Biblio

Cyber adversaries employ a variety of malware and exploits to attack computer systems, usually via sequential or “chained” attacks, that take advantage of vulnerability dependencies. In this paper, we introduce a formalism to model such attacks. We show that the determination of the set of capabilities gained by an attacker, which also translates to extent to which the system is compromised, corresponds with the convergence of a simple fixed-point operator. We then address the problem of determining the optimal/most-dangerous strategy for a cyber-adversary with respect to this model and find it to be an NP-Complete problem. To address this complexity we utilize an A*-based approach with an admissible heuristic, that incorporates the result of the fixed-point operator and uses memoization for greater efficiency. We provide an implementation and show through a suite of experiments, using both simulated and actual vulnerability data, that this method performs well in practice for identifying adversarial courses of action in this domain. On average, we found that our techniques decrease runtime by 82%.

This paper presents a general model to assess the mixed-degradation profiles of critical components in a Cyber-Physical System (CPS) based on the reliability of its critical physical and software components. In the proposed assessment, the cyber aspect of a CPS was approached from a software reliability perspective. Although extensive research has been done on physical components degradation and software reliability separately, research for the combined physical-software systems is still scarce. The non-homogeneous Poisson Processes (NHPP) software reliability models are deemed to fit well with the real data and have descriptive and predictive abilities, which could make them appropriate to estimate software components reliability. To show the feasibility of the proposed approach, a case study for mixed-degradation profiles assessment is presented with n physical components and one major software component forming a critical subsystem in CPS. Two physical components were assumed to have different degradation paths with the dependency between them. Series and parallel structures were investigated for physical components. The software component failure data was taken from a wireless network switching center and fitted into a Weibull software reliability model. The case study results revealed that mix-degradation profiles of physical components, combined with software component profile, produced a different CPS reliability profile.

The article considers some aspects of modeling information security circuits for information and communication systems used in Smart City. As a basic research paradigm, the postulates of game theory and mathematical dependencies based on Markov processes were used. Thus, it is possible to sufficiently substantively describe the procedure for selecting rational variants of cyber security systems used to protect information technologies in Smart City. At the same time, using the model proposed by us, we can calculate the probability of cyber threats for the Smart City systems, as well as the cybernetic risks of diverse threats. Further, on the basis of the described indicators, rational contour options are chosen to protect the information systems used in Smart City.

Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated setting and suggest multiple improvements for more efficient privacy budgeting at different levels. Our experiments show significant advantage over the state-of-the-art differential privacy bounds for federated learning on image classification tasks, including a medical application, bringing the privacy budget below ε = 1 at the client level, and below ε = 0.1 at the instance level. Lower amounts of noise also benefit the model accuracy and reduce the number of communication rounds.

Security challenges present in Machine-to-Machine Communication (M2M-C) and big data paradigm are fundamentally different from conventional network security challenges. In M2M-C paradigms, “Trust” is a vital constituent of security solutions that address security threats and for such solutions,it is important to quantify and evaluate the amount of trust in the information and its source. In this work, we focus on Machine Learning (ML) Based Trust (MLBT) evaluation model for detecting malicious activities in a vehicular Based M2M-C (VBM2M-C) network. In particular, we present an Entropy Based Feature Engineering (EBFE) coupled Extreme Gradient Boosting (XGBoost) model which is optimized with Binary Particle Swarm optimization technique. Based on three performance metrics, i.e., Accuracy Rate (AR), True Positive Rate (TPR), False Positive Rate (FPR), the effectiveness of the proposed method is evaluated in comparison to the state-of-the-art ensemble models, such as XGBoost and Random Forest. The simulation results demonstrates the superiority of the proposed model with approximately 10% improvement in accuracy, TPR and FPR, with reference to the attacker density of 30% compared with the start-of-the-art algorithms.

For multiple embedded computers working together, the functional failure resulting from the underlying hardware or system crash will cause a sudden abort of applications. Different types of applications may have security requirements for data isolation and access control. Therefore, we propose a scheme to implement multi-level security domain dynamic management oriented embedded computers based on MILS architecture. Firstly, the scheme builds local security policy items and access control lists according to type, function and security level. After that, security domain of all applications is constructed to achieve the safety purpose that applications can perform migration cross partitions and cross platforms. Our experiments and analysis show that the proposed scheme is feasible and correct.

The internet of things (IoT) is the popular wireless network for data collection applications. The IoT networks are deployed in dense or sparse architectures, out of which the dense networks are vastly popular as these are capable of gathering the huge volumes of data. The collected data is analyzed using the historical or continuous analytical systems, which uses the back testing or time-series analytics to observe the desired patterns from the target data. The lost or bad interval data always carries the high probability to misguide the analysis reports. The data is lost due to a variety of reasons, out of which the most popular ones are associated with the node failures and connectivity holes, which occurs due to physical damage, software malfunctioning, blackhole/wormhole attacks, route poisoning, etc. In this paper, the work is carried on the new routing scheme for the IoTs to avoid the connectivity holes, which analyzes the activity of wireless nodes and takes the appropriate actions when required.

Shamir's Secret Sharing Scheme is well established and widely used. It allows a so-called Dealer to split and share a secret k among n Participants such that at least t shares are needed to reconstruct k, where 0 \textbackslashtextbackslashtextless; t ≤ n. Nothing about the secret can be learned from less than t shares. To split secret k, the Dealer generates a polynomial f, whose independent term is k and the coefficients are randomly selected using a uniform distribution. A share is a pair (x, f(x)) where x is also chosen randomly using a uniform distribution. This scheme is useful, for example, to distribute cryptographic keys among different cloud providers and to create multi-factor authentication. The security of Shamir's Secret Sharing Scheme is usually analyzed using a threat model where the Dealer is trusted to split and share secrets as described above. In this paper, we demonstrate that there exists a different threat model where a malicious Dealer can compute shares such that a subset of less than t shares is allowed to reconstruct the secret. We refer to such subsets as hidden sets. We formally define hidden sets and prove lower bounds on the number of possible hidden sets for polynomials of degree t - 1. Yet, we show how to detect hidden sets given a set of n shares and describe how to create hidden sets while sharing a secret using a modification of Shamir's scheme.

With the increasing interest in studying Automated Driving System (ADS)-equipped vehicles through simulation, there is a growing need for comprehensive and agile middleware to provide novel Virtual Analysis (VA) functions of ADS-equipped vehicles towards enabling a reliable representation for pre-deployment test. The National Institute of Standards and Technology (NIST) Universal Cyber-physical systems Environment for Federation (UCEF) is such a VA environment. It provides Application Programming Interfaces (APIs) capable of ensuring synchronized interactions across multiple simulation platforms such as LabVIEW, OMNeT++, Ricardo IGNITE, and Internet of Things (IoT) platforms. UCEF can aid engineers and researchers in understanding the impact of different constraints associated with complex cyber-physical systems (CPS). In this work UCEF is used to produce a simulated Operational Domain Design (ODD) for ADS-equipped vehicles where control (drive cycle/speed pattern), sensing (obstacle detection, traffic signs and lights), and threats (unusual signals, hacked sources) are represented as UCEF federates to simulate a drive cycle and to feed it to vehicle dynamics simulators (e.g. OpenModelica or Ricardo IGNITE) through the Functional Mock-up Interface (FMI). In this way we can subject the vehicle to a wide range of scenarios, collect data on the resulting interactions, and analyze those interactions using metrics to understand trustworthiness impact. Trustworthiness is defined here as in the NIST Framework for Cyber-Physical Systems, and is comprised of system reliability, resiliency, safety, security, and privacy. The goal of this work is to provide an example of an experimental design strategy using Fractional Factorial Design for statistically assessing the most important safety metrics in ADS-equipped vehicles.

Statistics suggests, proceeding towards IoT generation, is increasing IoT devices at a drastic rate. This will be very challenging for our present-day network infrastructure to manage, this much of data. This may risk, both security and traffic collapsing. We have proposed an infrastructure with Fog Computing. The Fog layer consists two layers, using the concepts of Service oriented Architecture (SOA) and the Agent based composition model which ensures the traffic usage reduction. In order to have a robust and secured system, we have modified the Fog based agent model by replacing the SOA with secured Named Data Network (NDN) protocol. Knowing the fact that NDN has the caching layer, we are combining NDN and with Fog, as it can overcome the forwarding strategy limitation and memory constraints of NDN by the Agent Society, in the Middle layer along with Trust management.

We propose a novel application of coded computing to the problem of the nearest neighbor estimation using MatDot Codes (Fahim et al., Allerton'17) that are known to be optimal for matrix multiplication in terms of recovery threshold under storage constraints. In approximate nearest neighbor algorithms, it is common to construct efficient in-memory indexes to improve query response time. One such strategy is Multiple Random Projection Trees (MRPT), which reduces the set of candidate points over which Euclidean distance calculations are performed. However, this may result in a high memory footprint and possibly paging penalties for large or high-dimensional data. Here we propose two techniques to parallelize MRPT that exploit data and model parallelism respectively by dividing both the data storage and the computation efforts among different nodes in a distributed computing cluster. This is especially critical when a single compute node cannot hold the complete dataset in memory. We also propose a novel coded computation strategy based on MatDot codes for the model-parallel architecture that, in a straggler-prone environment, achieves the storage-optimal recovery threshold, i.e., the number of nodes that are required to serve a query. We experimentally demonstrate that, in the absence of straggling, our distributed approaches require less query time than execution on a single processing node, providing near-linear speedups with respect to the number of worker nodes. Our experiments on real systems with simulated straggling, we also show that in a straggler-prone environment, our strategy achieves a faster query execution than the uncoded strategy.

Cloud service has the computing characteristics of self-organizing strain on demand, which is prone to failure or loss of responsibility in its extensive application. In the prediction or accountability of this, the modeling of cloud service structure becomes an insurmountable priority. This paper reviews the modeling of cloud service network architecture. It mainly includes: Firstly, the research status of cloud service structure modeling is analyzed and reviewed. Secondly, the classification of time-varying structure of cloud services and the classification of time-varying structure modeling methods are summarized as a whole. Thirdly, it points out the existing problems. Finally, for cloud service accountability, research approach of time-varying structure modeling is proposed.

The exponential growth of the Internet and its users and the emergence of Web 2.0 have caused a large volume of textual data to be created. Automatic analysis of such data can be used in making decisions. As online text is created by different producers with different styles of writing, pre-processing is a necessity prior to any processes related to natural language tasks. An essential part of textual preprocessing prior to the recognition of the word vocabulary is normalization, which includes the correction of spaces that particularly in the Persian language this includes both full-spaces between words and half-spaces. Through the review of user comments within social media services, it can be seen that in many cases users do not adhere to grammatical rules of inserting both forms of spaces, which increases the complexity of the identification of words and henceforth, reducing the accuracy of further processing on the text. In this study, current issues in the normalization and tokenization of preprocessing tools within the Persian language and essentially identifying and correcting the separation of words are and the correction of spaces are proposed. The results obtained and compared to leading preprocessing tools highlight the significance of the proposed methodology.

In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.

We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients over the Internet. We study a proactive moving-target defense to thwart a DDoS attacker's reconnaissance phase and consequently reduce the attack's impact. The defense is effectively a moving-target (motag) technique in which the proxies dynamically change. The system is evaluated using an AWS prototype of HTTP redirection and by numerical evaluations of an “adversarial” coupon-collector mathematical model, the latter allowing larger-scale extrapolations.

Blockchain is the one of leading technology of this time; it has started to revolutionize several fields like, finance, business, industry, smart home, healthcare, social networks, Internet and the Internet of Things. It has many benefits like, decentralized network, robustness, availability, stability, anonymity, auditability and accountability. The applications of Blockchain are emerging, and it is found that most of the work is focused on its engineering implementation. While the theoretical part is very less considered and explored. In this paper we implemented the simulation of mining process in Blockchain based systems using queuing theory. We took the parameters of one of the mature Cryptocurrency, Bitcoin's real data and simulated using M/M/n/L queuing system in JSIMgraph. We have achieved realistic results; and expect that it will open up new research direction in theoretical research of Blockchain based systems.

This paper work is focused on Performance comparison of intrusion detection system between DBN Algorithm and SPELM Algorithm. Researchers have used this new algorithm SPELM to perform experiments in the area of face recognition, pedestrian detection, and for network intrusion detection in the area of cyber security. The scholar used the proposed State Preserving Extreme Learning Machine(SPELM) algorithm as machine learning classifier and compared it's performance with Deep Belief Network (DBN) algorithm using NSL KDD dataset. The NSL- KDD dataset has four lakhs of data record; out of which 40% of data were used for training purposes and 60% data used in testing purpose while calculating the performance of both the algorithms. The experiment as performed by the scholar compared the Accuracy, Precision, recall and Computational Time of existing DBN algorithm with proposed SPELM Algorithm. The findings have show better performance of SPELM; when compared its accuracy of 93.20% as against 52.8% of DBN algorithm;69.492 Precision of SPELM as against 66.836 DBN and 90.8 seconds of Computational time taken by SPELM as against 102 seconds DBN Algorithm.

The rapid development of the Cloud Computing Technologies (CCTs) has amended the conventional design of resource-constrained Network Control System (NCS) to the powerful and flexible design of Cloud-Based Networked Control System (CB-NCS) by relocating the processing part to the cloud server. This arrangement has produced many internets based exquisite applications. However, this new arrangement has also raised many network security challenges for the cloud-based control system related to cyber-physical part of the system. In the absence of robust verification methodology, an attacker can launch the modification attack in order to destabilize or take control of NCS. It is desirable that there shall be a solution authentication methodology used to verify whether the incoming solutions are coming from the cloud or not. This paper proposes a methodology used for the verification of the receiving solution to the local control system from the cloud using Karush-Kuhn-Tucker (KKT) conditions, which is then applied to actuator after verification and thus ensure the stability in case of modification attack.

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.

The aim of this paper is to explore the performance of two well-known wave energy converters (WECs) namely Floating Buoy Point Absorber (FBPA) and Oscillating Surge (OS) in onshore and offshore locations. To achieve clean energy targets by reducing greenhouse gas emissions, integration of renewable energy resources is continuously increasing all around the world. In addition to widespread renewable energy source such as wind and solar photovoltaic (PV), wave energy extracted from ocean is becoming more tangible day by day. In the literature, a number of WEC devices are reported. However, further investigations are still needed to better understand the behaviors of FBPA WEC and OS WEC under irregular wave conditions in onshore and offshore locations. Note that being surrounded by Bay of Bengal, Bangladesh has huge scope of utilizing wave power. To this end, FBPA WEC and OS WEC are simulated using the typical onshore and offshore wave height and wave period of the coastal area of Bangladesh. Afterwards, performances of the aforementioned two WECs are compared by analyzing their power output.

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.

The increasing publication of large amounts of data, theoretically anonymous, can lead to a number of attacks on the privacy of people. The publication of sensitive data without exposing the data owners is generally not part of the software developers concerns. The regulations for the data privacy-preserving create an appropriate scenario to focus on privacy from the perspective of the use or data exploration that takes place in an organization. The increasing number of sanctions for privacy violations motivates the systematic comparison of three known machine learning algorithms in order to measure the usefulness of the data privacy preserving. The scope of the evaluation is extended by comparing them with a known privacy preservation metric. Different parameter scenarios and privacy levels are used. The use of publicly available implementations, the presentation of the methodology, explanation of the experiments and the analysis allow providing a framework of work on the problem of the preservation of privacy. Problems are shown in the measurement of the usefulness of the data and its relationship with the privacy preserving. The findings motivate the need to create optimized metrics on the privacy preferences of the owners of the data since the risks of predicting sensitive attributes by means of machine learning techniques are not usually eliminated. In addition, it is shown that there may be a hundred percent, but it cannot be measured. As well as ensuring adequate performance of machine learning models that are of interest to the organization that data publisher.

There are increasing threats for cyberspace. This paper tries to identify how extreme cybersecurity incidents occur based on the scenario of a targeted attack through emails. Knowledge on how extreme cybersecurity incidents occur helps in identifying the key points on how they can be prevented from occurring. The model based on system thinking approach to the understanding how communication influences entities and how tiny initiating events scale up into extreme events provides a condensed figure of the cyberspace and surrounding threats. By taking cyberspace layers and characteristics of cyberspace identified by this model into consideration, it predicts most suitable risk mitigations.