Biblio

The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.

Critical infrastructures have suffered from different kind of cyber attacks over the years. Many of these attacks are performed using malwares by exploiting the vulnerabilities of these resources. Smart power grid is one of the major victim which suffered from these attacks and its SCADA system are frequently targeted. In this paper we describe our proposed framework to analyze smart power grid, while its SCADA system is under attack by malware. Malware propagation and its effects on SCADA system is the focal point of our analysis. OMNeT++ simulator and openDSS is used for developing and analyzing the simulated smart power grid environment.

With the recent explosion of data breaches and data misuse cases, there is more demand than ever for secure system designs that fundamentally tackle today's data trust models. One promising alternative to today's trust model is true end-to-end encryption without however compromising user experience nor data utility. Fully homomorphic encryption (FHE) provides a powerful tool in empowering users with more control over their data, while still benefiting from computing services of remote services, though without trusting them with plaintext data. However, due to the complexity of fully homomorphic encryption, it has remained reserved exclusively for a small group of domain experts. With our system Marble, we make FHE accessible to the broader community of researchers and developers. Marble takes away the complexity of setup and configuration associated with FHE schemes. It provides a familiar programming environment. Marble allows rapid feasibility assessment and development of FHE-based applications. More importantly, Marble benchmarks the overall performance of an FHE-based application, as part of the feasibility assessment. With real-world application case-studies, we show the practicality of Marble.

Tor provides low-latency anonymous and uncensored network access against a local or network adversary. Due to the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak. Attacks using (features extracted from) this information to infer the website a user visits are called Website Fingerprinting (WF) attacks. We develop a methodology and tools to measure the amount of leaked information about a website. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attacks and defenses.

We present the first formalisation of a blockchain-based distributed consensus protocol with a proof of its consistency mechanised in an interactive proof assistant. Our development includes a reference mechanisation of the block forest data structure, necessary for implementing provably correct per-node protocol logic. We also define a model of a network, implementing the protocol in the form of a replicated state-transition system. The protocol's executions are modeled via a small-step operational semantics for asynchronous message passing, in which packages can be rearranged or duplicated. In this work, we focus on the notion of global system safety, proving a form of eventual consistency. To do so, we provide a library of theorems about a pure functional implementation of block forests, define an inductive system invariant, and show that, in a quiescent system state, it implies a global agreement on the state of per-node transaction ledgers. Our development is parametric with respect to implementations of several security primitives, such as hash-functions, a notion of a proof object, a Validator Acceptance Function, and a Fork Choice Rule. We precisely characterise the assumptions, made about these components for proving the global system consensus, and discuss their adequacy. All results described in this paper are formalised in Coq.

The integrity and reliability of speech data have been important issues to probative use. Watermarking technologies supplies an alternative solution to guarantee the the authenticity of multiple data besides digital signature. This work proposes a novel digital watermarking based on a reversible compression algorithm with sample scanning to detect tampering in time domain. In order to detect tampering precisely, the digital speech data is divided into length-fixed frames and the content-based hash information of each frame is calculated and embedded into the speech data for verification. Huffman compression algorithm is applied to each four sampling bits from least significant bit in each sample after pulse-code modulation processing to achieve low distortion and high capacity for hiding payload. Experimental experiments on audio quality, detection precision and robustness towards attacks are taken, and the results show the effectiveness of tampering detection with a precision with an error around 0.032 s for a 10 s speech clip. Distortion is imperceptible with an average 22.068 dB for Huffman-based and 24.139 dB for intDCT-based method in terms of signal-to-noise, and with an average MOS 3.478 for Huffman-based and 4.378 for intDCT-based method. The bit error rate (BER) between stego data and attacked stego data in both of time-domain and frequency domain is approximate 28.6% in average, which indicates the robustness of the proposed hiding method.

Computational Intelligence (CI) algorithms/techniques are packaged in a variety of disparate frameworks/applications that all vary with respect to specific supported functionality and implementation decisions that drastically change performance. Developers looking to employ different CI techniques are faced with a series of trade-offs in selecting the appropriate library/framework. These include resource consumption, features, portability, interface complexity, ease of parallelization, etc. Considerations such as language compatibility and familiarity with a particular library make the choice of libraries even more difficult. The paper introduces MeetCI, an open source software framework for computational intelligence software design automation that facilitates the application design decisions and their software implementation process. MeetCI abstracts away specific framework details of CI techniques designed within a variety of libraries. This allows CI users to benefit from a variety of current frameworks without investigating the nuances of each library/framework. Using an XML file, developed in accordance with the specifications, the user can design a CI application generically, and utilize various CI software without having to redesign their entire technology stack. Switching between libraries in MeetCI is trivial and accessing the right library to satisfy a user's goals can be done easily and effectively. The paper discusses the framework's use in design of various applications. The design process is illustrated with four different examples from expert systems and machine learning domains, including the development of an expert system for security evaluation, two classification problems and a prediction problem with recurrent neural networks.

Virtualization offers the possibility of hosting services of multiple customers on shared hardware. When more than one Virtual Machine (VM) run on the same host, memory deduplication can save physical memory by merging identical pages of the VMs. However, this comes at the cost of leaking information between VMs. Based on that, we propose a novel timing-based side-channel attack that allows to identify software versions running in co-resident VMs or on the host. Our attack tests for the existence of memory pages in co-resident VMs that are unique among all versions of the respective software. Our evaluation results indicate that with few repetitions of our attack we can precisely identify software versions within reasonable time frames.

Attack simulations may be used to assess the cyber security of systems. In such simulations, the steps taken by an attacker in order to compromise sensitive system assets are traced, and a time estimate may be computed from the initial step to the compromise of assets of interest. Attack graphs constitute a suitable formalism for the modeling of attack steps and their dependencies, allowing the subsequent simulation. To avoid the costly proposition of building new attack graphs for each system of a given type, domain-specific attack languages may be used. These languages codify the generic attack logic of the considered domain, thus facilitating the modeling, or instantiation, of a specific system in the domain. Examples of possible cyber security domains suitable for domain-specific attack languages are generic types such as cloud systems or embedded systems but may also be highly specialized kinds, e.g. Ubuntu installations; the objects of interest as well as the attack logic will differ significantly between such domains. In this paper, we present the Meta Attack Language (MAL), which may be used to design domain-specific attack languages such as the aforementioned. The MAL provides a formalism that allows the semi-automated generation as well as the efficient computation of very large attack graphs. We declare the formal background to MAL, define its syntax and semantics, exemplify its use with a small domain-specific language and instance model, and report on the computational performance.

For the occurrence of network attacks, the most important thing for network security managers is how to conduct attack security defenses under low-risk control. And in the attack risk control, the first and most important step is to choose the defense node of risk control. In this paper, aiming to solve the problem of network attack security risk control under complex networks, we propose a game attack risk control node selection method based on game theory. The method utilizes the relationship between the vulnerabilities and analyzes the vulnerability intent information of the complex network to construct an attack risk diffusion network. In order to truly reflect the different meanings of each node in the attack risk diffusion network for attack and defense, this paper uses the host vulnerability attack and defense income evaluation calculation to give each node in the network its offensive and defensive income. According to the above-mentioned attack risk spread network of offensive and defensive gains, this paper combines game theory and maximum benefit ideas to select the best Top defense node information. In this paper, The method proposed in this paper can be used to select network security risk control nodes on complex networks, which can help network security managers to play a good auxiliary role in cyber attack defense.

Cyber situational awareness has become increasingly important for proactive risk management to help detect and mitigate cyber attacks. Being aware of the importance of individual information system assets to the goal or mission of the organisation is critical to help minimise enterprise risk. However current risk assessment methodologies do not give explicit support to assess mission related asset criticality. This paper describes ongoing efforts within the H2020 PROTECTIVE project to define a practical mission-centric risk assessment methodology for use across diverse organisation types.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

MANET is vulnerable to so many attacks like Black hole, Wormhole, Jellyfish, Dos etc. Attackers can easily launch Wormhole attack by faking a route from original within network. In this paper, we propose an algorithm on AD (Absolute Deviation) of statistical approach to avoid and prevent Wormhole attack. Absolute deviation covariance and correlation take less time to detect Wormhole attack than classical one. Any extra necessary conditions, like GPS are not needed in proposed algorithms. From origin to destination, a fake tunnel is created by wormhole attackers, which is a link with good amount of frequency level. A false idea is created by this, that the source and destination of the path are very nearby each other and will take less time. But the original path takes more time. So it is necessary to calculate the time taken to avoid and prevent Wormhole attack. Better performance by absolute deviation technique than AODV is proved by simulation, done by MATLAB simulator for wormhole attack. Then the packet drop pattern is also measured for Wormholes using Absolute Deviation Correlation Coefficient.

In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance. This is an important problem in tasks such as consumer health search, where the understandability and trustworthiness of information greatly influence people's decisions based on the search engine results, but common topicality-only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing framework used in the context of consumer health search. We showed how the proposed approach diverges from the UBIRE framework, and how MM can be used to better understand the trade-offs between topical relevance and the other relevance dimensions.

SDN (Software Defined Network) with multiple controllers draws more attention for the increasing scale of the network. The architecture can handle what SDN with single controller is not able to address. In order to understand what this architecture can accomplish and face precisely, we analyze it with formal methods. In this paper, we apply CSP (Communicating Sequential Processes) to model the routing service of SDN under HyperFlow architecture based on OpenFlow protocol. By using model checker PAT (Process Analysis Toolkit), we verify that the models satisfy three properties, covering deadlock freeness, consistency and fault tolerance.

Cloud-based cyber-physical systems, like vehicle and intelligent transportation systems, are now attracting much more attentions. These systems usually include large-scale distributed sensor networks covering various components and producing enormous measurement data. Lots of modeling languages are put to use for describing cyber-physical systems or its aspects, bringing contribution to the development of cyber-physical systems. But most of the modeling techniques only focuse on software aspect so that they could not exactly express the whole cloud-based cyber-physical systems, which require appropriate views and tools in its design; but those tools are hard to be used under systemic or object-oriented methods. For example, the widest used modeling language, UML, could not fulfil the above design's requirements by using the foremer's standard form. This paper presents a method designing the cloud-based cyber-physical systems with AADL, by which we can analyse, model and apply those requirements on cloud platforms ensuring QoS in a relatively highly extensible way at the mean time.

Mobile ad-hoc network (MANET) is a system of wireless mobile nodes that are dynamically self-organized in arbitrary and temporary topologies, that have received increasing interest due to their potential applicability to numerous applications. The deployment of such networks however poses several security challenging issues, due to their lack of fixed communication infrastructure, centralized administration, nodes mobility and dynamic topological changes, which make it susceptible to passive and active attacks such as single and cooperative black hole, sinkhole and eavesdropping attacks. The mentioned attacks mainly disrupt data routing processes by giving false routing information or stealing secrete information by malicious nodes in MANET. Thus, finding safe routing path by avoiding malicious nodes is a genuine challenge. This paper aims at combining the existing cooperative bait detection scheme which uses the baiting procedure to bait malicious nodes into sending fake route reply and then using a reverse tracing operation to detect the malicious nodes, with an RSA encryption technique to encode data packet before transmitting it to the destination to prevent eavesdropper and other malicious nodes from unauthorized read and write on the data packet. The proposed work out performs the existing Cooperative Bait Detection Scheme (CBDS) in terms of packet delivery ratio, network throughput, end to end delay, and the routing overhead.

Fully homomorphic encryption (FHE) makes it easier for cloud computing to be consistent with privacy. But the efficiency of existing FHE schemes is still far from the actual needs. The main cause is that most of existing FHE schemes are single-bit encryption. Hiromasa, Abe and Okamoto (PKC 2015) reached the major milestone by constructing the first fully homomorphic encryption (FHE) scheme that encrypted message matrices (with single-bit matrices components) and supported homomorphic matrix addition and multiplication. In this paper, we propose a more efficient variant of Hiromasa, Abe and Okamoto with a lower factor noise-expansion factor for homomorphic multiplication from $\Theta$(poly(n)) to $\Theta$(1) and multi-bit matrices components.

In this paper, we analyze the impact of the antenna correlation on the secrecy of multiple-input multiple-output (MIMO) wiretap channels with multiple eavesdroppers, where transmit antenna selection (TAS) and maximal-ratio combining (MRC) are employed at the transmitter, receiver and eavesdroppers, respectively. For the practical passive eavesdropping, we first develop new and closed general formulas for the secrecy outage probability and the probability of non-zero secrecy capacity to characterize the effect of spatial correlation, and results prove that the enhanced security performance can be achieved when multiple antennas are provided at the transmitter. We then explore how spatial correlation affects the asymptotic secrecy outage probability, and the secrecy diversity order is revealed. Based on these, the results show that when the average SNR of the main channel is relatively low, higher antenna correlation is more perfect to the secrecy. When the average SNR of the main channel is relatively high, higher antenna correlation is more destructive to the secrecy.

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors'' deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NetWalk has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3) flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NetWalk.

Permissioned Blockchain (PBC) has become a prevalent data structure to ensure that the records are immutable and secure. However, PBC still has significant challenges before it can be realized in different applications. One of such challenges is the overhead of the communication which is required to execute the Byzantine Agreement (BA) protocol that is needed for consensus building. As such, it may not be feasible to implement PBC for resource constrained environments such as Internet-of-Things (IoT). In this paper, we assess the communication overhead of running BA in an IoT environment that consists of wireless nodes (e.g., Raspberry PIs) with meshing capabilities. As the the packet loss ratio is significant and makes BA unfeasible to scale, we propose a network coding based approach that will reduce the packet overhead and minimize the consensus completion time of the BA. Specifically, various network coding approaches are designed as a replacement to TCP protocol which relies on unicasting and acknowledgements. The evaluation on a network of Raspberry PIs demonstrates that our approach can significantly improve scalability making BA feasible for medium size IoT networks.

Motivated by networked systems in which the functionality of the network depends on vertices in the network being within a bounded distance T of each other, we study the length-bounded multicut problem: given a set of pairs, find a minimum-size set of edges whose removal ensures the distance between each pair exceeds T . We introduce the first algorithms for this problem capable of scaling to massive networks with billions of edges and nodes: three highly scalable algorithms with worst-case performance ratios. Furthermore, one of our algorithms is fully dynamic, capable of updating its solution upon incremental vertex / edge additions or removals from the network while maintaining its performance ratio. Finally, we show that unless NP ⊆ BPP, there is no polynomial-time, approximation algorithm with performance ratio better than Omega (T), which matches the ratio of our dynamic algorithm up to a constant factor.

A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.

Mobile ad hoc networks (MANETs) are a set of mobile wireless nodes that can communicate without the need for an infrastructure. Features of MANETs have made them vulnerable to many security attacks including wormhole attack. In the past few years, different methods have been introduced for detecting, mitigating, and preventing wormhole attacks in MANETs. In this paper, we introduce a new decentralized scheme based on statistical metrics for detecting wormholes that employs “number of new neighbors” along with “number of neighbors” for each node as its parameters. The proposed scheme has considerably low detection delay and does not create any traffic overhead for routing protocols which include neighbor discovery mechanism. Also, it possesses reasonable processing power and memory usage. Our simulation results using NS3 simulator show that the proposed scheme performs well in terms of detection accuracy, false positive rate and mean detection delay.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.