Biblio

This paper proposes a Time Difference of Arrival (TDoA) based method for the localization of Radio Frequency (RF) emitters working at different carriers, by using wideband spectrum sensors exploiting compressive sampling. The proposed measurement method is based on four or more RF receivers, with known Cartesian positions, performing non uniform sampling on the received signal. By means of simulations, the method has been compared against a localization method adopting RF receivers performing uniform sampling at Nyquist rate. The obtained preliminary results demonstrate that the method is capable of localizing two RF emitters achieving the same results obtained with uniform sampling, with a compression ratio up to CR = 20.

A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.

The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

IoT data marketplaces are being developed to help cities and communities create large scale IoT applications. Such data marketplaces let the IoT device owners sell their data to the application developers. Following this application development model, the application developers need not deploy their own IoT devices when developing IoT applications; instead, they can buy data from a data marketplace. In a marketplace-based IoT application, the application developers are making critical business and operation decisions using the data produced by seller's IoT devices. Under these circumstances, it is crucial to verify and validate the security of IoT devices.In this paper, we assess the security of IoT data marketplaces. In particular, we discuss what kind of vulnerabilities exist in IoT data marketplaces using the well-known STRIDE model, and present a security assessment and certification framework for IoT data marketplaces to help the device owners to examine the security vulnerabilities of their devices. Most importantly, our solution certifies the IoT devices when they connect to the data marketplace, which helps the application developers to make an informed decision when buying and consuming data from a data marketplace. To demonstrate the effectiveness of the proposed approach, we have developed a proof-of-concept using I3 (Intelligent IoT Integrator), which is an open-source IoT data marketplace developed at the University of Southern California, and IoTcube, which is a vulnerability detection toolkit developed by researchers at Korea University. Through this work, we show that it is possible to increase the reliability of a IoT data marketplace while not damaging the convenience of the users.

Increased peripheral performance is causing strain on the memory subsystem of modern processors. For example, available DRAM throughput can no longer sustain the traffic of a modern network card. Scrambling to deliver the promised performance, instead of transferring peripheral data to and from DRAM, modern Intel processors perform I/O operations directly on the Last Level Cache (LLC). While Direct Cache Access (DCA) instead of Direct Memory Access (DMA) is a sensible performance optimization, it is unfortunately implemented without care for security, as the LLC is now shared between the CPU and all the attached devices, including the network card.In this paper, we reverse engineer the behavior of DCA, widely referred to as Data-Direct I/O (DDIO), on recent Intel processors and present its first security analysis. Based on our analysis, we present NetCAT, the first Network-based PRIME+PROBE Cache Attack on the processor's LLC of a remote machine. We show that NetCAT not only enables attacks in cooperative settings where an attacker can build a covert channel between a network client and a sandboxed server process (without network), but more worryingly, in general adversarial settings. In such settings, NetCAT can enable disclosure of network timing-based sensitive information. As an example, we show a keystroke timing attack on a victim SSH connection belonging to another client on the target server. Our results should caution processor vendors against unsupervised sharing of (additional) microarchitectural components with peripherals exposed to malicious input.

This paper studies the physical layer security (PLS) of a vehicular network employing a reconfigurable intelligent surface (RIS). RIS technologies are emerging as an important paradigm for the realisation of smart radio environments, where large numbers of small, low-cost and passive elements, reflect the incident signal with an adjustable phase shift without requiring a dedicated energy source. Inspired by the promising potential of RIS-based transmission, we investigate two vehicular network system models: One with vehicle-to-vehicle communication with the source employing a RIS-based access point, and the other model in the form of a vehicular adhoc network (VANET), with a RIS-based relay deployed on a building. Both models assume the presence of an eavesdropper to investigate the average secrecy capacity of the considered systems. Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the secrecy capacity is affected by the location of the RIS-relay and the number of RIS cells. The effect of other system parameters such as source power and eavesdropper distances are also studied.

The root causes of many security vulnerabilities include a pernicious combination of two problems, often regarded as inescapable aspects of computing. First, the protection mechanisms provided by the mainstream processor architecture and C/C++ language abstractions, dating back to the 1970s and before, provide only coarse-grain virtual-memory-based protection. Second, mainstream system engineering relies almost exclusively on test-and-debug methods, with (at best) prose specifications. These methods have historically sufficed commercially for much of the computer industry, but they fail to prevent large numbers of exploitable bugs, and the security problems that this causes are becoming ever more acute.In this paper we show how more rigorous engineering methods can be applied to the development of a new security-enhanced processor architecture, with its accompanying hardware implementation and software stack. We use formal models of the complete instruction-set architecture (ISA) at the heart of the design and engineering process, both in lightweight ways that support and improve normal engineering practice - as documentation, in emulators used as a test oracle for hardware and for running software, and for test generation - and for formal verification. We formalise key intended security properties of the design, and establish that these hold with mechanised proof. This is for the same complete ISA models (complete enough to boot operating systems), without idealisation.We do this for CHERI, an architecture with hardware capabilities that supports fine-grained memory protection and scalable secure compartmentalisation, while offering a smooth adoption path for existing software. CHERI is a maturing research architecture, developed since 2010, with work now underway on an Arm industrial prototype to explore its possible adoption in mass-market commercial processors. The rigorous engineering work described here has been an integral part of its development to date, enabling more rapid and confident experimentation, and boosting confidence in the design.

The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.

Blackhole (BH) attacks are one of the most serious threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks are widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a detection-and-prevention method that uses local information shared with neighboring nodes. Our experiments show that the proposed method successfully detects and contains even smart BH threats. Consequently, the attack success rate decreases.

Adaptation of e-commerce in third world countries requires more secure computing facilities. Online data is vulnerable and susceptible to active attacks. Hundreds of security mechanisms and services have been proposed to curb this challenge. However, available security mechanisms, sufficiently strong, are heavy for the machines used. To secure online data where machines' processing power and memory are deficient, a Hybrid Encryption Standard (HES) is proposed. The HES is built on the Data Encryption Standard (DES) algorithm and its siblings. The component units of the DES are redesigned towards reduced demands for processing power and memory. Precisely, white box designs of IP tables, PC tables, Expansion tables, Rotation tables, S-boxes and P-boxes are proposed, all aimed at reducing the processing time and memory demands. Evaluation of the performance of the HES algorithm against the performance of the traditional DES algorithm reveal that the HES out-performs the DES with regards to speed, memory demands, and general acceptance by novice practitioners in the cryptography field. In addition, reproducibility and flexibility are attractive features of the HES over the DES.

Distributed training is a novel approach to accelerating training of Deep Neural Networks (DNN), but common training libraries fall short of addressing the distributed nature of heterogeneous processors or interruption by other workloads on the shared processing nodes. This paper describes distributed training of DNN on computational storage devices (CSD), which are NAND flash-based, high-capacity data storage with internal processing engines. A CSD-based distributed architecture incorporates the advantages of federated learning in terms of performance scalability, resiliency, and data privacy by eliminating the unnecessary data movement between the storage device and the host processor. The paper also describes Stannis, a DNN training framework that improves on the shortcomings of existing distributed training frameworks by dynamically tuning the training hyperparameters in heterogeneous systems to maintain the maximum overall processing speed in term of processed images per second and energy efficiency. Experimental results on image classification training benchmarks show up to 3.1x improvement in performance and 2.45x reduction in energy consumption when using Stannis plus CSD compare to the generic systems.

Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.

The wide adoption of Bloom filters makes their security an important issue to be addressed. For example, an attacker can increase their error rate through polluting and eventually saturating the filter by inserting elements that set to one a large number of positions in the filter. This is known as a pollution attack and requires that the attacker knows the hash functions used to construct the filter. Such information is not available in many practical settings and in addition a simple protection can be achieved through using a random salt in the hash functions. The same pollution attacks can also be done to counting Bloom filters that in addition to insertions and lookups support removals. This paper considers pollution attacks on counting Bloom filters. We describe two novel pollution attacks that do not require any knowledge of the counting Bloom filter implementation details and evaluate them. These methods show that a counting Bloom filter is vulnerable to pollution attacks even when the attacker has only access to the filter as a black box to perform insertions, removals, and lookups.

The rise of supply chain finance has provided effective assistance to SMEs with financing difficulties. This study mainly explores the financial risk evaluation of supply chain under the leadership of 3PL. According to the risk identification, 27 comprehensive rating indicators were established, and then the model under the BP neural network was constructed through empirical data. The actual verification results show that the model performs very well in risk assessment which helps 3PL companies to better evaluate the business risks of supply chain finance, so as to take more effective risk management measures.

Businesses were moving during the past decades to-ward full digital models, which made companies face new threats and cyberattacks affecting their services and, consequently, their profits. To avoid negative impacts, companies' investments in cybersecurity are increasing considerably. However, Small and Medium-sized Enterprises (SMEs) operate on small budgets, minimal technical expertise, and few personnel to address cybersecurity threats. In order to address such challenges, it is essential to promote novel approaches that can intuitively present cybersecurity-related technical information.This paper introduces SecBot, a cybersecurity-driven conversational agent (i.e., chatbot) for the support of cybersecurity planning and management. SecBot applies concepts of neural networks and Natural Language Processing (NLP), to interact and extract information from a conversation. SecBot can (a) identify cyberattacks based on related symptoms, (b) indicate solutions and configurations according to business demands, and (c) provide insightful information for the decision on cybersecurity investments and risks. A formal description had been developed to describe states, transitions, a language, and a Proof-of-Concept (PoC) implementation. A case study and a performance evaluation were conducted to provide evidence of the proposed solution's feasibility and accuracy.

Digital signal processor (DSP) intellectual property (IP) cores are the underlying hardware responsible for high performance data intensive applications. However an unauthorized IP vendor may counterfeit the DSP IPs and infuse them into the design-chain. Thus fake IPs or integrated circuits (ICs) are unknowingly integrated into consumer electronics (CE) systems, leading to reliability and safety issues for users. The latent solution to this threat is hardware steganography wherein vendor's secret information is covertly inserted into the design to enable detection of counterfeiting. A key-regulated hash-modules chaining based IP steganography is presented in our paper to secure against counterfeiting threat. The proposed approach yielded a robust steganography achieving very high security with regard to stego-key length than previous approaches.

This article describes the principles of construction, training and use of neural networks. The features of the neural network approach are indicated, as well as the range of tasks for which it is most preferable. Algorithms of functioning, software implementation and results of work of an artificial neural network are presented.

One of the main goals of studying pattern matching techniques is their significant role in real-world applications, such as the intrusion detection systems branch. The purpose of the network attack detection systems NIDS is to protect the infocommunication network from unauthorized access. This article provides an analysis of the exact match and fuzzy matching methods, and discusses a new implementation of the classic Aho-Korasik pattern matching algorithm at the hardware level. The proposed approach to the implementation of the Aho-Korasik algorithm can make it possible to ensure the efficient use of resources, such as memory and energy.

Adversary emulation is an offensive exercise that provides a comprehensive assessment of a system’s resilience against cyber attacks. However, adversary emulation is typically a manual process, making it costly and hard to deploy in cyber-physical systems (CPS) with complex dynamics, vulnerabilities, and operational uncertainties. In this paper, we develop an automated, domain-aware approach to adversary emulation for CPS. We formulate a Markov Decision Process (MDP) model to determine an optimal attack sequence over a hybrid attack graph with cyber (discrete) and physical (continuous) components and related physical dynamics. We apply model-based and model-free reinforcement learning (RL) methods to solve the discrete-continuous MDP in a tractable fashion. As a baseline, we also develop a greedy attack algorithm and compare it with the RL procedures. We summarize our findings through a numerical study on sensor deception attacks in buildings to compare the performance and solution quality of the proposed algorithms.

Cloud-based payments, virtual car keys, and digital rights management are examples of consumer electronics applications that use secure software. White-box implementations of the Advanced Encryption Standard (AES) are important building blocks of secure software systems, and the attack of Billet, Gilbert, and Ech-Chatbi (BGE) is a well-known attack on such implementations. A drawback from the adversary’s or security tester’s perspective is that manual reverse engineering of the implementation is required before the BGE attack can be applied. This paper presents a method to automate the BGE attack on a class of white-box AES implementations with a specific type of external encoding. The new method was implemented and applied successfully to a CHES 2016 capture the flag challenge.

Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.

With almost unlimited storage capacity and low maintenance cost, cloud storage becomes a convenient and efficient way for data sharing among cloud users. However, this introduces the challenges of access control and privacy protection when data sharing for multiple groups, as each group usually has its own encryption and access control mechanism to protect data confidentiality. In this paper, we propose a multiple-group data sharing scheme with privacy preservation in the cloud. This scheme constructs a flexible access control framework by using group signature, ciphertext-policy attribute-based encryption and broadcast encryption, which supports both intra-group and cross-group data sharing with anonymous access. Furthermore, our scheme supports efficient user revocation. The security and efficiency of the scheme are proved thorough analysis and experiments.

Analysis of the state of development of research on the protection of valuable scientific and educational databases, library resources, information centers, publishers show the importance of information security, especially in corporate information networks and systems for data exchange. Corporate library networks include dozens and even hundreds of libraries for active information exchange, and they (libraries) are equipped with information security tools to varying degrees. The purpose of the research is to create effective methods and tools to protect the databases of the scientific and educational resources from unauthorized access in libraries and library networks using fuzzy logic methods.