Biblio

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.

Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA’s high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.

At present, the security protection of distribution automation system is faced with complex and diverse operating environment, and the main use of public network may bring greater security risks, there are still some deficiencies. According to the actual situation of distribution automation of China Southern Power Grid, this paper designs multiple protection technology, carries out encryption distribution terminal research, and realizes end-to-end longitudinal security protection of distribution automation system, which is effectively improving the anti-attack ability of distribution terminal.

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.

This position paper presents and illustrates the concept of security requirements as code – a novel approach to security requirements specification. The aspiration to minimize code duplication and maximize its reuse has always been driving the evolution of software development approaches. Object-Oriented programming (OOP) takes these approaches to the state in which the resulting code conceptually maps to the problem that the code is supposed to solve. People nowadays start learning to program in the primary school. On the other hand, requirements engineers still heavily rely on natural language based techniques to specify requirements. The key idea of this paper is: artifacts produced by the requirements process should be treated as input to the regular object-oriented analysis. Therefore, the contribution of this paper is the presentation of the major concepts for the security requirements as the code method that is illustrated with a real industry example from the VeriDevOps project.

Information system security is very important and very complicated, security is to prevent potential crisis. To detect both from external invasion behavior, also want to check the internal unauthorized behavior. Presented here ABHIDS hybrid intrusion detection system model, designed a component Agent, controller, storage, filter, manager component (database), puts forward a new detecting DDoS attacks (trinoo) algorithm and the implementation. ABHIDS adopts object-oriented design method, a study on intrusion detection can be used as a working mechanism of the algorithms and test verification platform.

Internet Exchange Points (IXPs) are critical components of the Internet infrastructure that affect its performance, evolution, security and economy. In this work, we introduce a technique to improve the well-known TraIXroute tool with its ability to identify IXPs. TraIXroute is a tool written in python3. It always encounters problems during its installation by network administrators and researchers. This problem remains unchanged in the field of internet ixp measurement tools. Our paper aims to make a critical analysis of TraIXroute tool which has some malfunctions. Furthermore, our main objective is to implement an improved tool for detecting ixps on the traceroute path with ipv4 and ipv6. The tool will have options for Geolocation of ixps as well as ASs. Our tool is written in C\# (C sharp) and python which are object oriented programming languages.

In the last decade the use of artificial neural networks (ANNs) in many fields like image processing or speech recognition has become a common practice because of their effectiveness to solve complex tasks. However, in such a rush, very little attention has been paid to security aspects. In this work we explore the possibility to embed a watermark into the ANN parameters. We exploit model redundancy and adaptation capacity to lock a subset of its parameters to carry the watermark sequence. The watermark can be extracted in a simple way to claim copyright on models but can be very easily attacked with model fine-tuning. To tackle this culprit we devise a novel watermark aware training strategy. We aim at delving into the loss landscape to find an optimal configuration of the parameters such that we are robust to fine-tuning attacks towards the watermarked parameters. Our experimental results on classical ANN models trained on well-known MNIST and CIFAR-10 datasets show that the proposed approach makes the embedded watermark robust to fine-tuning and compression attacks.

The ever-increasing cost and complexity of cutting-edge manufacturing and test processes have migrated the semiconductor industry towards a globalized business model. With many untrusted entities involved in the supply chain located across the globe, original intellectual property (IP) owners face threats such as IP theft/piracy, tampering, counterfeiting, reverse engineering, and overproduction. Logic locking has emerged as a promising solution to protect integrated circuits (ICs) against supply chain vulnerabilities. It inserts key gates to corrupt circuit functionality for incorrect key inputs. A logic-locked chip test can be performed either before or after chip activation (becoming unlocked) by loading the unlocking key into the on-chip tamperproof memory. However, both pre-activation and post-activation tests suffer from lower test coverage, higher test cost, and critical security vulnerabilities. To address the shortcomings, we propose LL-ATPG, a logic-locking aware test method that applies a set of valet (dummy) keys based on a target test coverage to perform manufacturing test in an untrusted environment. LL-ATPG achieves high test coverage and minimizes test time overhead when testing the logic-locked chip before activation without sharing the unlocking key. We perform security analysis of LL-ATPG and experimentally demonstrate that sharing the valet keys with the untrusted foundry does not create additional vulnerability for the underlying locking method.

Due to the large number of terminal nodes and wide deployment of power IOT, it is vulnerable to attacks such as physical hijacking, communication link theft and replay. In order to sense and measure the security risks and threats of massive power IOT terminals in real time, a security threat assessment for power IOT terminals based on knowledge graph was proposed. Firstly, the basic data, operation data and alarm threat data of power IOT terminal equipment are extracted and correlated, and the power IOT terminal based on knowledge graph is constructed. Then, the real-time monitoring data of the power IOT terminal is preprocessed. Based on the knowledge graph of the power IOT terminal, the safety analysis and operation analysis of the terminal are carried out, and the threat index of the power IOT terminal is perceived in real time. Finally, security operation and maintenance personnel make disposal decisions on the terminals according to the threat index of power IOT terminals to ensure the safe and stable operation of power IOT terminal nodes. The experimental results show that compared with the traditional IPS, the method can effectively detect the security threat of the power IOT terminal and reduce the alarm vulnerability rate.

The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57- and 118-bus test system, outperforming relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.

With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in.

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.

Recommender systems, which aim to suggest personalized lists of items for users, have drawn a lot of attention. In fact, many of these state-of-the-art recommender systems have been built on deep neural networks (DNNs). Recent studies have shown that these deep neural networks are vulnerable to attacks, such as data poisoning, which generate fake users to promote a selected set of items. Correspondingly, effective defense strategies have been developed to detect these generated users with fake profiles. Thus, new strategies of creating more ‘realistic’ user profiles to promote a set of items should be investigated to further understand the vulnerability of DNNs based recommender systems. In this work, we present a novel framework CopyAttack. It is a reinforcement learning based black-box attacking method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items. CopyAttack is constructed to both efficiently and effectively learn policy gradient networks that first select, then further refine/craft user profiles from the source domain, and ultimately copy them into the target domain. CopyAttack’s goal is to maximize the hit ratio of the targeted items in the Top-k recommendation list of the users in the target domain. We conducted experiments on two real-world datasets and empirically verified the effectiveness of the proposed framework. The implementation of CopyAttack is available at https://github.com/wenqifan03/CopyAttack.

A new method for the detection of ransomware in an infected host is described and evaluated. The method utilizes data streams from on-board sensors to fingerprint the initiation of a ransomware infection. These sensor streams, which are common in modern computing systems, are used as a side channel for understanding the state of the system. It is shown that ransomware detection can be achieved in a rapid manner and that the use of slight, yet distinguishable changes in the physical state of a system as derived from a machine learning predictive model is an effective technique. A feature vector, consisting of various sensor outputs, is coupled with a detection criteria to predict the binary state of ransomware present versus normal operation. An advantage of this approach is that previously unknown or zero-day version s of ransomware are vulnerable to this detection method since no apriori knowledge of the malware characteristics are required. Experiments are carried out with a variety of different system loads and with different encryption methods used during a ransomware attack. Two test systems were utilized with one having a relatively low amount of available sensor data and the other having a relatively high amount of available sensor data. The average time for attack detection in the "sensor-rich" system was 7.79 seconds with an average Matthews correlation coefficient of 0.8905 for binary system state predictions regardless of encryption method and system load. The model flagged all attacks tested.

The current supply of a highly specialized cyber security professionals cannot meet the demands for societies seeking digitization. To close the skill gap, there is a need for introducing students in higher education to cyber security, and to combine theoretical knowledge with practical skills. This paper presents how the cyber security training platform Haaukins, initially developed to increase interest and knowledge of cyber security among high school students, was further developed to support the need for training in higher education. Based on the differences between the existing and new target audiences, a set of design principles were derived which shaped the technical adjustments required to provide a suitable platform - mainly related to dynamic tooling, centralized access to exercises, and scalability of the platform to support courses running over longer periods of time. The implementation of these adjustments has led to a series of teaching sessions in various institutions of higher education, demonstrating the viability for Haaukins for the new target audience.

Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.

Cyber ranges are valuable assets but have limitations in simulating complex realities and multi-sector dependencies; to address this, federated cyber ranges are emerging. This work presents the ECHO Federated Cyber Range, a marketplace for cyber range services, that establishes a mechanism by which independent cyber range capabilities can be interconnected and accessed via a convenient portal. This allows for more complex and complete emulations, spanning potentially multiple sectors and complex exercises. Moreover, it supports a semi-automated approach for processing and deploying service requests to assist customers and providers interfacing with the marketplace. Its features and architecture are described in detail, along with the design, validation and deployment of a training scenario.

In-browser cryptomining uses the computational power of a website's visitors to mine cryptocurrency, i.e., to create new coins. With the rise of ready-to-use mining scripts distributed by service providers (e.g., Coinhive), it has become trivial to turn a website into a cryptominer by copying and pasting the mining script. Both legitimate webpage owners who want to raise an extra revenue under users' explicit consent and malicious actors who wish to exploit the computational power of the users' computers without their consent have started to utilize this emerging paradigm of cryptocurrency operations. In-browser cryptomining, though mostly abused by malicious actors in practice, is indeed a promising funding model that can be utilized by website owners, publishers, or non-profit organizations for legitimate business purposes, such as to collect revenue or donations for humanitarian projects, inter alia. However, our analysis in this paper shows that in practice, regardless of their being legitimate or not, all in-browser mining scripts are treated the same as malicious cryptomining samples (aka cryptojacking) and blacklisted by browser extensions or antivirus programs. Indeed, there is a need for a better understanding of the in-browser cryptomining ecosystem. Hence, in this paper, we present an in-depth empirical analysis of in-browser cryptomining processes, focusing on the samples explicitly asking for user consent, which we call permissioned cryptomining. To the best of our knowledge, this is the first study focusing on the permissioned cryptomining samples. For this, we created a dataset of 6269 unique web sites containing cryptomining scripts in their source codes to characterize the in-browser cryptomining ecosystem by differentiating permissioned and permissionless cryptomining samples. We believe that (1) this paper is the first attempt showing that permissioned in-browser cryptomining could be a legitimate and viable monetization tool if implemented responsibly and without interrupting the user, and (2) this paper will catalyze the widespread adoption of legitimate crvptominina with user consent and awareness.

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryp-tocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through cryptojacking malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging crypto-jacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features. However, the literature lacks a systemic study with a deep understanding of the emerging cryptojacking malware and a comprehensive review of studies in the literature. To fill this gap in the literature, in this SoK paper, we present a systematic overview of cryptojacking malware based on the information obtained from the combination of academic research papers, two large cryptojacking datasets of samples, and 45 major attack instances. Finally, we also present lessons learned and new research directions to help the research community in this emerging area.

As the traditional grids are transitioning to the smart grid, they are getting more prone to cyber-attacks. Among all the cyber-attack one of the most dangerous attack is false data injection attack. When this attack is performed with historical information of the data packet the attack goes undetected. As the false data is included for training and testing the model, the accuracy is decreased, and decision making is affected. In this paper we analyzed the impact of the false data injection attack(FDIA) on AI based smart grid. These analyses were performed using two different multi-layer perceptron architectures with one of the independent variables being compared and modified by the attacker. The root-mean squared values were compared with different models.

Deploying a new network architecture in the Internet requires changing some, but not necessarily all elements between communicating applications. One way to achieve gradual deployment is a proxy or gateway which "translates" between the new architecture and TCP/IP. We present such a proxy, called "Performance Enhancing Proxy for Deploying Network Architectures (PEP-DNA)", which allows TCP/IP applications to benefit from advanced features of a new network architecture without having to be redeveloped. Our proxy is a kernel-based Linux implementation which can be installed wherever a translation needs to occur between a new architecture and TCP/IP domains. We discuss the proxy operation in detail and evaluate its efficiency and performance in a local testbed, demonstrating that it achieves high throughput with low additional latency overhead. In our experiments, we use the Recursive InterNetwork Architecture (RINA) and Information-Centric Networking (ICN) as examples, but our proxy is modular and flexible, and hence enables realistic gradual deployment of any new "clean-slate" approaches.

This paper studies the physical layer security of four reactive relay selection methods (optimum relay selection, opportunist relay selection enhancement, suboptimal relay selection enhancement and partial relay selection enhancement) in a cooperative cognitive radio network including one pair of primary users, one eavesdropper, multiple relays and secondary users with perfect and imperfect channel state information (CSI) at receivers. In addition, we consider existing a direct link from a secondary source (S) to secondary destination receivers (D) and eavesdroppers (E). The secrecy outage probability, outage probability, intercept probability and reliability are calculated to verify the four relay selection methods with the fading channels by using Monte Carlo simulation. The results show that the loss of secrecy outage probability when remaining direct links from S to D and S to E. Additionally, the results also show that the trade-off between secrecy outage probability and the intercept probability and the optimum relay selection method outperforms other methods.

Physical-layer security (PLS) for an underlay cognitive radio network (CRN)-based simultaneous wireless information and power transfer (SWIPT) over cascaded κ-µ fading channels is investigated. The network is composed of a pair of secondary users (SUs), a primary user (PU) receiver, and an eavesdropper attempting to intercept the data shared by the SUs. To improve the SUs’ data transmission security, we assume a full-duplex (FD) SU destination, which employs energy harvesting (EH) to extract the power required for generating jamming signals to be emitted to confound the eavesdropper. Two scenarios are presented and compared; harvesting and non-harvesting eavesdropper. Moreover, a trade-off between the system’s secrecy and reliability is explored. PLS is studied in terms of the probability of non-zero secrecy capacity and the intercept probability, whereas the reliability is studied in terms of the outage probability. Results reveal the great impact of jamming over the improvement of the SUs’ secrecy. Additionally, our work indicates that studying the system’s secrecy over cascaded channels has an influence on the system’s PLS that cannot be neglected.