Biblio

This work considers the trade-off between security and performance when revealing partial information about encrypted data computed on. The focus of our work is on information revealed through control flow side-channels when executing programs on encrypted data. We use quantitative information flow to measure security, running time to measure performance and program transformation techniques to alter the trade-off between the two. Combined with information flow policies, we perform a policy-aware security and performance trade-off (PASAPTO) analysis. We formalize the problem of PASAPTO analysis as an optimization problem, prove the NP-hardness of the corresponding decision problem and present two algorithms solving it heuristically. We implemented our algorithms and combined them with the Dataflow Authentication (DFAuth) approach for outsourcing sensitive computations. Our DFAuth Trade-off Analyzer (DFATA) takes Java Bytecode operating on plaintext data and an associated information flow policy as input. It outputs semantically equivalent program variants operating on encrypted data which are policy-compliant and approximately Pareto-optimal with respect to leakage and performance. We evaluated DFATA in a commercial cloud environment using Java programs, e.g., a decision tree program performing machine learning on medical data. The decision tree variant with the worst performance is 357% slower than the fastest variant. Leakage varies between 0% and 17% of the input.

Distributed Denial of Service (DDoS) attacks aim at bringing down or decreasing the availability of services for their legitimate users, by exhausting network or server resources. It is difficult to differentiate attack traffic from legitimate traffic as the attack can come from distributed nodes that additionally might spoof their IP addresses. Traditional DoS mitigation solutions fail to defend all kinds of DoS attacks and huge DoS attacks might exceed the processing capacity of routers and firewalls easily. The advent of Software-defined Networking (SDN) and Network Function Virtualization (NFV) has brought a new perspective for network defense. Key features of such technologies like global network view and flexibly positionable security functionality can be used for mitigating DDoS attacks. In this paper, we propose a collaborative DDoS attack mitigation scheme that uses SDN and NFV. We adopt a machine learning algorithm from related work to derive accurate patterns describing DDoS attacks. Our experimental results indicate that our framework is able to differentiate attack and legitimate traffic with high accuracy and in near-realtime. Furthermore, the derived patterns can be used to create OpenFlow (OF) or Firewall rules that can be pushed back into the direction of the attack origin for more efficient and distributed filtering.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.

The concept of the adversary model has been widely applied in the context of cryptography. When designing a cryptographic scheme or protocol, the adversary model plays a crucial role in the formalization of the capabilities and limitations of potential attackers. These models further enable the designer to verify the security of the scheme or protocol under investigation. Although being well established for conventional cryptanalysis attacks, adversary models associated with attackers enjoying the advantages of machine learning techniques have not yet been developed thoroughly. In particular, when it comes to composed hardware, often being security-critical, the lack of such models has become increasingly noticeable in the face of advanced, machine learning-enabled attacks. This paper aims at exploring the adversary models from the machine learning perspective. In this regard, we provide examples of machine learning-based attacks against hardware primitives, e.g., obfuscation schemes and hardware root-of-trust, claimed to be infeasible. We demonstrate that this assumption becomes however invalid as inaccurate adversary models have been considered in the literature.

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.

Recently, border monitoring and security has become an important topic since current methods against illegal immigration are expensive and inefficient. In particular, inefficiency and ineffectiveness increase when monitoring operations are focused on complex borders, where there is no available/reliable connectivity. In the last decade, the deployment of different kinds of unmanned aerial vehicles was seen as the main paradigm to provide on-demand wireless network access. Significant research work has been done on so called mobile base stations. Nevertheless, drones have specific technical limitations in terms, for example, of battery life and carried weight. Given above fundamental limits, network virtualization becomes a fundamental paradigm for system realization. In the last years, baseband processing was not seen any more as a monolithic block but has been studied as a chain of virtual functions. Especially, baseband unit can be split into five sub-blocks belonging to layer 1 to layer 3, where each degree of splitting implies more and more stringent requirements to be guaranteed, mainly in terms of throughput and latency. Split E is the logic separation of hybrid automatic repeat request from lower layers, which imposes the most flexible requirements. On the other hand, Split D (forward error correction, encoding/decoding logic functions) sets more stringent bounds on throughput and latency so that it requires careful study and detailed analysis for a correct system-level design. The main objective of this article is to study theoretically and numerically (i.e. via simulations) Split D to make it feasible with the help of small satellites. The paper will study the structure and the capabilities of small satellites to be used as small data centers to host radio access virtual network functions like forward error correction. The theoretical analysis is supported by simulations in order to highlight advantages and challenges of the proposed approach.

Supply chain security threats pose new challenges to security risk modeling techniques for complex ICT systems such as the IoT. With established techniques drawn from attack trees and reliability analysis providing needed points of reference, graph-based analysis can provide a framework for considering the role of suppliers in such systems. We present such a framework here while highlighting the need for a component-centered model. Given resource limitations when applying this model to existing systems, we study various classes of uncertainties in model development, including structural uncertainties and uncertainties in the magnitude of estimated event probabilities. Using case studies, we find that structural uncertainties constitute a greater challenge to model utility and as such should receive particular attention. Best practices in the face of these uncertainties are proposed.

Simultaneous Wireless Information and Power Transfer (SWIPT) technique is introduced in Radio Frequency (RF) communication to carry both information and power in same medium. In this approach, the energy can be harvested while decoding the information carries in an RF wave. Recently, the same concept applied in Visible Light Communication (VLC) namely Simultaneous Light Wave Information and Power Transfer (SLIPT), which is highly recommended in an indoor applications to overcome the problem facing in RF communication. Thus, SLIPT is introduced to transmit the power through a Light Emitting Diode (LED) luminaries. In this work, we compare both SWIPT and SLIPT technologies and realize SLIPT technology archives increased performance in terms of the amount of harvested energy, outage probability and error rate performance.

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.

The autonomy of intelligent systems and their safety rely on their ability for local decision making based on collected environmental information. This is even more for cyber-physical systems running safety critical activities. While this intelligence is partial and fragmented, and cognitive techniques are of limited maturity, the decision function must produce results whose validity and scope must be weighted in light of the underlying assumptions, unavoidable uncertainty and hypothetical safety limitation. Besides the cognitive techniques dependability, it is about the assurance level of the decision self-making. Beyond the pure decision-making capabilities of the autonomous intelligent system, we need techniques that guarantee the system assurance required for the intended use. Security mechanisms for cognitive systems may be consequently tightly intricated. We propose a trustworthiness module which is part of the system and its resulting safety. In this paper, we briefly review the state of the art regarding the dependability of cognitive techniques, the assurance level definition in this context, and related engineering practices. We elaborate regarding the design of autonomous intelligent systems safety, then we discuss its security design and approaches for the mitigation of safety violations by the cognitive functions.

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

Due to the wide adoption of IoT/CPS systems, embedded devices (IoT frontends) become increasingly connected and mission-critical, which in turn has attracted advanced attacks (e.g., control-flow hijacks and data-only attacks). Unfortunately, IoT backends (e.g., remote controllers or in-cloud services) are unable to detect if such attacks have happened while receiving data, service requests, or operation status from IoT devices (remotely deployed embedded devices). As a result, currently, IoT backends are forced to blindly trust the IoT devices that they interact with.To fill this void, we first formulate a new security property for embedded devices, called "Operation Execution Integrity" or OEI. We then design and build a system, OAT, that enables remote OEI attestation for ARM-based bare-metal embedded devices. Our formulation of OEI captures the integrity of both control flow and critical data involved in an operation execution. Therefore, satisfying OEI entails that an operation execution is free of unexpected control and data manipulations, which existing attestation methods cannot check. Our design of OAT strikes a balance between prover's constraints (embedded devices' limited computing power and storage) and verifier's requirements (complete verifiability and forensic assistance). OAT uses a new control-flow measurement scheme, which enables lightweight and space-efficient collection of measurements (97% space reduction from the trace-based approach). OAT performs the remote control-flow verification through abstract execution, which is fast and deterministic. OAT also features lightweight integrity checking for critical data (74% less instrumentation needed than previous work). Our security analysis shows that OAT allows remote verifiers or IoT backends to detect both controlflow hijacks and data-only attacks that affect the execution of operations on IoT devices. In our evaluation using real embedded programs, OAT incurs a runtime overhead of 2.7%.

Object recognition with the help of outdoor video surveillance cameras is an important task in the context of ensuring the security at enterprises, public places and even private premises. There have long existed systems that allow detecting moving objects in the image sequence from a video surveillance system. Such a system is partially considered in this research. It detects moving objects using a background model, which has certain problems. Due to this some objects are missed or detected falsely. We propose to combine the moving objects detection results with the classification, using a deep neural network. This will allow determining whether a detected object belongs to a certain class, sorting out false detections, discarding the unnecessary ones (sometimes individual classes are unwanted), to divide detected people into the employees in the uniform and all others, etc. The authors perform a network training in the Keras developer-friendly environment that provides for quick building, changing and training of network architectures. The performance of the Keras integration into a video analysis system, using direct Python script execution techniques, is between 6 and 52 ms, while the precision is between 59.1% and 97.2% for different architectures. The integration, made by freezing a selected network architecture with weights, is selected after testing. After that, frozen architecture can be imported into video analysis using the TensorFlow interface for C++. The performance of such type of integration is between 3 and 49 ms. The precision is between 63.4% and 97.8% for different architectures.

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.

Due to the low latency and high speed of 5G networks, the Internet of Vehicles (IoV) under the 5G network has been rapidly developed and has broad application prospects. The Third Generation Partnership Project (3GPP) committee has taken remote diagnosis as one of the development cores of IoV. However, how to ensure the security of remote diagnosis and maintenance services is also a key point to ensure vehicle safety, which is directly related to the safety of vehicle passengers. In this paper, we propose a secure and efficient authentication scheme based on extended chebyshev chaotic maps for remote diagnosis and maintenance in IoVs. In the proposed scheme, to provide strong security, anyone, such as the vehicle owner or the employee of the Vehicle Service Centre (VSC), must enter the valid biometrics and password in order to enjoy or provide remote diagnosis and maintenance services, and the vehicle and the VSC should authenticate each other to ensure that they are legitimate. The security analysis and performance evaluation results show that the proposed scheme can provide robust security with ideal efficiency.

In this paper, we propose a novel deep learning and blockchain-based energy framework for smart grids, entitled DeepCoin. The DeepCoin framework uses two schemes, a blockchain-based scheme and a deep learning-based scheme. The blockchain-based scheme consists of five phases: setup phase, agreement phase, creating a block phase and consensus-making phase, and view change phase. It incorporates a novel reliable peer-to-peer energy system that is based on the practical Byzantine fault tolerance algorithm and it achieves high throughput. In order to prevent smart grid attacks, the proposed framework makes the generation of blocks using short signatures and hash functions. The proposed deep learning-based scheme is an intrusion detection system (IDS), which employs recurrent neural networks for detecting network attacks and fraudulent transactions in the blockchain-based energy network. We study the performance of the proposed IDS on three different sources the CICIDS2017 dataset, a power system dataset, and a web robot (Bot)-Internet of Things (IoT) dataset.

Mining, the process where multiple miners compete to add blocks to Proof-of-Work (PoW) blockchains, is of great importance to maintain the tamper-resistance feature of blockchains. In current blockchain networks, miners usually form groups, called mining pools, to improve their revenues. When multiple pools exist, a fundamental mining pool selection problem arises: which pool should each miner join to maximize its revenue? In addition, the existence of mining pools also leads to another critical issue, i.e., Block WithHolding (BWH) attack, where a pool sends some of its miners as spies to another pool to gain extra revenues without contributing to the mining of the infiltrated pool. This paper therefore aims to investigate the mining pool selection issue (i.e., the stable population distribution of miners in the pools) in the presence of BWH attack from the perspective of evolutionary game theory. We first derive the expected revenue density of each pool to determine the expected payoff of miners in that pool. Based on the expected payoffs, we formulate replicator dynamics to represent the growth rates of the populations in all pools. Using the replicator dynamics, we obtain the rest points of the growth rates and discuss their stability to identify the Evolutionarily Stable States (ESSs) (i.e., stable population distributions) of the game. Simulation and numerical results are also provided to corroborate our analysis and to illustrate the theoretical findings.

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.

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.

Web application services have gradually become an important support of Internet services, but are also facing increasingly serious security problems. It is extremely necessary to evaluate the security of Web application services to deal with attacks against them effectively. In this paper, in view of the characteristics of the current attack of Web application services, a Web security analysis model based on the kill chain is established, and the possible attacks against Web application services are analyzed in depth from the perspective of the kill chain. Then, the security of Web application services is evaluated in a quantitative manner. In this way, it can make up the defects of insufficient inspection by the existing security vulnerability model and the security specification of the tracking of Web application services, so as to realize the objective and scientific evaluation of the security state of Web application services.

Although quite a few results on resilient consensus of multi-agent systems with malicious agents and fixed topology have been reported in the literature, we lack any known results on such a problem for multi-agent systems with time-varying topologies. Herein, we study the resilient consensus problem of time-varying networked systems in the presence of misbehaving nodes. A novel concept of joint ( r, s) -robustness is firstly proposed to characterize the robustness of the time-varying topologies. It is further revealed that the resilient consensus of multi-agent systems under F-total malicious network can be reached by the Weighted Mean-Subsequence-Reduced algorithm if and only if the time-varying graph is jointly ( F+1, F+1) -robust. Numerical simulations are finally performed to verify the effectiveness of the analytical results.

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.

The concept of AI Gossip is presented. It is analogous to the traditional understanding of a pernicious human failing. It is made more egregious by the technology of AI, internet, current privacy policies, and practices. The recognition by the technological community of its complacency is critical to realizing its damaging influence on human rights. A current example from the medical field is provided to facilitate the discussion and illustrate the seriousness of AI Gossip. Further study and model development is encouraged to support and facilitate the need to develop standards to address the implications and consequences to human rights and dignity.