Biblio

SUMMARY & CONCLUSIONSA Multi-access Edge Computing (MEC) micro data center (MEDC) consists of multiple MEC hosts close to endpoint devices. MEC service is delivered by instantiating a virtualization system (e.g., Virtual Machines or Containers) on a MEC host. MEDC faces more new security risks due to various device connections in an open environment. When more and more IoT/CPS systems are connected to MEDC, it is necessary for MEC service providers to quantitatively analyze any security loss and then make defense-related decision. This paper develops a CTMC model for quantitatively analyzing the security and dependability of a vulnerable MEDC system under lateral movement attacks, from the adversary’s initial successful access until the MEDC becomes resistant to the attack. The proposed model captures the behavior of the system in a scenario where (i) the rate of vulnerable MEC servers being infected increases with the increasing number of infected MEC servers, (ii) each infected MEC server can perform its compromising activity independently and randomly, and (iii) any infected MEC may fail and then cannot provide service. We also introduce the formulas for computing metrics. The proposed model and formula are verified to be approximately accurate by comparing numerical results and simulation results.

We investigate a compressive sampling (CS) stepped frequency ground penetrating radar for detection of underground objects, which uses Bayesian estimation and a probabilistic model for the target support. Due to the underground targets being sparse, the B-scan is a sparse image. Using the CS principle, the stepped frequency radar is implemented using a subset of random frequencies at each antenna position. For image reconstruction we use Markov Chain and Markov Random Field models for the target support in the B-scan, where we also estimate the model parameters using the Expectation Maximization algorithm. The approach is tested using Web radar data obtained by measuring the signal responses scattered off land mine targets in a laboratory experimental setup. Our approach results in improved performance compared to the standard denoising algorithm for image reconstruction.

Modern cyberattacks are the most powerful disturbance factor for computer networks, as they have a complex and devastating impact. The impact of cyberattacks is primarily aimed at disrupting the performance of computer network protection means. Therefore, managing this defense system in the face of cyberattacks is an important task. The paper examines a technique for constructing an effective control system for a computer network security system operating in real time in the context of cyber attacks. It is supposed that it is built on the basis of constructing a system state space and a stack of control decisions. The probability of finding the security system in certain state at each control step is calculated using a finite Markov chain. The technique makes it possible to predict the number of iterations for managing the security system when exposed to cyber attacks, depending on the segment of the space of its states and the selected number of transitions, as well as automatically generate control decisions. An algorithm has been developed for situational control of a computer network security system in conditions of cyber attacks. The experimental results obtained using the generated dataset demonstrated the high efficiency of the developed technique and the ability to use it to determine the parameters that are most susceptible to abnormal deviations during the impact of cyber attacks.

In this paper, we propose a relational anonymous P2P communication network evaluation model based on Markov chain (AEMC), and show how to extend our model to the anonymous evaluation of sender and receiver relationship anonymity when the attacker attacks the anonymous P2P communication network and obtains some information. Firstly, the constraints of the evaluation model (the attacker assumption for message tracing) are specified in detail; then the construction of AEMC anonymous evaluation model and the specific evaluation process are described; finally, the simulation experiment is carried out, and the evaluation model is applied to the probabilistic anonymous evaluation of the sender and receiver relationship of the attacker model, and the evaluation is carried out from the perspective of user (message).

Medical institutions are increasingly adopting IoT platforms to share data, communicate rapidly and improve healthcare treatment abilities. However, this trend is also raising the risk of potential data manipulation attacks. In decentralized networks, defense mechanisms against external entities have been widely enabled while protection against insider attackers is still the weakest link of the chain. Most of the platforms are based on the assumption that all the insider nodes are trustworthy. However, these nodes are exploiting of this assumption to lead manipulation attacks and violate data integrity and reliability without being detected. To address this problem, we propose a secure decentralized management system able to detect insider malicious nodes. Our proposal is based on a three layer architecture: storage layer, blockchain based network layer and IoT devices layer. In this paper, we mainly focus on the network layer where we propose to integrate a decentralized trust based authorization module. This latter allows updating dynamically the nodes access rights by observing and evaluating their behavior. To this aim, we combine probabilistic modelling and stochastic modelling to classify and predict the nodes behavior. Conducted performance evaluation and security analysis show that our proposition provides efficient detection of malicious nodes compared to other trust based management approaches.

MIL-STD-1553 is a military standard that defines the specification of a serial communication bus that has been implemented in military and aerospace avionic platforms for over 40 years. MIL-STD-1553 was designed for a high level of fault tolerance while less attention was paid to cyber security issues. Thus, as indicated in recent studies, it is exposed to various threats. In this article, we suggest enhancing the security of MIL-STD-1553 communication buses by integrating a machine learning-based intrusion detection system (IDS); such anIDS will be capable of detecting cyber attacks in real time. The IDS consists of two modules: 1) a remote terminal (RT) authentication module that detects illegitimately connected components and data transfers and 2) a sequence-based anomaly detection module that detects anomalies in the operation of the system. The IDS showed high detection rates for both normal and abnormal behavior when evaluated in a testbed using real 1553 hardware, as well as a very fast and accurate training process using logs from a real system. The RT authentication module managed to authenticate RTs with +0.99 precision and +0.98 recall; and detect illegitimate component (or a legitimate component that impersonates other components) with +0.98 precision and +0.99 recall. The sequence-based anomaly detection module managed to perfectly detect both normal and abnormal behavior. Moreover, the sequencebased anomaly detection module managed to accurately (i.e., zero false positives) model the normal behavior of a real system in a short period of time ( 22 s).

Cyber-physical systems (CPS) depend on cybersecurity to ensure functionality, data quality, cyberattack resilience, etc. There are known and unknown cyber threats and attacks that pose significant risks. Information assurance and information security are critical. Many systems are vulnerable to intelligence exploitation and cyberattacks. By investigating cybersecurity risks and formal representation of CPS using spatiotemporal dynamic graphs and networks, this paper investigates topics and solutions aimed to examine and empower: (1) Cybersecurity capabilities; (2) Information assurance and system vulnerabilities; (3) Detection of cyber threat and attacks; (4) Situational awareness; etc. We introduce statistically-characterized dynamic graphs, novel entropy-centric algorithms and calculi which promise to ensure near-real-time capabilities.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple.

We investigate an implementation of a compressive sampling (CS) stepped frequency ground penetrating radar. Due to the small number of targets, the B-scan is represented as a sparse image. Due to the nature of stepped frequency radar, smaller number of random frequencies can be used to obtain each A-scan (sparse delays). Also, the measurements obtained from different antenna positions can be reduced to a smaller number of random antenna positions. We also use the structure in the B-scan, i.e. the shape of the targets, which can be known, for instance, when detecting land mines. We demonstrate our method using radar data available from the Web from the land mine targets buried in the ground. We use group sparsity, i.e. we assume that the targets have some non-zero (and presumably known) dimension in the cross-range coordinate of the B-scan. For such targets, we also use the Markov chain model for the targets, where we simultaneously estimate the model parameters using the EMturboGAMP algorithm. Both approaches result in improved performance.

This paper surveys some prior work regarding attack models in a cyber-physical system and discusses the potential benefits. For comparison, the full paper will model a bad data injection attack scenario in power grid using the surveyed prior work.

A detailed model of an attack on the power grid involves both a preparation stage as well as an execution stage of the attack. This paper introduces a novel Hybrid Attack Model (HAM) that combines Probabilistic Learning Attacker, Dynamic Defender (PLADD) model and a Markov Chain model to simulate the planning and execution stages of a bad data injection attack in power grid. We discuss the advantages and limitations of the prior work models and of our proposed Hybrid Attack Model and show that HAM is more effective compared to individual PLADD or Markov Chain models.

Near Field Communication (NFC) is a short- range wireless communication technology that supports several features, one of which is an electronic payment. NFC works at a limited distance to exchange information. In terms of security, NFC technology has a gap for attackers to carry out attacks by forwarding information illegally using the target NFC network. A relay attack that occurs due to the theft of some data by an attacker by utilizing close communication from NFC is one of them. Relay attacks can cause a lot of loss in terms of material sacrifice. It takes countermeasures to overcome the problem of electronic payments with NFC technology. Detection of anomalous data is one way that can be done. In an attack, several abnormalities can be detected which can be used to prevent an attack. Markov Chain is one method that can be used to detect relay attacks that occur in electronic payments using NFC. The result shows Markov chain can detect anomalies in relay attacks in the case of electronic payment.

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.

Quantifying vulnerability and security levels for smart grid diversified link of networks have been a challenging task for a long period of time. Security experts and network administrators used to act based on their proficiencies and practices to mitigate network attacks rather than objective metrics and models. This paper uses the Markov Chain Model [1] to evaluate quantitatively the vulnerabilities associated to the 802.11 Wi-Fi network in a smart grid. Administrator can now assess the level of severity of potential attacks based on determining the probability density of the successive states and thus, providing the corresponding security measures. This model is based on the observed vulnerabilities provided by the Common Vulnerabilities and Exposures (CVE) database explored by MITRE [2] to calculate the Markov processes (states) transitions probabilities and thus, deducing the vulnerability level of the entire attack paths in an attack graph. Cumulative probabilities referring to high vulnerability level in a specific attack path will lead the system administrator to apply appropriate security measures a priori to potential attacks occurrence.

The paper dwells on the design of a diagnostic system and expert assessment of the significance of threats to the security of industrial networks. The proposed system is based on a new cyber-attacks classification and presupposes the existence of two structural blocks: the industrial network virtual model based on the scan selected nodal points and the generator of cyber-attacks sets. The diagnostic and expert assessment quality is improved by the use of the Markov chains or the Monte Carlo numerical method. The numerical algorithm of generating cyber-attacks sets is based on the LP$\tau$-sequence.

The prevalence of mobile devices and location-based services (LBS) has generated great concerns regarding the LBS users' privacy, which can be compromised by statistical analysis of their movement patterns. A number of algorithms have been proposed to protect the privacy of users in such systems, but the fundamental underpinnings of such remain unexplored. Recently, the concept of perfect location privacy was introduced and its achievability was studied for anonymization-based LBS systems, where user identifiers are permuted at regular intervals to prevent identification based on statistical analysis of long time sequences. In this paper, we significantly extend that investigation by incorporating the other major tool commonly employed to obtain location privacy: obfuscation, where user locations are purposely obscured to protect their privacy. Since anonymization and obfuscation reduce user utility in LBS systems, we investigate how location privacy varies with the degree to which each of these two methods is employed. We provide: (1) achievability results for the case where the location of each user is governed by an i.i.d. process; (2) converse results for the i.i.d. case as well as the more general Markov Chain model. We show that, as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect location privacy; and, in the second region, no user has location privacy.

As one of the next generation network architectures, Named Data Networking(NDN) which features location-independent addressing and content caching makes it more suitable to be deployed into Vehicular Ad-hoc Network(VANET). However, a new attack pattern is found when NDN and VANET combine. This new attack is Interest Packet Popple Broadcast Diffusion Attack (PBDA). There is no mitigation strategies to mitigate PBDA. In this paper a mitigation strategies called RVMS based on node reputation value (RV) is proposed to detect malicious nodes. The node calculates the neighbor node RV by direct and indirect RV evaluation and uses Markov chain predict the current RV state of the neighbor node according to its historical RV. The RV state is used to decide whether to discard the interest packet. Finally, the effectiveness of the RVMS is verified through modeling and experiment. The experimental results show that the RVMS can mitigate PBDA.

Simple connectivity and data requirements together with high lifetime of battery are the main issues for the machine-to-machine (M2M) communications. 3GPP focuses on three main licensed standardizations based on Long Term Evolution (LTE), GSM and clean-slate technologies. The paper considers the last one and proposes a modified slotted-Aloha method to increase the capability of supporting a massive number of low-throughput devices. The proposed method increases the access rate of users belonging to each class considered in the clean-slate standard and consequently the total throughput offered by the system. To derive the mean access rate per class, we use the Markov chain approach and simulation results are provided for scenarios with different data rate and also in terms of cell average delay.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

Although computational systems are looking towards post CMOS devices in the pursuit of lower power, the expected inherent unreliability of such devices makes it difficult to design robust systems without additional power overheads for guaranteeing robustness. As such, algorithmic structures with inherent ability to tolerate computational errors are of significant interest. We propose to cast applications as stochastic algorithms based on Markov chains (MCs) as such algorithms are both sufficiently general and tolerant to transition errors. We show with four example applications—Boolean satisfiability, sorting, low-density parity-check decoding and clustering—how applications can be cast as MC algorithms. Using algorithmic fault injection techniques, we demonstrate the robustness of these implementations to transition errors with high error rates. Based on these results, we make a case for using MCs as an algorithmic template for future robust low-power systems.

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.

Location privacy preservation has become an important issue in providing location based services (LBSs). When the mobile users report their locations to the LBS server or the third-party servers, they risk the leak of their location information if such servers are compromised. To address this issue, we propose a Location Privacy Preservation Scheme (LPPS) based on distributed cache pushing which is based on Markov Chain. The LPPS deploys distributed cache proxies in the most frequently visited areas to store the most popular location-related data and pushes them to mobile users passing by. In the way that the mobile users receive the popular location-related data from the cache proxies without reporting their real locations, the users' location privacy is well preserved, which is shown to achieve k-anonymity. Extensive experiments illustrate that the proposed LPPS achieve decent service coverage ratio and cache hit ratio with low communication overhead.