Things terminals need to connect to the power information network through the public network, which have an impact on the existing security protection system of the power grid. This article analyzes the characteristics of the border protection model commonly used in network security protection. Aiming at the lack of security protection capabilities of this model, a zero-trust security architecture-based power Internet of Things network security protection model is proposed. Finally, this article analyzes and studies the application of zero trust in the power Internet of Things.

Non-interference is a program property that ensures the absence of information leaks. In the context of programming languages, there exist two common approaches for establishing non-interference: type systems and program logics. Type systems provide strong automation (by means of type checking), but they are inherently restrictive in the kind of programs they support. Program logics support challenging programs, but they typically require significant human assistance, and cannot handle modules or higher-order programs.To connect these two approaches, we present SeLoC—a separation logic for non-interference, on top of which we build a type system using the technique of logical relations. By building a type system on top of separation logic, we can compositionally verify programs that consist of typed and untyped parts. The former parts are verified through type checking, while the latter parts are verified through manual proof.The core technical contribution of SeLoC is a relational form of weakest preconditions that can track information flow using separation logic resources. SeLoC is fully machine-checked, and built on top of the Iris framework for concurrent separation logic in Coq. The integration with Iris provides seamless support for fine-grained concurrency, which was beyond the reach of prior type systems and program logics for non-interference.

In this work an overview of basic approaches to choosing protective measures for automated process control systems is done. The aim of the research was to develop a method for choosing protection measures for information security at every APCs level using set theory within analysis of basic sets of protection measures. In the framework of the research relevant attacks on industrial infrastructure are considered, an algorithm of choosing APCs protective measures is constructed, and it is suggested that it is required to use protective measures for every system level in accordance with an individual assessment of data protection class at the corresponding level. The authors concluded that it is necessary to exclude from consideration “specification of an adapted basic set” of the algorithm for choosing APCs protection measures in case the adapted basic set of APCs protective measures provides blocking all security threats at the considered system level. The approach to choosing protection measures based on building Euler-Venn diagrams is suggested. The results of the research are recommended to be used when modeling information security threats and developing requirements for APCs information protection means.

Current permissionless cryptocurrencies such as Bitcoin suffer from a limited transaction rate and slow confirmation time, which hinders further adoption. Payment channels are one of the most promising solutions to address these problems, as they allow the parties of the channel to perform arbitrarily many payments in a peer-to-peer fashion while uploading only two transactions on the blockchain. This concept has been generalized into payment channel networks where a path of payment channels is used to settle the payment between two users that might not share a direct channel between them. However, this approach requires the active involvement of each user in the path, making the system less reliable (they might be offline), more expensive (they charge fees per payment), and slower (intermediaries need to be actively involved in the payment). To mitigate this issue, recent work has introduced the concept of virtual channels (IEEE S&P’19), which involve intermediaries only in the initial creation of a bridge between payer and payee, who can later on independently perform arbitrarily many off-chain transactions. Unfortunately, existing constructions are only available for Ethereum, as they rely on its account model and Turing-complete scripting language. The realization of virtual channels in other blockchain technologies with limited scripting capabilities, like Bitcoin, was so far considered an open challenge.In this work, we present the first virtual channel protocols that are built on the UTXO-model and require a scripting language supporting only a digital signature scheme and a timelock functionality, being thus backward compatible with virtually every cryptocurrency, including Bitcoin. We formalize the security properties of virtual channels as an ideal functionality in the Universal Composability framework and prove that our protocol constitutes a secure realization thereof. We have prototyped and evaluated our protocol on the Bitcoin blockchain, demonstrating its efficiency: for n sequential payments, they require an off-chain exchange of 9+2n transactions or a total of 3524+695n bytes, with no on-chain footprint in the optimistic case. This is a substantial improvement compared to routing payments in a payment channel network, which requires 8n transactions with a total of 3026n bytes to be exchanged.

Quantum computing is poised to be the innovation driver of the next decade. Its information processing capabilities will radically accelerate drug discovery, improve online security, or even boost artificial intelligence [1]. Building a quantum computer promises to have a major positive impact in society, however building the hardware that will enable that paradigm change its one of the greatest technological challenges for humanity.