Biblio

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.

Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore.

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an apriori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the dataflow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility is calculated, an empirical and non-invasive characterization of the inherent objectives of the software designers. By comparing design (a-priori) utility with deploy (deployed system) utility, we show, using a small but real ROS example, that it's possible to monitor a performance criterion and relate violations of the criterion to parts of the software. The software is then patched using automated software repair techniques and evaluated against the original off-line utility.

The recent spurt of incidents related to copyrights and security breaches has led to the monetary loss of several digital content creators and publishers. These incidents conclude that the existing system lacks the ability to uphold the integrity of their published content. Moreover, some of the digital content owners rely on third parties, results in lack of ability to provide provenance of digital media. The question that needs to be addressed today is whether modern technologies can be leveraged to suppress such incidents and regain the confidence of creators and the audience. Fortunately, this paper presents a unique framework that empowers digital content creators to have complete control over the place of its origin, accessibility and impose restrictions on unauthorized alteration of their content. This framework harnesses the power of the Ethereum platform, a part of Blockchain technology, and uses S mart Contracts as a key component empowering the creators with enhanced control of their content and the corresponding audience.

In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy ($\approx$0.9167) on the dataset provided by VirusTotal.

This paper compares the known method of Analytical Design of Aggregated Regulators (ADAR) with the method of Analytical Design of Optimal Regulators (ADOR). Both equivalence of these methods and the significant difference in the approaches to the analytical synthesis of control laws are shown. It is shown that the ADAR method has significant advantages associated with a simpler and analytical procedure of design of nonlinear laws for optimal control, clear physical representation of weighting factors of optimality criteria, validity and unambiguity of selecting regulator setting parameters, more simple approach to the analysis of the closed-loop system asymptotic stability. These advantages are illustrated by the examples of synthesis.

In the digital payment system, the transactions and their data about clients are very sensitive, so the security and privacy of personal information of the client is a big concern. The confirmation towards security necessities prevents the data from a stolen and unauthorized person over the digital transactions, So the stronger authentication methods required, which must be based on cryptography. Initially, in the payment ecosystem, they were using the Kerberos protocol, but now different approaches such as Challenge-Handshake Authentication Protocol (CHAP), Tokenization, Two-Factor Authentication(PIN, MPIN, OTP), etc. such protocols are being used in the payment system. This paper presents the use cases of different authentication protocols. Further, the use of these protocols in online payment systems to verify each individual are explained.

While we cannot question the high performance capabilities of the kernel bypass approach in the network functions world, we recognize that the Linux kernel provides a rich ecosystem with an efficient resource management and an effective resource sharing ability that cannot be ignored. In this work we argue that by mixing kernel-bypass and in kernel processing can benefit applications and network function middleboxes. We leverage a high-performance user space TCP stack and recent additions to the Linux kernel to propose a hybrid approach (kernel-user space) to accelerate SDN/NFV deployments leveraging services of the reliable transport layer (i.e., stateful middleboxes, Layer 7 network functions and applications). Our results show that this approach enables highperformance, high CPU efficiency, and enhanced integration with the kernel ecosystem. We build our solution by extending mTCP which is the basis of some state-of-the-art L4-L7 NFV frameworks. By having more efficient CPU usage, NFV applications can have more CPU cycles available to run the network functions and applications logic. We show that for a CPU intense workload, mTCP/AF\_XDP can have up to 64% more throughput than the previous implementation. We also show that by receiving cooperation from the kernel, mTCP/AF\_XDP enables the creation of protection mechanisms for mTCP. We create a simulated DDoS attack and show that mTCP/AF\_XDP can maintain up to 287% more throughput than the unprotected system during the attack.

This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise.

Attribute-based encryption received widespread attention as soon as it was proposed. However, due to its specific characteristics, some restrictions on attribute set are not flexible enough in actual operation. In addition, since access authorities are determined according to users' attributes, users sharing the same attributes are difficult to be distinguished. Once a malicious user makes illicit gains by their decryption authorities, it is difficult to track down specific user. This paper follows practical demands to propose a more flexible key-policy attribute-based encryption scheme with black-box traceability. The scheme has a constant size of public parameters which can be utilized to construct attribute-related parameters flexibly, and the method of traitor tracing in broadcast encryption is introduced to achieve effective malicious user tracing. In addition, the security and feasibility can be proved by the security proofs and performance evaluation in this paper.

Autonomous Underwater Vehicles (AUVs) have several fundamental civilian and military applications, and Denial of Service (DoS) attacks against their communications are a serious threat. In this work, we analyze such an attack using game theory in an asymmetric scenario, in which the node under attack does not know the position of the jammer that blocks its signals. The jammer has a dual objective, namely, disrupting communications and forcing the legitimate transmitter to spend more energy protecting its own transmissions. Our model shows that, if both nodes act rationally, the transmitter is able to quickly reduce its disadvantage, estimating the location of the jammer and responding optimally to the attack.

Security is one of the key aspects of underwater acoustic networks, due to the critical importance of the scenarios in which these networks can be employed. For example, attacks performed to military underwater networks or to assets deployed for tsunami prevention can lead to disastrous consequences. Nevertheless, countermeasures to possible network attacks have not been widely investigated so far. One way to identify possible attackers is by using reputation, where a node gains trust each time it exhibits a good behavior, and loses trust each time it behaves in a suspicious way. The first step for analyzing if a node is behaving in a good way is to inspect the network traffic, by detecting all conversations. This paper proposes both centralized and decentralized algorithms for performing this operation, either from the network or from the node perspective. While the former can be applied only in post processing, the latter can also be used in real time by each node, and so can be used for creating the trust value. To evaluate the algorithms, we used real experimental data acquired during the EDA RACUN project (Robust Underwater Communication in Underwater Networks).

With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impact on the safety of water area, it is of great significance to identify underwater small targets in time to make early warning for it. In this paper, convolutional neural network is applied to underwater small target recognition. The recognition targets are diver, whale and dolphin. Due to the time-frequency spectrum can reflect the essential features of underwater target, convolutional neural network can learn a variety of features of the acoustic signal through the image processed by the time-frequency spectrum, time-frequency image is input to convolutional neural network to recognize the underwater small targets. According to the study of learning rate and pooling mode, the network parameters and structure suitable for underwater small target recognition in this paper are selected. The results of data processing show that the method can identify underwater small targets accurately.

With the improvement of the current Internet software and hardware performance, cloud storage has become one of the most widely used applications. This paper proposes a user privacy protection algorithm suitable for tennis match live broadcast from media cloud platform. Through theoretical and experimental verification, this algorithm can better protect the privacy of users in the live cloud platform. This algorithm is a ciphertext calculation algorithm based on data blocking. Firstly, plaintext data are grouped, then AES ciphertext calculation is performed on each group of plaintext data simultaneously and respectively, and finally ciphertext data after grouping encryption is spliced to obtain final ciphertext data. Experimental results show that the algorithm has the characteristics of large key space, high execution efficiency, ciphertext statistics and good key sensitivity.

Recent advances in Deep Learning (DL) have enabled a paradigm shift to include machine intelligence in a wide range of autonomous tasks. As a result, a largely unexplored surface has opened up for attacks jeopardizing the integrity of DL models and hindering the success of autonomous systems. To enable ubiquitous deployment of DL approaches across various intelligent applications, we propose to develop architectural support for hardware implementation of secure and robust DL. Towards this goal, we leverage hardware/software co-design to develop a DL execution engine that supports algorithms specifically designed to defend against various attacks. The proposed framework is enhanced with two real-time defense mechanisms, securing both DL training and execution stages. In particular, we enable model-level Trojan detection to mitigate backdoor attacks and malicious behaviors induced on the DL model during training. We further realize real-time adversarial attack detection to avert malicious behavior during execution. The proposed execution engine is equipped with hardware-level IP protection and usage control mechanism to attest the legitimacy of the DL model mapped to the device. Our design is modular and can be tuned to task-specific demands, e.g., power, throughput, and memory bandwidth, by means of a customized hardware compiler. We further provide an accompanying API to reduce the nonrecurring engineering cost and ensure automated adaptation to various domains and applications.

Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter ε controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while ε-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.

In the face of increasingly serious firmware attacks, it is of great significance to analyze the vulnerability security of UEFI. This paper first introduces the commonly used trusted authentication mechanisms of UEFI. Then, aiming at the loopholes in the process of UEFI trust verification in the startup phase, combined with the state transition diagram, PageRank algorithm and Bayesian network theory, the analysis model of UEFI trust verification startup vulnerability is constructed. And according to the example to verify the analysis. Through the verification and analysis of the data obtained, the vulnerable attack paths and key vulnerable nodes are found. Finally, according to the analysis results, security enhancement measures for UEFI are proposed.

In security protocol design, Visible Light Communication (VLC) has often been abstracted as an ideal channel that is resilient to eavesdropping, manipulation, and jamming. Camera Communication (CamCom), a subcategory of VLC, further strengthens the level of security by providing a visually verifiable association between the transmitter and the extracted information. However, the ideal security guarantees of visible light channels may not hold in practice due to limitations and tradeoffs introduced by hardware, software, configuration, environment, etc. This paper presents our experience and lessons learned from implementing CamCom for security protocols. We highlight CamCom's security-enhancing properties and security applications that it enables. Backed by real implementation and experiments, we also systematize the practical considerations of CamCom-based security protocols.

In recent years, cross-domain roaming through Wi-Fi is ubiquitous, and the number of roaming users has increased dramatically. It is essential to authenticate users belonging to different institutes to ensure network privacy and security. Existing systems, such as eduroam, have centralized and hierarchical structure on indorse accounts that create privacy and security issues. We have proposed UniRoam, a blockchain-based cross-domain authentication scheme that provides accountability and anonymity without any trusted authority. Unlike traditional centralized approaches, UniRoam provides access authentication for its servers and users to provide anonymity and accountability without any privacy leakage issues efficiently. By using the sovrin identifier as an anonymous identity, we integrate our system with Hyperledger and Intel SGX to authenticate users that preserves both anonymity and trust when the user connects to the network. Therefore, UniRoam is highly “faulted-tolerant” to deal with different attacks and provides an effective solution that can be deployed easily in different environments.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity.

The information-theoretic approach to security entails harnessing the correlated randomness available in nature to establish security. It uses tools from information theory and coding and yields provable security, even against an adversary with unbounded computational power. However, the feasibility of this approach in practice depends on the development of efficiently implementable schemes. In this paper, we review a special class of practical schemes for information-theoretic security that are based on 2-universal hash families. Specific cases of secret key agreement and wiretap coding are considered, and general themes are identified. The scheme presented for wiretap coding is modular and can be implemented easily by including an extra preprocessing layer over the existing transmission codes.

In this work, communication secrecy in cooperative and multi-hop wireless communications for various radio frequencies are examined. Attenuation lines and ranges of both detection and ultimate secrecy regions were calculated for cooperative communication channel and multi-hop channel with various number of hops. From results, frequency ranges with the highest potential to apply bandwidth saving method known as frequency reuse were determined and compared to point-to-point channel. Frequencies with the highest attenuation were derived and their ranges of both detection and ultimate secrecy are calculated. Point-to-point, cooperative and multi-hop channels were compared in terms of ultimate secrecy ranges. Multi-hop channel measurements were made with different number of hops and the relation between the number of hops and communication security is examined. Ultimate secrecy ranges were calculated up to 1 Terahertz and found to be less than 13 meters between 550-565 GHz frequency range. Therefore, for short-range wireless communication systems such as indoor and in-device communication systems (board-to-board or chip-to-chip communications), it is shown that various bands in the Terahertz band can be used to reuse the same frequency in different locations to obtain high security and high bandwidth.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.