Biblio

The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.

The increased dissemination of open source software to a broader audience has led to a proportional increase in the dissemination of vulnerabilities. These vulnerabilities are introduced by developers, some intentionally or negligently. In this paper, we work to quantity the relative risk that a given developer represents to a software project. We propose using empirical software engineering based analysis on the vast data made available by GitHub to create a Developer Risk Score (DRS) for prolific contributors on GitHub. The DRS can then be aggregated across a project as a derived vulnerability assessment, we call this the Computational Vulnerability Assessment Score (CVAS). The CVAS represents the correlation between the Developer Risk score across projects and vulnerabilities attributed to those projects. We believe this to be a contribution in trying to quantity risk introduced by specific developers across open source projects. Both of the risk scores, those for contributors and projects, are derived from an amalgamation of data, both from GitHub and outside GitHub. We seek to provide this risk metric as a force multiplier for the project maintainers that are responsible for reviewing code contributions. We hope this will lead to a reduction in the number of introduced vulnerabilities for projects in the Open Source ecosystem.

Cloud computing has become an integral part of medical big data. The cloud has the capability to store the large data volumes has attracted more attention. The integrity and privacy of patient data are some of the issues that cloud-based medical big data should be addressed. This research work introduces data integrity auditing scheme for cloud-based medical big data. This will help minimize the risk of unauthorized access to the data. Multiple copies of the data are stored to ensure that it can be recovered quickly in case of damage. This scheme can also be used to enable doctors to easily track the changes in patients' conditions through a data block. The simulation results proved the effectiveness of the proposed scheme.

In order to gain more benefits from big data, they must be shared, published, analyzed and processed without having any harm or facing any violation and finally get better values from these analytics. The literature reports that this analytics brings an issue of privacy violations. This issue is also protected by law and bring fines to the companies, institutions or individuals. As a result, data collectors avoid to publish or share their big data due to these concerns. In order to obtain plausible solutions, there are a number of techniques to reduce privacy risks and to enable publishing big data while preserving privacy at the same time. These are known as privacy-preserving big data publishing (PPBDP) models. This study presents the privacy problem in big data, evaluates big data components from privacy perspective, privacy risks and protection methods in big data publishing, and reviews existing privacy-preserving big data publishing approaches and anonymization methods in literature. The results were finally evaluated and discussed, and new suggestions were presented.

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.

Aim: Object Detection is one of the latest topics in today’s world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransom ware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.

Power system robustness against high-impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorms is evaluated on the IEEE reliability test system (RTS) by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered discrete characteristics of the system.

Recent scientific results show that audio feedback associated with body movements can be fundamental during the development to learn new spatial concepts [1], [2]. Within the weDraw project [3], [4], we have investigated how this link can be useful to learn mathematical concepts. Here we present a study investigating how mathematical skills changes after multisensory training based on human-computer interaction (RobotAngle and BodyFraction activities). We show that embodied angle and fractions exploration associated with audio and visual feedback can be used in typical children to improve cognition of spatial mathematical concepts. We finally present the exploitation of our results: an online, optimized version of one of the tested activity to be used at school. The training result suggests that audio and visual feedback associated with body movements is informative for spatial learning and reinforces the idea that spatial representation development is based on sensory-motor interactions.

Kerberos protocol is a derivative type of server used for the authentication purpose. Kerberos is a network-based authentication protocol which communicates the tickets from one network to another in a secured manner. Kerberos protocol encrypts the messages and provides mutual authentication. Kerberos uses the symmetric cryptography which uses the public key to strengthen the data confidentiality. The KDS Key Distribution System gives the center of securing the messages. Kerberos has certain disadvantages as it provides public key at both ends. In this proposed approach, the Kerberos are secured by using the HMAC Hash-based Message Authentication Code which is used for the authentication of message for integrity and authentication purpose. It verifies the data by authentication, verifies the e-mail address and message integrity. The computer network and security are authenticated by verifying the user or client. These messages which are transmitted and delivered have to be integrated by authenticating it. Kerberos authentication is used for the verification of a host or user. Authentication is based on the tickets on credentials in a secured way. Kerberos gives faster authentication and uses the unique ticketing system. It supports the authentication delegation with faster efficiency. These encrypt the standard by encrypting the tickets to pass the information.

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.

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.

In this paper we present techniques for enhancing the security of south bound infrastructure in SDN which includes OpenFlow switches and end hosts. In particular, the proposed security techniques have three main goals: (i) validation and secure configuration of flow rules in the OpenFlow switches by trusted SDN controller in the domain; (ii) securing the flows from the end hosts; and (iii) detecting attacks on the switches by malicious entities in the SDN domain. We have implemented the proposed security techniques as an application for ONOS SDN controller. We have also validated our application by detecting various OpenFlow switch specific attacks such as malicious flow rule insertions and modifications in the switches over a mininet emulated network.

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the “best of both worlds,” the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges-and resultant bugs-involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation-the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.

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.

Virtual Private Networks (VPNs) have become a communication medium for accessing information, data exchange and flow of information. Many organizations require Intranet or VPN, for data access, access to servers from computers and sharing different types of data among their offices and users. A secure VPN environment is essential to the organizations to protect the information and their IT infrastructure and their assets. Every organization needs to protect their computer network environment from various malicious cyber threats. This paper presents a comprehensive network security management which includes significant strategies and protective measures during the management of a VPN in an organization. The paper also presents the procedures and necessary counter measures to preserve the security of VPN environment and also discussed few Identified Security Strategies and measures in VPN. It also briefs the Network Security and their Policies Management for implementation by covering security measures in firewall, visualized security profile, role of sandbox for securing network. In addition, a few identified security controls to strengthen the organizational security which are useful in designing a secure, efficient and scalable VPN environment, are also discussed.

We present the new class of non-uniform Rowhammer access patterns that bypass undocumented, proprietary in-DRAM Target Row Refresh (TRR) while operating in a production setting. We show that these patterns trigger bit flips on all 40 DDR4 DRAM devices in our test pool. We make a key observation that all published Rowhammer access patterns always hammer “aggressor” rows uniformly. While uniform accesses maximize the number of aggressor activations, we find that in-DRAM TRR exploits this behavior to catch aggressor rows and refresh neighboring “victims” before they fail. There is no reason, however, to limit Rowhammer attacks to uniform access patterns: smaller technology nodes make underlying DRAM technologies more vulnerable, and significantly fewer accesses are nowadays required to trigger bit flips, making it interesting to investigate less predictable access patterns. The search space for non-uniform access patterns, however, is tremendous. We design experiments to explore this space with respect to the deployed mitigations, highlighting the importance of the order, regularity, and intensity of accessing aggressor rows in non-uniform access patterns. We show how randomizing parameters in the frequency domain captures these aspects and use this insight in the design of Blacksmith, a scalable Rowhammer fuzzer that generates access patterns that hammer aggressor rows with different phases, frequencies, and amplitudes. Blacksmith finds complex patterns that trigger Rowhammer bit flips on all 40 of our recently purchased DDR4 DIMMs, \$2.6 \textbackslashtimes\$ more than state of the art, and generating on average \$87 \textbackslashtimes\$ more bit flips. We also demonstrate the effectiveness of these patterns on Low Power DDR4X devices. Our extensive analysis using Blacksmith further provides new insights on the properties of currently deployed TRR mitigations. We conclude that after almost a decade of research and deployed in-DRAM mitigations, we are perhaps in a worse situation than when Rowhammer was first discovered.

The increasing data generation rate and the proliferation of deep learning applications have led to the development of machine learning-as-a-service (MLaaS) platforms by major Cloud providers. The existing MLaaS platforms, however, fall short in protecting the clients’ private data. Recent distributed MLaaS architectures such as federated learning have also shown to be vulnerable against a range of privacy attacks. Such vulnerabilities motivated the development of privacy-preserving MLaaS techniques, which often use complex cryptographic prim-itives. Such approaches, however, demand abundant computing resources, which undermine the low-latency nature of evolving applications such as autonomous driving.To address these challenges, we propose SCLERA–an efficient MLaaS framework that utilizes trusted execution environment for secure execution of clients’ workloads. SCLERA features a set of optimization techniques to reduce the computational complexity of the offloaded services and achieve low-latency inference. We assessed SCLERA’s efficacy using image/video analytic use cases such as scene detection. Our results show that SCLERA achieves up to 23× speed-up when compared to the baseline secure model execution.

Nowadays Osmotic Computing is emerging as one of the paradigms used to guarantee the Cloud Continuum, and this popularity is strictly related to the capacity to embrace inside it some hot topics like containers, microservices, orchestration and Function as a Service (FaaS). The Osmotic principle is quite simple, it aims to create a federated heterogeneous infrastructure, where an application's components can smoothly move following a concentration rule. In this work, we aim to solve two big constraints of Osmotic Computing related to the incapacity to manage dynamic access rules for accessing the applications inside the Osmotic Infrastructure and the incapacity to keep alive and secure the access to these applications even in presence of network disconnections. For overcoming these limits we designed and implemented a new Osmotic component, that acts as an eventually consistent distributed peer to peer access management system. This new component is used to keep a local Identity and Access Manager (IAM) that permits at any time to access the resource available in an Osmotic node and to update the access rules that allow or deny access to hosted applications. This component has been already integrated inside a Kubernetes based Osmotic Infrastructure and we presented two typical use cases where it can be exploited.

The advancements in technology can be seen in recent years, and people have been adopting the emerging technologies. Though people rely upon these advancements, many loopholes can be seen if you take a particular field, and attackers are thirsty to steal personal data. There has been an increasing number of cyber threats and breaches happening worldwide, primarily for fun or for ransoms. Web servers and sites of the users are being compromised, and they are unaware of the vulnerabilities. Vulnerabilities include OWASP's top vulnerabilities like SQL injection, Cross-site scripting, and so on. To overcome the vulnerabilities and protect the site from getting down, the proposed work includes the implementation of a Web Application Firewall focused on the Application layer of the OSI Model; the product protects the target web applications from the Common OWASP security vulnerabilities. The Application starts analyzing the incoming and outgoing requests generated from the traffic through the pre-built Application Programming Interface. It compares the request and parameter with the algorithm, which has a set of pre-built regex patterns. The outcome of the product is to detect and reject general OWASP security vulnerabilities, helping to secure the user's business and prevent unauthorized access to sensitive data, respectively.

Injection attack is one of the best 10 security dangers declared by OWASP. SQL infusion is one of the main types of attack. In light of their assorted and quick nature, SQL injection can detrimentally affect the line, prompting broken and public data on the site. Therefore, this article presents a profound woodland-based technique for recognizing complex SQL attacks. Research shows that the methodology we use resolves the issue of expanding and debasing the first condition of the woodland. We are currently presenting the AdaBoost profound timberland-based calculation, which utilizes a blunder level to refresh the heaviness of everything in the classification. At the end of the day, various loads are given during the studio as per the effect of the outcomes on various things. Our model can change the size of the tree quickly and take care of numerous issues to stay away from issues. The aftereffects of the review show that the proposed technique performs better compared to the old machine preparing strategy and progressed preparing technique.

A new type of time modulated metamaterial (MTM) antenna array transmitter capable of realizing 2D directional modulation (DM) for physical layer (PHY) security is presented in this work. The proposed 2D DM MTM antenna array is formed by a time modulated corporate feed network loaded with composite right/left-handed (CRLH) leaky wave antennas (LWAs). By properly designing the on-off states of the switch for each antenna feeding branch as well as harnessing the frequency scanning characteristics of CRLH L WAs, 2D DM can be realized to form a PHY secured transmission link in the 2D space. Experimental results demonstrate the bit-error-rate (BER) is low only at a specific 2D angle for the orthogonal frequency-division multiplexing (OFDM) wireless data links.

Wireless security and privacy is gaining a significant interest due to the burgeoning growth of communication devices across the electromagnetic spectrum. In this article, we introduce the concept of the space-time modulated millimeter-wave wireless links enabling physical layer security in highspeed communication links. Such an approach does not require cryptographic key exchanges and enables security in a seamless fashion with no overhead on latency. We show both the design and implementation of such a secure system using custom integrated chips at 71-76 GHz with off-chip packaged antenna array. We also demonstrate the security metric of such a system and analyze the efficacy through distributed eavesdropper attack.

In this paper, we consider a discrete-time stochastic Stackelberg game where there is a defender (also called leader) who has to defend a target and an attacker (also called follower). The attacker has a private type that evolves as a controlled Markov process. The objective is to compute the stochastic Stackelberg equilibrium of the game where defender commits to a strategy. The attacker’s strategy is the best response to the defender strategy and defender’s strategy is optimum given the attacker plays the best response. In general, computing such equilibrium involves solving a fixed-point equation for the whole game. In this paper, we present an algorithm that computes such strategies by solving lower dimensional fixed-point equations for each time t. Based on this algorithm, we compute the Stackelberg equilibrium of a security example.

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.