Biblio

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.

Capability machines such as CHERI provide memory capabilities that can be used by compilers to provide security benefits for compiled code (e.g., memory safety). The existing C to CHERI compiler, for example, achieves memory safety by following a principle called “pointers as capabilities” (PAC). Informally, PAC says that a compiler should represent a source language pointer as a machine code capability. But the security properties of PAC compilers are not yet well understood. We show that memory safety is only one aspect, and that PAC compilers can provide significant additional security guarantees for partial programs: the compiler can provide security guarantees for a compilation unit, even if that compilation unit is later linked to attacker-provided machine code.As such, this paper is the first to study the security of PAC compilers for partial programs formally. We prove for a model of such a compiler that it is fully abstract. The proof uses a novel proof technique (dubbed TrICL, read trickle), which should be of broad interest because it reuses the whole-program compiler correctness relation for full abstraction, thus saving work. We also implement our scheme for C on CHERI, show that we can compile legacy C code with minimal changes, and show that the performance overhead of compiled code is roughly proportional to the number of cross-compilation-unit function calls.

Attribute-based encryption (ABE) is an access control mechanism where a sender encrypts messages according to an attribute set for multiple receivers. With fine-grained access control, it has been widely applied to cloud storage and file sharing systems. In such a mechanism, it is a challenge to achieve the revocation efficiently on a specific user since different users may share common attributes. Thus, dynamic membership is a critical issue to discuss. On the other hand, most works on LSSS-based ABE do not address the situation about threshold on the access structure, and it lowers the diversity of access policies. This manuscript presents an efficient attribute-based encryption scheme with dynamic membership by using LSSS. The proposed scheme can implement threshold gates in the access structure. Furthermore, it is the first ABE supporting complete dynamic membership that achieves the CCA security in the standard model, i.e. without the assumption of random oracles.

Today the mobile devices are more and more present in our lives, and the mobile applications market has experienced a sharp growth. Most of these applications are made to make our daily lives easier, and for this a large part of them consume various web services. Given this transition, from desktop and web applications to mobile applications, many critical services have begun to expose their APIs for use by such application clients. Unfortunately, this transition has paved the way for new vulnerabilities, vulnerabilities used to compress cloud services. In this article we analyzed the main security problems and how they can be solved using the attestation services, the services that indicate that the device running the application and the client application are genuine.

Malwares are the key means leveraged by threat actors in the cyber space for their attacks. There is a large array of commercial solutions in the market and significant scientific research to tackle the challenge of the detection and defense against malwares. At the same time, attackers also advance their capabilities in creating polymorphic and metamorphic malwares to make it increasingly challenging for existing solutions. To tackle this issue, we propose a methodology to perform malware detection and family attribution. The proposed methodology first performs the extraction of opcodes from malwares in each family and constructs their respective opcode graphs. We explore the use of clustering algorithms on the opcode graphs to detect clusters of malwares within the same malware family. Such clusters can be seen as belonging to different sub-family groups. Opcode graph signatures are built from each detected cluster. Hence, for each malware family, a group of signatures is generated to represent the family. These signatures are used to classify an unknown sample as benign or belonging to one the malware families. We evaluate our methodology by performing experiments on a dataset consisting of both benign files and malware samples belonging to a number of different malware families and comparing the results to existing approach.

With the emergence and development of quantum computers, the traditional public-key cryptography (PKC) is facing the risk of being cracked. In order to resist quantum attacks and ensure long-term communication security, NIST launched a global collection of Post Quantum Cryptography (PQC) standards in 2016, and it is currently in the third round of selection. There are three Lattice-based PKC algorithms that stand out, and NTRU is one of them. In this article, we proposed the first complete and compact full hardware implementation of NTRU algorithm submitted in the third round. By using one structure to complete the design of the three types of complex polynomial multiplications in the algorithm, we achieved better performance while reducing area costs.

In this paper, we discuss the methodology of generalizing the optimal control law from learned component tasks to unlearned composite tasks on Multi-Agent Systems (MASs), by using the linearity composition principle of linearly solvable optimal control (LSOC) problems. The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs. We investigate the application of the proposed approach on the MAS with coordination between agents. The experiments show feasible results in investigated scenarios, including both discrete and continuous dynamical systems for task generalization without resampling.

Spin-transfer torque magnetoresistive random-access memories (STT-MRAMs) are gradually superseding conventional SRAMs as last-level cache in System-on-Chip designs. Their manufacturing process includes trimming a reference resistance in STT-MRAM modules to reliably determine the logic values of 0 and 1 during read operations. Typically, an on-chip trimming routine consists of multiple runs of a test algorithm with different settings of a trimming port. It may inherently produce a large number of mismatches. Diagnosis of such a sizeable volume of errors by means of existing memory built-in self-test (MBIST) schemes is either infeasible or a time-consuming and expensive process. In this paper, we propose a new memory fault diagnosis scheme capable of handling STT-MRAM-specific error rates in an efficient manner. It relies on a convolutional reduction of memory outputs and continuous shifting of the resultant data to a tester through a few output channels that are typically available in designs using an on-chip test compression technology, such as the embedded deterministic test. It is shown that processing the STT-MRAM output by using a convolutional compactor is a preferable solution for this type of applications, as it provides a high diagnostic resolution while incurring a low hardware overhead over traditional MBIST logic.

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.

This paper considers the covert identification problem in which a sender aims to reliably convey an identification (ID) message to a set of receivers via a binary-input discrete memoryless channel (BDMC), and simultaneously to guarantee that the communication is covert with respect to a warden who monitors the communication via another independent BDMC. We prove a square-root law for the covert identification problem. This states that an ID message of size exp(exp($\Theta$($\surd$ n)) can be transmitted over n channel uses. We then characterize the exact pre-constant in the $\Theta$($\cdot$) notation. This constant is referred to as the covert identification capacity. We show that it equals the recently developed covert capacity in the standard covert communication problem, and somewhat surprisingly, the covert identification capacity can be achieved without any shared key between the sender and receivers. The achievability proof relies on a random coding argument with pulse-position modulation (PPM), coupled with a second stage which performs code refinements. The converse proof relies on an expurgation argument as well as results for channel resolvability with stringent input constraints.

Industry 4.0 or also known as the fourth industrial revolution poses a great cybersecurity risk for Supervisory control and data acquisition (SCADA) systems. Nowadays, lots of enterprises have turned into renewable energy and are changing the energy dependency to be on wind power. The SCADA systems are often vulnerable against different kinds of cyberattacks and thus allowing intruders to successfully and intrude exfiltrate different wind farm SCADA systems. During our research a future concept testbed of a wind farm SCADA system is going to be introduced. The already existing real-world vulnerabilities that are identified are later on going to be demonstrated against the test SCADA wind farm system.

CAPTCHA or Completely Automated Public Turing test to Tell Computers and Humans Apart is a technique to distinguish between humans and computers by generating and evaluating tests that can be passed by humans but not computer bots. However, captchas are not foolproof, and they can be bypassed which raises security concerns. Hence, sites over the internet remain open to such vulnerabilities. This research paper identifies the vulnerabilities found in some of the commonly used captcha schemes by cracking them using Deep Learning techniques. It also aims to provide solutions to safeguard against these vulnerabilities and provides recommendations for the generation of secure captchas.

One of the attacks in the RPL protocol is the Clone ID attack, that the attacker clones the node's ID in the network. In this research, a Clone ID detection system is designed for the Internet of Things (IoT), implemented in Contiki operating system, and evaluated using the Cooja emulator. Our evaluation shows that the proposed method has desirable performance in terms of energy consumption overhead, true positive rate, and detection speed. The overhead cost of the proposed method is low enough that it can be deployed in limited-resource nodes. The proposed method in each node has two phases, which are the steps of gathering information and attack detection. In the proposed scheme, each node detects this type of attack using control packets received from its neighbors and their information such as IP, rank, Path ETX, and RSSI, as well as the use of a routing table. The design of this system will contribute to the security of the IoT network.

With smart vehicles interconnected with multiple systems and other entities, whether they are people or IoT devices, the importance of a digital identity for them has emerged. We present in this paper how a Self-Sovereign Identities combined with blockchain can provide a solution to this end, in order to decentralize the identity management and provide them with capabilities to identify the other entities they interact with. Such entities can be the owners of the vehicles, other drivers and workshops that act as service providers. Two use cases are examined along with the interactions between the participants, to demonstrate how a decentralized identity management solution can take care of the necessary authentication and authorization processes. Finally, we test the system and provide the measurements to prove its feasibility in real-life deployments.

Skyrmion racetrack memory (Sk-RM) is a new storage technology in which skyrmions are used to represent data bits to provide high storage density. During the reading procedure, the skyrmion is driven by a current and sensed by a fixed read head. However, synchronization errors may happen if the skyrmion does not pass the read head on time. In this paper, a polar coding scheme is proposed to correct the synchronization errors in the Sk-RM. Firstly, we build two error correction models for the reading operation of Sk-RM. By connecting polar codes with the marker codes, the number of deletion errors can be determined. We also redesign the decoding algorithm to recover the information bits from the readout sequence, where a tighter bound of the segmented deletion errors is derived and a novel parity check strategy is designed for better decoding performance. Simulation results show that the proposed coding scheme can efficiently improve the decoding performance.

This research work consists in to design a system of occupancy simulation in smart homes based on IoT, in order to create configurations within a home that make look like the daily behavior of home inhabitants. Due to the high rate of burglary in uninhabited places, reaching an 9% in average in 2019 in the Chilean case, technologies have been involved with greater emphasis on improving security systems, where the implementation of the Internet of Things will allow rapid action against the intruder detection in those places. The proposed IoT system is based on a motion sensor, actuators as relays and lights, Arduino platform to control system, and a Amazon Echo virtual assistant to interface with inhabitants. The main contribution of this prototype security system is the integration of different IoT (Adafruit, IFTTT) and control platforms (Arduino uno and NodeMCU), virtual assistant (Alexa) and actuators, which has features that can be replicated in larger processes and with a larger number of devices. The results demonstrate that security system create an environment occupied by owners without to be inside home, through sensors and actuators.

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.

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.

Digital forensics is concerned with identifying, reporting and responding to security breaches. It is about how to acquire, analyze and report digital evidence and using the technical skills, discovering the traces of Cyber Crime. The field of digital forensics is in high demand due to the constant threats of data breaches and information hacks. Digital Forensics is utilized in the identification and elimination of crimes in any controversy where evidence is preserved in online space. This is the use of specialized techniques for retrieval, authentication and electronic data analysis. Computer forensics deals with the identification, preservation, analysis, documentation and presentation of digital evidence. The paper has analyzed the present-day trends that includes IoT forensics, cloud forensics, network forensics and social media forensics. Recent researches have shown a wide range of threats and cyber-attacks, which requires forensic investigators and forensics scientists to simplify the digital world. Hence, all our research gives a clear view of digital forensics which could be of a great help in forensic investigation. In this research paper we have discussed about the need and way to preserve the digital evidence, so that it is not compromised at any point in time and an unalter evidence can be presented before the court of law.

Ensuring the network security, resists the malicious traffic attacks as much as possible, and ensuring the network security, the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) are combined. Then, a Software Defined Networking (SDN) anomaly detection architecture is built and continuously optimized to ensure network security as much as possible and enhance the reliability of the detection architecture. The results show that the proposed network architecture can greatly improve the accuracy of detection, and its performance will be different due to the different number of CNN layers. When the two-layer CNN structure is selected, its performance is the best among all algorithms. Especially, the accuracy of GRU- CNN-2 is 98.7%, which verifies that the proposed method is effective. Therefore, under deep learning, the utilization of GRU- CNN to explore and optimize the SDN anomaly detection is of great significance to ensure information transmission security in the future.

The envisioned massive deployment of network slices in 5G and beyond mobile systems makes the shift towards zero-touch, scalable and secure slice lifecycle management a necessity. This is to harvest the benefits of network slicing in enabling profitable services. These benefits will not be attained without ensuring a high level security of the created network slices and the underlying infrastructure, above all in a zero-touch automated fashion. In this vein, this paper presents the architecture of an innovative network slicing security orchestration framework, being developed within the EU H2020 MonB5G project. The framework leverages the potential of Security as a Service (SECaaS) and Artificial Intelligence (AI) to foster fully-distributed, autonomic and fine-grained management of network slicing security from the node level to the end-to-end and inter-slice levels.

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.In this paper, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.

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.

Photo Response Non-Uniformity (PRNU) noise obtained from images or videos is used as a camera fingerprint to attribute visual objects captured by a camera. The PRNU-based source attribution method, however, fails when there is misalignment between the fingerprint and the query object. One example of such a misalignment, which has been overlooked in the field, is caused by the in-camera resizing technique that a video may have been subjected to. This paper investigates the attribution of visual media in the context of matching a video query object to an image fingerprint or vice versa. Specifically this paper focuses on improving camera attribution performance by taking into account the effects of binning, a commonly used in-camera resizing technique applied to video. We experimentally show that the True Positive Rate (TPR) obtained when binning is considered is approximately 3% higher.

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.