Biblio

The competency to use mobile devices for work-related tasks gives advantages to the company productiveness and expedites business processes. Thus Bring Your Own Device (BYOD) setting emerge to enable work flexibility and technological compatibility. For management, employees’ productivity is important, but they could not jeopardise the security of information and data stored in the corporate network. Securing data and network becomes more complex tasks as it deals with foreign devices, i.e., devices that do not belong to the organisation. With much research focused on pre-implementation and the technical aspects of mobile device usage, post-implementation advancement is receiving less attention. IS audit as one of the post-implementation mechanisms provides performance evaluation of existing IS assets, business operations and process implementation, thus helping management formulating the best strategies in optimising IS practices. This paper discusses the feasibility of IS audit in assessing mobile device security by exploring the risks and vulnerabilities of mobile devices for organisational IS security as well as the perception of Information system management in mobile device security. By analysing related literature, authors pointed out how the references used in the current IS audit research address the mobile device security. This work serves a significant foundation in the future development in mobile device audit.

Security has become an important issue in an IT system of an organization. Each IT component has to be configured correctly, otherwise the risk of attack could increase. An important component is networking device such as router and switch. To avoid this misconfiguration, a well-known process called audit is used. There are several auditing tools both commercial and open-source. However, none of the existing tools that are open-source can automatically audit the security settings of networking device based on standard e.g., CIS benchmark. We, thus propose a tool that can verify the networking device automatically based on best practices so that auditors can conveniently check as well as issue a report.

An Information security audit is a process of obtaining objective audit evidence and evaluating it objectively for compliance with audit criteria. Given resource constraints, it's advisable to focus on obtaining evidence that has a significant impact on its effectiveness when developing an audit program to organize the audit. The person managing the audit program faces an urgent task developing an audit program, taking into account the information content of extracted evidence and resource constraints. In practice, evidence cannot be evaluated correctly directly in numerical scales, so they are forced to use less informative scales. The purpose of scientific research is to develop a methodology for assessing the materiality of audit evidence using expert assessments, their statistical processing, and transition to quantitative scales. As a result, the person managing the audit program gets a tool for developing an effective audit program.

Artificial intelligence technology is becoming more active in all areas of our lives day by day. This technology affects our daily life by more developing in areas such as industry 4.0, security and education. Deep reinforcement learning is one of the most developed algorithms in the field of artificial intelligence. In this study, it is aimed that three different robots in a limited area learn to move without hitting each other, fixed obstacles and the boundaries of the field. These robots have been trained using the deep reinforcement learning approach and Proximal policy optimization (PPO) policy. Instead of uses value-based methods with the discrete action space, PPO that can easily manipulate the continuous action field and successfully determine the action of the robots has been proposed. PPO policy achieves successful results in multi-agent problems, especially with the use of the Actor-Critic network. In addition, information is given about environment control and learning approaches for swarm behavior. We propose parameter sharing and behavior-based method for this study. Finally, trained model is recorded and tested in 9 different environments where the obstacles are located differently. With our method, robots can perform their tasks in closed environments in the real world without damaging anyone or anything.

With the rapid development of smart grid, the data generated by grid services are growing rapidly, and the requirements for time delay are becoming more and more stringent. The storage and computing capacity of the existing terminal equipment can not meet the needs of high bandwidth and low delay of the system at the same time. Fortunately, mobile edge computing (MEC) can provide users with nearby storage and computing services at the network edge, this can give an option to simultaneously meet the requirement of high bandwidth and low delay. Aiming at the problem of service offload scheduling in edge computing, this paper proposes a delay optimized task offload algorithm based on task priority classification. Firstly, the priority of power grid services is divided by using analytic hierarchy process (AHP), and the processing efficiency and quality of service of emergency tasks are guaranteed by giving higher weight coefficients to delay constraints and security levels. Secondly, the service is initialized and unloaded according to the task preprocessing time. Finally, the reasonable subchannel allocation is carried out based on the task priority design decision method. Simulation results show that compared with the traditional approaches, our algorithm can effectively improve the overall system revenue and reduce the average user task delay.

This study used the support vector machine (SVM) algorithm to predict Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks on a proxy server. Proxy-servers are prone to attacks such as DoS and DDoS and existing detection and prediction systems are inefficient. Three convex optimization problems using the Gaussian, linear and non-linear kernel methods were solved using the SVM module to detect the attacks. The SVM module and proxy server were implemented in Python and javascript respectively and made to run on a local network. Four other computers running on the same network where made to each communicate with the proxy server (two dedicated to attack the server). The server was able to detect and filter out the malicious requests from the attacking clients. Hence, the SVM module can effectively predict cyber attacks and can be integrated into any server to detect such attacks for improved security.

In recent years, with the rapid development of DNN, the algorithm complexity in a series of fields such as computer vision and natural language processing is increasing rapidly. FPGA-based DNN accelerators have demonstrated superior flexibility and performance, with higher energy efficiency compared to high-performance devices such as GPU. However, the computing resources of a single FPGA are limited and it is difficult to flexibly meet the requirements of high throughput and high energy efficiency of different computing scales. Therefore, this paper proposes a DNN implementation method based on the scalable heterogeneous FPGA cluster to adapt to different tasks and achieve high throughput and energy efficiency. Firstly, the method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between multiple boards. Secondly, a task deployment method based on dichotomy is proposed to maximize the balance of task execution time of different pipeline stages to improve throughput and energy efficiency. Thirdly, optimize DNN computing module according to the relationship between computing power and bandwidth, and improve energy efficiency by reducing waste of ineffective resources and improving resource utilization. The experiment results on Alexnet and VGG-16 demonstrate that we use Zynq 7035 cluster can at most achieves ×25.23 energy efficiency of optimized AMD AIO processor. Compared with previous works of single FPGA and FPGA cluster, the energy efficiency is improved by 59.5% and 18.8%, respectively.

Generalizing automated medical image segmentation methods to new image domains is inherently difficult. We have previously developed a number of automated segmentation methods that perform at the level of human readers on images acquired under similar conditions to the original training data. We are interested in exploring techniques that will improve model generalization to new imaging domains. In this study we explore a method to limit the inherent bias of these models to intensity and textural information. Using a dataset of 100 T2-weighted MR images with fat-saturation, and 100 T2-weighted MR images without fat-saturation, we explore the use of neural style transfer to induce shape preference and improve model performance on the task of segmenting the kidneys in patients affected by polycystic kidney disease. We find that using neural style transfer images improves the average dice value by 0.2. In addition, visualizing individual network kernel responses highlights a drastic difference in the optimized networks. Biasing models to invoke shape preference is a promising approach to create methods that are more closely aligned with human perception.

In recent years, neural style transfer have shown impressive results in deep learning. In particular, for text style transfer, recent researches have successfully completed the transition from the text font domain to the text style domain. However, for text style transfer, multiple style transfer often requires learning many models, and generating multiple styles images of texts in a single model remains an unsolved problem. In this paper, we propose a multiple style transformation network for text style transfer, which can generate multiple styles of text images in a single model and control the style of texts in a simple way. The main idea is to add conditions to the transfer network so that all the styles can be trained effectively in the network, and to control the generation of each text style through the conditions. We also optimize the network so that the conditional information can be transmitted effectively in the network. The advantage of the proposed network is that multiple styles of text can be generated with only one model and that it is possible to control the generation of text styles. We have tested the proposed network on a large number of texts, and have demonstrated that it works well when generating multiple styles of text at the same time.

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing \$L\$∞ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (Fe-FETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.

K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications.

Wireless Sensor Network (WSN)is the most promising area which is widely used in the field of military, healthcare systems, flood control, and weather forecasting system. In WSN every node is connected with another node and exchanges the information from one to another. While sending data between nodes data security is an important factor. Security is a vital issue in the area of networking. This paper addresses the issue of security in terms of distinct attacks and their solutions provided by the different authors. Whenever data is transferred from source to destination then it follows some route so there is a possibility of a malicious node in the network. It is a very difficult task to identify the malicious node present in the network. Insecurity intruder attacks on data packets that are transferred from one node to another node. While transferring the data from source to destination node hacker hacks the data and changes the actual data. In this paper, we have discussed the numerous security solution provided by the different authors and they had used the Machine Learning (ML) approach to handle the attacks. Various ML techniques are used to determine the authenticity of the node. Network attacks are elaborated according to the layer used for WSN architecture. In this paper, we will categorize the security attacks according to layer-wise and type-wise and represent the solution using the ML technique for handling the security attack.

The increasing use of Multiprocessor Systems-on-Chip (MPSoCs) within scalable multi-tenant systems, such as fog/cloud computing, faces the challenge of potential attacks originated by the execution of malicious tasks. Flooding Denial- of-Service (FDoS) attacks are one of the most common and powerful threats for Network-on-Chip (NoC)-based MPSoCs. Since, by overwhelming the NoC, the system is unable to forward legitimate traffic. However, the effectiveness of FDoS attacks depend on the NoC configuration. Moreover, designing a secure MPSoC capable of detecting such attacks while avoiding excessive power/energy and area costs is challenging. To this end, we present two contributions. First, we demonstrate two types of FDoS attacks: based on the packet injection rate (PIR-based FDoS) and based on the packet's payload length (PPL-based FDoS). We show that fair round-robin NoCs are intrinsically protected against PIR-based FDoS. Instead, PPL-based FDoS attacks represent a real threat to MPSoCs. Second, we propose a novel lightweight monitoring method for detecting communication disruptions. Simulation and synthesis results show the feasibility and efficiency of the presented approach.

Malware evolves quickly as new attack, evasion and mutation techniques are commonly used by hackers to build new malicious malware families. For malware detection and classification, multi-class learning model is one of the most popular machine learning models being used. To recognize malicious programs, multi-class model requires malware types to be predefined as output classes in advance which cannot be dynamically adjusted after the model is trained. When a new variant or type of malicious programs is discovered, the trained multi-class model will be no longer valid and have to be retrained completely. This consumes a significant amount of time and resources, and cannot adapt quickly to meet the timely requirement in dealing with dynamically evolving malware types. To cope with the problem, an evolvable malware classification deep learning model, namely EC-Model, is proposed in this paper which can dynamically adapt to new malware types without the need of fully retraining. Consequently, the reaction time can be significantly reduced to meet the timely requirement of malware classification. To our best knowledge, our work is the first attempt to adopt multi-task, deep learning for evolvable malware classification.

In recent years, cyber security has become a challenging task to protect the networks and computing systems from various types of digital attacks. Therefore, to preserve these systems, various innovative methods have been reported and implemented in practice. However, still more research work needs to be carried out to have malware free computing system. In this paper, an attempt has been made to develop simple but reliable ML based malware detection systems which can be implemented in practice. Keeping this in view, the present paper has proposed and compared the performance of three ML based malware detection systems applicable for computer systems. The proposed methods include k-NN, RF and LR for detection purpose and the features extracted comprise of Byte and ASM. The performance obtained from the simulation study of the proposed schemes has been evaluated in terms of ROC, Log loss plot, accuracy, precision, recall, specificity, sensitivity and F1-score. The analysis of the various results clearly demonstrates that the RF based malware detection scheme outperforms the model based on k-NN and LR The efficiency of detection of proposed ML models is either same or comparable to deep learning-based methods.

The task of malware labeling on real datasets faces huge challenges—ever-changing datasets and lack of ground-truth labels—owing to the rapid growth of malware. Clustering malware on their respective families is a well known tool used for improving the efficiency of the malware labeling process. In this paper, we addressed the challenge of clustering email malware, and carried out a cluster analysis on a real dataset collected from email campaigns over a 13-month period. Our main original contribution is to analyze the usefulness of email’s header information for malware clustering (a novel approach proposed by Burton [1]), and compare it with features collected from the malware directly. We compare clustering based on email header’s information with traditional features extracted from varied resources provided by VirusTotal [2], including static and dynamic analysis. We show that email header information has an excellent performance.

Robotic systems are becoming an ever-increasing part of everyday life due to their capacity to carry out physical tasks on behalf of human beings. Found in nearly every facet of our lives, robotic systems are used domestically, in small and large-scale factories, for the production and processing of agriculture, for military operations, to name a few. The Robotic Operating System (ROS) is the standard operating system used today for the development of modular robotic systems. However, in its development, ROS has been notorious for the absence of security mechanisms, placing people in danger both physically and digitally. This dissertation summary presents the development of a suite of ROS tools, leading up to the development of a modular, secure framework for ROS. An integrated approach for the security of ROS-enabled robotic systems is described, to set a baseline for the continual development to increase ROS security. The work culminates in the ROS security tool ROS-Immunity, combining internal system defense, external system verification, and automated vulnerability detection in an integrated tool that, in conjunction with Secure-ROS, provides a suite of defenses for ROS systems against malicious attackers.

Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment-one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how agile developers may act when behaving strategically. We support our argument by presenting a general model for self-assignment of development tasks based on and extending concepts drawn from established game theory research. We further introduce the software engineering community to two metrics drawn from game theory-the price-of-stability and price-of-anarchy-which can be used to gauge the efficiencies of self-organizing teams compared to centralized management. We demonstrate how these metrics can be used in a case study evaluating the hypothesis that smaller teams self-organize more efficiently than larger teams, with conditional support for that hypothesis. Our game-theoretic framework provides new perspective for the software engineering community, opening many avenues for future research.

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Prior research in human-robot teamwork proposes fairness definitions for human-robot teaming that are based on the work completed by each team member. However, metrics that properly capture people’s perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people’s perception of fairness. First, we extend prior fairness metrics based on team members’ capabilities and workload to a bigger team. We also develop a new metric to quantify the amount of time that the robot spends working on the same task as each person. We conduct an online user study (n=95) and show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people’s assessment of fairness. When asked to rate fairness based on the amount of time that the robot spends working with each person, participants used two factors (fairness based on the robot’s time and teammates’ capabilities). This bleed-over effect is stronger when people are asked to assess fairness based on capability. From these insights, we propose design guidelines for algorithms to enable robotic teammates to consider fairness in its decision-making to maintain positive team social dynamics and team task performance.

The fourth industrial revolution envisions industry manufacturing systems to be software driven where mundane manufacturing tasks can be automated. As software is perceived as an integral part of this vision, discovering vulnerabilities is of paramount of importance so that manufacturing systems are secure. A categorization of vulnerability discovery strategies can inform practitioners on how to identify undiscovered vulnerabilities in software. Recently researchers have investigated and identified vulnerability discovery strategies used in open source software (OSS) projects. The efficacy of the derived strategy needs to be validated by obtaining feedback from practitioners. Such feedback can be helpful to assess if identified strategies are useful for practitioners and possible directions the derived vulnerability discovery strategies can be improvised. We survey 51 practitioners to assess if four vulnerability discovery strategies: diagnostics, malicious payload construction, misconfiguration, and pernicious execution can be used to identify undiscovered vulnerabilities. Practitioners perceive the strategies to be useful: for example, we observe 88% of the surveyed practitioners to agree that diagnostics could be used to discover vulnerabilities. Our work provides evidence of usefulness for the identified strategies.

Today technological changes make the probability of more complex things made into simple tasks with more accuracy in major areas and mostly in Manufacturing Industry. Internet of things contributes its major part in automation which helps human to make life easy by monitoring and directed to a related person with in a fraction of second. Continuous advances and improvement in computer vision, mobile computing and tablet screens have led to a revived interest in Augmented Reality the Augmented Reality makes the complex automation into an easier task by making more realistic real time animation in monitoring and automation on Internet of Things (eg like temperature, time, object information, installation manual, real time testing).In order to identify and link the augmented content, like object control of home appliances, industrial appliances. The AR-IoT will have a much cozier atmosphere and enhance the overall Interactivity of the IoT environment. Augmented Reality applications use a myriad of data generated by IoT devices and components, AR helps workers become more competitive and productive with the realistic environment in IoT. Augmented Reality and Internet of Things together plays a critical role in the development of next generation technologies. This paper describes the concept of how Augmented Reality can be integrated with industry(AR-IoT)4.0 and how the sensors are used to monitoring objects/things contiguously round the clock, and make the process of converting real-time physical objects into smart things for the upcoming new era with AR-IoT.

Performing an interaction using gaze and pinch has been certified as an efficient interactive method in Mixed Reality, for such techniques can provide users concise and natural experiences. However, executing a task with individual interactions gradually is inefficient in some application scenarios. In this paper, we propose the Hand-Pinch Menu, which core concept is to reduce unnecessary operations by combining several interactions. Users can continuously perform multiple interactions on a selected object concurrently without changing gestures by using this technique. The user study results show that our Gaze-Pinch Menu can improve operational efficiency effectively.

Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.

Cloud computing enables users and organizations to conveniently store and share data in large volumes and to enjoy on-demand services. Security and the protection of big data sharing from various attacks is the most challenging issue. Proxy re-encryption (PRE) is an effective method to improve the security of data sharing in the cloud environment. However, in PRE schemes, offloading big data for re-encryption will impose a heavy computational burden on the cloud proxy server, resulting in an increased computation delay and response time for the users. In this paper, we propose a novel parallel PRE workload distribution scheme to dynamically route the big data re-encryption process into the fog of the network. Moreover, this paper proposes a dynamic load balancing technique to avoid an excessive workload for the fog nodes. It also uses lightweight asymmetric cryptography to provide end-to-end security for the big data sharing between users. Within the proposed scheme, the offloading overhead on the centralized cloud server is effectively mitigated. Meanwhile, the processing delay incurred by the big data re-encryption process is efficiently improved.

This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.