Biblio

Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.

Customer Relationship Management (CRM), Supply Chain Management (SCM), banking, and e-commerce are just a few of the internet-primarily based commercial enterprise programmes that make use of distributed computing generation. These programmes are the principal target of large-scale attacks known as DDoS attacks, which cause the denial of service (DoS) of resources to legitimate customers. Servers that provide dependable services to real consumers in distributed environments are vulnerable to such attacks, which send phoney requests that appear legitimate. Flash crowd, on the other hand, is a massive collection of traffic generated by flash events that imitate Distributed Denial of Service assaults. Detecting and distinguishing between Distributed Denial of Service assaults and flash crowds is a difficult problem to tackle, as is preventing DDoS attacks. Existing solutions are generally intended for DDoS attacks or flash crowds, and more research is required to have a thorough understanding. This study presents a technique for distinguishing between different types of Distributed Denial of Service attacks and Flash Crowds. This research work has suggested an approach to prevent DDOS attacks in addition to detecting and discriminating. The performance of the suggested technique is validated using NS-2 simulations.

End users consider the data available through web as unmodified. Even when the web is secured by HTTPS, the data can be tampered in numerous tactical ways reducing trust on the integrity of data at the clients' end. One of the ways in which the web pages can be modified is via client side browser extensions. The extensions can transparently modify the web pages at client's end and can include new data to the web pages with minimal permissions. Clever modifications can be addition of a fake news or a fake advertisement or a link to a phishing website. We have identified through experimentation that such attacks are possible and have potential for serious damages. To prevent and detect such modifications we present a novel domain expressiveness based approach that uses DNS (Domain Name System) TXT records to express the Hash of important web pages that gets verified by the browsers to detect/thwart any modifications to the contents that are launched via client side malicious browser extensions or via cross site scripting. Initial experimentation suggest that the technique has potential to be used and deployed.

Consider the identification (ID) via channels problem, where a receiver wants to decide whether the transmitted identifier is its identifier, rather than decoding the identifier. This model allows to transmit identifiers whose size scales doubly-exponentially in the blocklength, unlike common transmission (or channel) codes whose size scales exponentially. It suffices to use binary constant-weight codes (CWCs) to achieve the ID capacity. By relating the parameters of a binary CWC to the minimum distance of a code and using higher-order correlation moments, two upper bounds on the binary CWC size are proposed. These bounds are shown to be upper bounds also on the identifier sizes for ID codes constructed by using binary CWCs. We propose two code constructions based on optical orthogonal codes, which are used in optical multiple access schemes, have constant-weight codewords, and satisfy cyclic cross-correlation and autocorrelation constraints. These constructions are modified and concatenated with outer Reed-Solomon codes to propose new binary CWCs optimal for ID. Improvements to the finite-parameter performance of both our and existing code constructions are shown by using outer codes with larger minimum distance vs. blocklength ratios. We also illustrate ID performance regimes for which our ID code constructions perform significantly better than existing constructions.

We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.

Federated Identity Management (FIM) is a model of identity management in which different trusted organizations can provide secure online services to their uses. Security Assertion Markup Language (SAML) is one of the widely-used technologies for FIM. However, a SAML-based FIM has two significant issues: the metadata (a crucial component in SAML) has security issues, and federation management is hard to scale. The concept of dynamic identity federation has been introduced, enabling previously unknown entities to join in a new federation facilitating inter-organization service provisioning to address federation management's scalability issue. However, the existing dynamic federation approaches have security issues concerning confidentiality, integrity, authenticity, and transparency. In this paper, we present the idea of facilitating dynamic identity federations utilizing blockchain technology to improve the existing approaches' security issues. We demonstrate its architecture based on a rigorous threat model and requirement analysis. We also discuss its implementation details, current protocol flows and analyze its performance to underline its applicability.

The router's buffer size imposes significant impli-cations on the performance of the network. Network operators nowadays configure the router's buffer size manually and stati-cally. They typically configure large buffers that fill up and never go empty, increasing the Round-trip Time (RTT) of packets significantly and decreasing the application performance. Few works in the literature dynamically adjust the buffer size, but are implemented only in simulators, and therefore cannot be tested and deployed in production networks with real traffic. Previous work suggested setting the buffer size to the Bandwidth-delay Product (BDP) divided by the square root of the number of long flows. Such formula is adequate when the RTT and the number of long flows are known in advance. This paper proposes a system that leverages programmable switches as passive instruments to measure the RTT and count the number of flows traversing a legacy router. Based on the measurements, the programmable switch dynamically adjusts the buffer size of the legacy router in order to mitigate the unnecessary large queuing delays. Results show that when the buffer is adjusted dynamically, the RTT, the loss rate, and the fairness among long flows are enhanced. Additionally, the Flow Completion Time (FCT) of short flows sharing the queue is greatly improved. The system can be adopted in campus, enterprise, and service provider networks, without the need to replace legacy routers.

Cyber ranges are valuable assets but have limitations in simulating complex realities and multi-sector dependencies; to address this, federated cyber ranges are emerging. This work presents the ECHO Federated Cyber Range, a marketplace for cyber range services, that establishes a mechanism by which independent cyber range capabilities can be interconnected and accessed via a convenient portal. This allows for more complex and complete emulations, spanning potentially multiple sectors and complex exercises. Moreover, it supports a semi-automated approach for processing and deploying service requests to assist customers and providers interfacing with the marketplace. Its features and architecture are described in detail, along with the design, validation and deployment of a training scenario.

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.

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN. The proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme. The evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks. These results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors.

Emerging non-volatile memory technology enables non-volatile main memory (NVMM) that can provide larger capacity and better energy-saving opportunities than DRAMs. However, its non-volatility raises security concerns, where the data in NVMMs can be taken if the memory is stolen. Memory encryption protects the data by limiting it always stays encrypted outside the processor boundary. However, the decryption latency before the data being used by the processor brings new performance burdens. Unlike DRAM-based main memory, such performance overhead worsens on the NVMM due to the slow latency. In this paper, we will introduce optimizations that can be used to re-design the encryption scheme. In our tests, our two new designs, 3-level split counter mode encryption and 8-block split counter mode encryption, improved performance by 26% and 30% at maximum and by 8% and 9% on average from the original encryption scheme, split counter encryption.

Experimental implementation of phase-locked loop (PLL) with bandpass filter is proposed. Such PLL is noteworthy for neuron-like dynamics. It generates both regular and chaotic spikes and bursts. Previously proposed hardware implementation of this system has significant disadvantage – absence of excitable (non-oscillating) mode that is vital for brain neurons. The proposed electronic neuron-like generator is modified and could be used for hardware implementation of spiking neural networks.

To improve the safety precaution level of network system, a combined network intrusion detection method is proposed based on Supervised Locality Preserving Projections (SLPP) feature extraction and Extreme Learning Machine (ELM). In this method, the feature extraction capability of SLPP is first used to reduce the dimensionality of the original network connection and system audit data, and get a feature set, then, based on this, the advantages of ELM in pattern recognition is adopted to build a network intrusion detection model for detecting and determining intrusion behavior. Simulation results show that, under the same experiment conditions, compared with traditional neural networks and support vector machines, the proposed method has more advantages in training efficiency and generalization performance.

The number of people suffering from mental health issues like depression and anxiety have spiked enormously in recent times. Conversational agents like chatbots have emerged as an effective way for users to express their feelings and anxious thoughts and in turn obtain some empathetic reply that would relieve their anxiety. In our work, we construct two types of empathetic conversational agent models based on sequence-to-sequence modeling with and without attention mechanism. We implement the attention mechanism proposed by Bahdanau et al. for neural machine translation models. We train our model on the benchmark Facebook Empathetic Dialogue dataset and the BLEU scores are computed. Our empathetic conversational agent model incorporating attention mechanism generates better quality empathetic responses and is better in capturing human feelings and emotions in the conversation.

Software Defined Networking (SDN) is an innovative technology, which can be applied in a plethora of applications and areas. Recently, SDN has been identified as one of the most promising solutions for industrial applications as well. The key features of SDN include the decoupling of the control plane from the data plane and the programmability of the network through application development. Researchers are looking at these features in order to enhance the Quality of Service (QoS) provisioning of modern network applications. To this end, the following work presents the development of an SDN application, capable of mitigating attacks and maximizing the network’s QoS, by implementing mixed integer linear programming but also using genetic algorithms. Furthermore, a low-cost, physical SDN testbed was developed in order to evaluate the aforementioned application in a more realistic environment other than only using simulation tools.

Smart Contracts based cryptocurrencies such as Ethereum are becoming increasingly popular in various domains: but with this increase in popularity comes a significant decrease in throughput and efficiency. Smart Contracts are executed by every miner in the system serially without any parallelism, both inter and intra-Smart Contracts. Such a serial execution inhibits the scalability required to obtain extremely high throughput pertaining to computationally intensive tasks deployed with such Smart Contracts. While significant advancements have been made in the field of concurrency, from GPU architectures that enable massively parallel computation to tools such as MapRe-duce that distributed computing to several nodes connected in the system to achieve higher performance in distributed systems, none are incorporated in blockchain-based distributed computing. The team proposes a novel blockchain that allows public nodes in a permission-independent blockchain to deploy and run Smart Contracts that provide concurrency-related functionalities within the Smart Contract framework. In this paper, the researchers present “ConCurrency,” a blockchain network capable of handling big data-based computations. The technique is based on currently used distributed system paradigms, such as MapReduce, while also allowing for fundamental parallelly computable problems. Concurrency is achieved using a sharding protocol incorporated with consensus mechanisms to ensure high scalability, high reliability, and better efficiency. A detailed methodology and a comprehensive analysis of the proposed blockchain further indicate a significant increase in throughput for parallelly computable tasks, as detailed in this paper.

In Indonesia, there have been many certificates forgery happened. The lack of security system for the certificate and the difficulty in verification process toward the authenticity certificate become the main factor of the certificate forgery cases happen. The aim of this research is to improve the security system such digital signature and QR code to authenticate the authenticity certificate and to facilitate the user in verify their certificate and also to minimize the certificate forgery cases. The aim of this research is to improve the security system such digital signature and QR code to authenticate the authenticity certificate and to facilitate the user in verify their certificate and also to minimize the certificate forgery cases. The application is built in web system to facilitate the user to access it everywhere and any time. This research uses Research and Development method for problem analysis and to develop application using Software Development Life Cycle method with waterfall approach. Black box testing is chosen as testing method for each function in this system. The result of this research is creatcate application that’s designed to support the publishing and the verification of the electronic authenticity certificate by online. There are two main schemes in system: the scheme in making e-certificate and the scheme of verification QR Code. There is the electronic professional certificate application by applying digital signature and QR Code. It can publish e-certificate that can prevent from criminal action such certificate forgery, that’s showed in implementation and can be proven in test.

It is important to ensure source location privacy (SLP) protection in safety-critical monitoring applications. Also, to achieve effective long-term monitoring, it is essential to design SLP protocols with high energy efficiency and energy balancing. Therefore, this study proposes a new phantom with angle (PwA) protocol. The PwA protocol employs dynamic routing paths which are designed to achieve SLP protection with energy efficiency and energy balancing. Analysis results reveal that the PwA protocol exhibits superior performance features to outperform existing protocols by achieving high levels of SLP protection for time petime periods. The results confirm that the PwA protocol is practical in long-term monitoring systems.riods. The results confirm that the PwA protocol is practical in long-term monitoring systems.

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.

Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.

Nowadays, face recognition is used everywhere in all fields. Though the face recognition is used for security purposes there is also chance in hacking the faces which is used for face recognition. For enhancing the face security, encryption and decryption technique is used. Face cognizance has been engaged in more than a few security-connected purposes such as supervision, e-passport, and etc… The significant use of biometric raises vital private concerns, in precise if the biometric same method is carried out at a central or unfrosted servers, and calls for implementation of Privacy improving technologies. For privacy concerns the encoding and decoding is used. For achieving the result we are using the Open Computer Vision (OpenCV) tool. With the help of this tool we are going to cipher the face and decode the face with advanced encryption standards techniques. OpenCV is the tool used in this project

Malicious software (malware) creators are constantly mutating malware files in order to avoid detection, resulting in hundreds of millions of new malware every year. Therefore, most malware files are unlabeled due to the time and cost needed to label them manually. This makes it very challenging to perform malware detection, i.e., deciding whether a file is malware or not, and malware classification, i.e., determining the family of the malware. Most solutions use supervised learning (e.g., ResNet and VGG) whose accuracy degrades significantly with the lack of abundance of labeled data. To solve this problem, this paper proposes a semi-supervised learning model for image-based malware classification. In this model, malware files are represented as grayscale images, and semi-supervised learning is carefully selected to handle the plethora of unlabeled data. Our proposed model is an enhanced version of the ∏-model, which makes it more accurate and consistent. Experiments show that our proposed model outperforms the original ∏-model by 4% in accuracy and three other supervised models by 6% in accuracy especially when the ratio of labeled samples is as low as 20%.

The main aim of this report is to find how data security can be improved in a cloud environment using the remote data auditing technique. The research analysis of the existing journal articles that are peer-reviewed Q1 level of articles is selected to perform the analysis.The main taxonomy that is proposed in this project is being data, auditing, monitoring, and output i.e., DAMO taxonomy that is used and includes these components. The data component would include the type of data; the auditing would ensure the algorithm that would be used at the backend and the storage would include the type of database as single or the distributed server in which the data would be stored.As a result of this research, it would help understand how the data can be ensured to have the required level of privacy and security when the third-party database vendors would be used by the organizations to maintain their data. Since most of the organizations are looking to reduce their burden of the local level of data storage and to reduce the maintenance by the outsourcing of the cloud there are still many issues that occur when there comes the time to check if the data is accurate or not and to see if the data is stored with resilience. In such a case, there is a need to use the Remote Data Auditing techniques that are quite helpful to ensure that the data which is outsourced is reliable and maintained with integrity when the information is stored in the single or the distributed servers.

5G improves previous generations not only in terms of radio access but the whole infrastructure and services paradigm. Automation, dynamism and orchestration are now key features that allow modifying network behaviour, such as Virtual Network Functions (VNFs), and resource allocation reactively and on demand. However, such dynamic ecosystem must pay special attention to security while ensuring that the system actions are trustworthy and reliable. To this aim, this paper introduces the integration of the Manufacturer Usage Description (MUD) standard alongside a Trust and Reputation Manager (TRM) into the INSPIRE-5GPlus framework, enforcing security properties defined by MUD files while the whole infrastructure, virtual and physical, as well as security metrics are continuously audited to compute trust and reputation values. These values are later fed to enhance trustworthiness on the zero-touch decision making such as the ones orchestrating end-to-end security in a closed-loop.

Over past years, the high demand to efficiently process deep learning (DL) models has driven the market of the chip design companies. However, the new Deep Chip architectures, a common term to refer to DL hardware accelerator, have slightly paid attention to the security requirements in quantized neural networks (QNNs), while the black/white -box adversarial attacks can jeopardize the integrity of the inference accelerator. Therefore in this paper, a comprehensive study of the resiliency of QNN topologies to black-box attacks is examined. Herein, different attack scenarios are performed on an FPGA-processor co-design, and the collected results are extensively analyzed to give an estimation of the impact’s degree of different types of attacks on the QNN topology. To be specific, we evaluated the sensitivity of the QNN accelerator to a range number of bit-flip attacks (BFAs) that might occur in the operational lifetime of the device. The BFAs are injected at uniformly distributed times either across the entire QNN or per individual layer during the image classification. The acquired results are utilized to build the entropy-based model that can be leveraged to construct resilient QNN architectures to bit-flip attacks.