Biblio

Document-level relation extraction (RE) aims to extract relations among entities within a document, which is more complex than its sentence-level counterpart, especially in biomedical text mining. Chemical-disease relation (CDR) extraction aims to extract complex semantic relationships between chemicals and diseases entities in documents. In order to identify the relations within and across multiple sentences at the same time, existing methods try to build different document-level heterogeneous graph. However, the entity relation representations captured by these models do not make full use of the document information and disregard the noise introduced in the process of integrating various information. In this paper, we propose a novel model DAM-GAN to document-level biomedical RE, which can extract entity-level and mention-level representations of relation instances with R-GCN and Dual-Attention Multi-Instance Learning (DAM) respectively, and eliminate the noise with Generative Adversarial Network (GAN). Entity-level representations of relation instances model the semantic information of all entity pairs from the perspective of the whole document, while the mention-level representations from the perspective of mention pairs related to these entity pairs in different sentences. Therefore, entity- and mention-level representations can be better integrated to represent relation instances. Experimental results demonstrate that our model achieves superior performance on public document-level biomedical RE dataset BioCreative V Chemical Disease Relation(CDR).

Data exfiltration is of increasing concern throughout the world. The number of incidents and capabilities of data exfiltration attacks are growing at an unprecedented rate. However, such attack vectors have not been deeply explored in the literature. This paper aims to address this gap by implementing a data exfiltration methodology, detailing some data exfiltration methods. Groups of exfiltration methods are incorporated into a program that can act as a testbed for owners of any network that stores sensitive data. The implemented methods are tested against the well-known network intrusion detection system Snort, where all of them have been successfully evaded detection by its community rule sets. Thus, in this paper, we have developed new countermeasures to prevent and detect data exfiltration attempts using these methods.

Stochastic computing is based on probability concepts which are different from conventional mathematical operations. Advantages of stochastic computing in the fields of neural networks and digital image processing have been reported in literature recently. Arithmetic operations especially multiplications can be performed either by logical AND gates in unipolar format or by EXNOR gates in bipolar format in stochastic computation. Stochastic computing is inherently fault-tolerant and requires fewer logic gates to implement arithmetic operations. Long computing time and low accuracy are the main drawbacks of this system. In this presentation, to reduce hardware requirement and delay, modified stochastic multiplication using AND gate array and multiplexer are used for the design of Finite Impulse Response Filter cores. Performance parameters such as area, power and delay for FIR filter using modified stochastic computing methods are compared with conventional floating point computation.

Process injection techniques on Windows appli-cations are considered a serious threat to software security specialists. The attackers use these techniques to exploit the targeted program or process and take advantage of it by injecting a malicious process within the address space of the hosted process. Such attacks could be carried out using the so-called reverse engineering realm” the code caves”. For that reason, detecting these code caves in a particular application/program is deemed crucial to prevent the adversary from exploiting the programs through them. Code caves are simply a sequence of null bytes inside the executable program. They form due to the unuse of uninitialized variables. This paper presents a tool that can detect code caves in Windows programs by disassembling the program and looking for the code caves inside it; additionally, the tool will also eliminate those code caves without affecting the program’s functionality. The tool has proven reliable and accurate when tested on various types of programs under the Windows operating system.

The main goal of the system is to make it easier to standardize the style of program code written in C++. Based on the results of the review of existing static analyzers, in addition to the main requirements, requirements for the structure of stylistic rules were identified. Based on the results obtained, a system for static analysis of the C++ language has been developed, consisting of a set of modules. The system is implemented using the Python 3.7 programming language. HTML and CSS markup languages were used to generate html reports. To ensure that rules can be stored in the database, the MongoDB database management system and the pymongo driver module were used.

The user's access history can be used as an important reference factor in determining whether to allow the current access request or not. And it is often ignored by the existing access control models. To make up for this defect, a Dynamic Trust - game theoretic Access Control model is proposed based on the previous work. This paper proposes a method to quantify the user's trust in the cloud environment, which uses identity trust, behavior trust, and reputation trust as metrics. By modeling the access process as a game and introducing the user's trust value into the pay-off matrix, the mixed strategy Nash equilibrium of cloud user and service provider is calculated respectively. Further, a calculation method for the threshold predefined by the service provider is proposed. Authorization of the access request depends on the comparison of the calculated probability of the user's adopting a malicious access policy with the threshold. Finally, we summarize this paper and make a prospect for future work.

With the recent rise of HTTPS adoption on the Web, attackers have begun "HTTPSifying" phishing websites. HTTPSifying a phishing website has the advantage of making the website appear legitimate and evading conventional detection methods that leverage URLs or web contents in the network. Further, adopting HTTPS could also contribute to generating intrinsic footprints and provide defenders with a great opportunity to monitor and detect websites, including phishing sites, as they would need to obtain a public-key certificate issued for the preparation of the websites. The potential benefits of certificate-based detection include: (1) the comprehensive monitoring of all HTTPSified websites by using certificates immediately after their issuance, even if the attacker utilizes dynamic DNS (DDNS) or hosting services; this could be overlooked with the conventional domain-registration-based approaches; and (2) to detect phishing websites before they are published on the Internet. Accordingly, we address the following research question: How can we make use of the footprints of TLS certificates to defend against phishing attacks? For this, we collected a large set of TLS certificates corresponding to phishing websites from Certificate Transparency (CT) logs and extensively analyzed these TLS certificates. We demonstrated that a template of common names, which are equivalent to the fully qualified domain names, obtained through the clustering analysis of the certificates can be used for the following promising applications: (1) The discovery of previously unknown phishing websites with low false positives and (2) understanding the infrastructure used to generate the phishing websites. We use our findings on the abuse of free certificate authorities (CAs) for operating HTTPSified phishing websites to discuss possible solutions against such abuse and provide a recommendation to the CAs.

“Phishing emails” are phishing emails with illegal links that direct users to pages of some real websites that are spoofed, or pages where real HTML has been inserted with dangerous HTML code, so as to deceive users' private information such as bank or credit card account numbers, email account numbers, and passwords. People are the most vulnerable part of security. Phishing emails use human weaknesses to attack. This article describes the application of the principle of persuasion in phishing emails, and based on the existing methods, this paper proposes a phishing email detection method based on the persuasion principle. The principle of persuasion principle is to count whether the corresponding word of the feature appears in the mail. The feature is selected using an information gain algorithm, and finally 25 features are selected for detection. Finally experimentally verified, accuracy rate reached 99.6%.

Email based Uniform Resource Locator (URL) distribution is one of the popular ways for starting phishing attacks. Conventional anti-phishing solutions rely on security facilities and investigate all incoming emails. This makes the security facilities get overloaded and cause consequences of upgrades or new deployments even with no better options. This paper presents a novel detour strategy for the traffic of visiting potential phishing URLs based on dynamic Domain Name System (DNS) Response Policy Zone (RPZ) in order to mitigate the overloads on security facilities. In the strategy, the URLs included in the incoming emails will be extracted and the corresponding Fully Qualified Domain Name (FQDN) will be registered in the RPZ of the local DNS cache server with mapping the IP address of a special Hypertext Transfer Protocol (HTTP) proxy. The contribution of the approach is to avoid heavy investigations on all incoming emails and mitigate the overloads on security facilities by directing the traffic to phishing URLs to the special HTTP proxy connected with a set of security facilities conducting various inspections. The evaluation results on the prototype system showed that the URL extraction and FQDN registration were finished before the emails had been delivered and accesses to the URLs were successfully directed to the special HTTP proxy. The results of overhead measurements also confirmed that the proposed strategy only affected the internal email server with 11% of performance decrease on the prototype system.

The co-existence between Internet Protocol (IP) and Named-Data Networking (NDN) protocol is inevitable during the transition period. We propose a privacy-preserving translation method between IP and NDN called the dual-channel translation gateway. The gateway provides two different channels dedicated to the interest and the data packet to translate the IP to the NDN protocol and vice versa. Additionally, the name resolution table is provided at the gateway that binds an IP packet securely with a prefix name. Moreover, we compare the dual-channel gateway performance with the encapsulation gateway.

The paper proposes ways to solve the problem of secure remote access to telecommunications’ equipment. The purpose of the study is to develop a set of procedures to ensure secure interaction while working remotely with Cisco equipment using the SSH protocol. This set of measures is a complete list of measures which ensures security of remote connection to a corporate computer network using modern methods of cryptography and network administration technologies. It has been tested on the GNS3 software emulator and Cisco telecommunications equipment and provides a high level of confidentiality and integrity of remote connection to a corporate computer network. In addition, the study detects vulnerabilities in the IOS operating system while running SSH service and suggests methods for their elimination.

Decentralized Identifiers (DIDs) and Self-Sovereign Identity (SSI) are emerging decentralized identity solutions. DIDs allow legal entities like organizations to create and fully control their identifiers while building the necessary infrastructure for SSI, enabling entities like persons, organizations, or machines to fully control and own their digital identities without the involvement of an intermediate central authority. DIDs are identifiers that are used to reference entities unambiguously and, together with DID Documents stored in a verifiable data registry, establish a new, decentralized public-key infrastructure. An SSI-based digital identity may be composed of many different claims certified by an issuer. Examples are the identity holder’s name, age, gender, university degree, driving license, or other attributes. What makes SSI unique compared to other identity management solutions is that the users keep their digital identities in storage of their choice and thus determine their distribution and processing.With this privacy-by-design approach, the emergence of DIDs and SSI can shape the architecture of the future Internet and its applications, which will impact the future of mobile networks. While 5G networks are currently being rolled out, a discussion about the new capabilities of 6G networks, which are still in the distant future, has long since begun. In addition to even faster access, shorter delays, and new applications, features such as human-centricity, data protection, and privacy are being addressed in particular in the discussions. These latter points make DIDs, SSI, and related concepts and architectures promising candidates for 6G adoption.The talk gives a brief introduction to DIDs and SSI and then discusses the benefits and drawbacks the integration of these technologies into 6G may have. Furthermore, the talk identifies different use cases and identifies the system components and functions of cellular networks affected by a 6G integration.

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.

With smart vehicles interconnected with multiple systems and other entities, whether they are people or IoT devices, the importance of a digital identity for them has emerged. We present in this paper how a Self-Sovereign Identities combined with blockchain can provide a solution to this end, in order to decentralize the identity management and provide them with capabilities to identify the other entities they interact with. Such entities can be the owners of the vehicles, other drivers and workshops that act as service providers. Two use cases are examined along with the interactions between the participants, to demonstrate how a decentralized identity management solution can take care of the necessary authentication and authorization processes. Finally, we test the system and provide the measurements to prove its feasibility in real-life deployments.

Modern day grid operation requires multiple interlinked applications and many automated processes at control center for monitoring and operation of grid. Information technology integrated with operational technology plays a critical role in grid operation. Computing resource requirements of these software applications varies widely and includes high processing applications, high Input/Output (I/O) sensitive applications and applications with low resource requirements. Present day grid operation control center uses various applications for load despatch schedule management, various real-time analytics & optimization applications, post despatch analysis and reporting applications etc. These applications are integrated with Operational Technology (OT) like Data acquisition system / Energy management system (SCADA/EMS), Wide Area Measurement System (WAMS) etc. This paper discusses various design considerations and implementation of converged infrastructure through virtualization technology by consolidation of servers and storages using multi-cluster approach to meet high availability requirement of the applications and achieve desired objectives of grid control center of north eastern region in India. The process involves weighing benefits of different architecture solution, grouping of application hosts, making multiple clusters with reliability and security considerations, and designing suitable infrastructure to meet all end objectives. Reliability, enhanced resource utilization, economic factors, storage and physical node selection, integration issues with OT systems and optimization of cost are the prime design considerations. Modalities adopted to minimize downtime of critical systems for grid operation during migration from the existing infrastructure and integration with OT systems of North Eastern Regional Load Despatch Center are also elaborated in this paper.

Information systems and applications provide indispensable services at every stage of life, enabling us to carry out our activities more effectively and efficiently. Today, information technology systems produce many alarm and event records. These produced records often have a relationship with each other, and when this relationship is captured correctly, many interruptions that will harm institutions can be prevented before they occur. For example, an increase in the disk I/O speed of a server or a problem may cause the business software running on that server to slow down and cause different results in this slowness. Here, an institution’s accurate analysis and management of all event records, and rule-based analysis of the resulting records in certain time periods and depending on certain rules will ensure efficient and effective management of millions of alarms. In addition, it will be possible to prevent possible problems by removing the relationships between events. Events that occur in IT systems are a kind of footprint. It is also vital to keep a record of the events in question, and when necessary, these event records can be analyzed to analyze the efficiency of the systems, harmful interferences, system failure tendency, etc. By understanding the undesirable situations such as taking the necessary precautions, possible losses can be prevented. In this study, the model developed for fault prediction in systems by performing event log analysis in information systems is explained and the experimental results obtained are given.

Cyber security is reliant on the actions of both machine and human and remains a domain of importance and continual evolution. While the study of human behavior has grown, less attention has been paid to the adversarial operator. Cyber environments consist of complex and dynamic situations where decisions are made with incomplete information. In such scenarios people form strategies based on simplified models of the world and are often efficient and effective, yet may result in judgement or decision-making bias. In this paper, we examine an initial list of biases affecting adversarial cyber actors. We use subject matter experts to derive examples and demonstrate these biases likely exist, and play a role in how attackers operate.

In order to protect the safety of power grid, improve the early warning precision of false data injection. This paper presents a dynamic detection model for false data injection attacks. Based on the characteristics of APT attacks, a model of attack characteristics for trusted regions is constructed. In order to realize the accurate state estimation, unscented Kalman filtering algorithm is used to estimate the state of nonlinear power system and realize dynamic attack detection. Experimental results show that the precision of this method is higher than 90%, which verifies the effectiveness of this paper in attack detection.

Malware detection is an important area of cyber security. Computer systems rely on malware detection applications to prevent malware attacks from succeeding. Malware detection is not a straightforward task, as new variants of malware are generated at an increasing rate. Machine learning (ML) has been utilised to generate predictive classification models to identify new malware variants which conventional malware detection methods may not detect. Machine learning, has however, been found to be vulnerable to different types of adversarial attacks, in which an attacker is able to negatively affect the classification ability of the ML model. Several defensive measures to prevent adversarial poisoning attacks have been developed, but they often rely on the use of a trusted clean dataset to help identify and remove adversarial examples from the training dataset. The defence in this paper does not require a trusted clean dataset, but instead, identifies intentional false negatives (zero day malware classified as benign) at the testing stage by examining the activation weights of the ML model. The defence was able to identify 94.07% of the successful targeted poisoning attacks.

Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming languages and libraries. However, many existing programming languages designed for compositional verification of differential privacy impose significant burden on the programmer (in the form of complex type annotations). Supplementary library support for privacy analysis built on top of existing general-purpose languages has been more usable, but incapable of pervasive end-to-end enforcement of sensitivity analysis and privacy composition. We introduce DDuo, a dynamic analysis for enforcing differential privacy. DDuo is usable by non-experts: its analysis is automatic and it requires no additional type annotations. DDuo can be implemented as a library for existing programming languages; we present a reference implementation in Python which features moderate runtime overheads on realistic workloads. We include support for several data types, distance metrics and operations which are commonly used in modern machine learning programs. We also provide initial support for tracking the sensitivity of data transformations in popular Python libraries for data analysis. We formalize the novel core of the DDuo system and prove it sound for sensitivity analysis via a logical relation for metric preservation. We also illustrate DDuo's usability and flexibility through various case studies which implement state-of-the-art machine learning algorithms.

In Forward Error Correction (FEC), redundant bits are added for detecting and correcting bit error which increases the bandwidth. To solve this issue we combined FEC method with higher order M-ary modulation to provide a bandwidth efficient system. An input bit stream is mapped to a bi-orthogonal code on different levels based on the code rates (4/16, 3/16, and 2/16) used. The jamming attack on wireless networks are mitigated by Chaotic Frequency Hopping (CFH) spread spectrum technique. In this paper, to achieve better data rate and to transmit the data in a secured manner we combined FEC and CFH technique, represented as Code and Chaotic Frequency Modulation (CCFM). In addition, two rate adaptation algorithms namely Static retransmission rate ARF (SARF) and Fast rate reduction ARF (FARF) are employed in CFH technique to dynamically adapt the code rate based on channel condition to reduce a packet retransmission. Symbol Error Rate (SER) performance of the system is analyzed for different code rate with the conventional OFDM in the presence AWGN and Rayleigh channel and the reliability of CFH method is tested under different jammer.

Fog computing extends cloud resources to the edge of the network, thus enabling network providers to support real-time applications at low latencies. These applications further demand high security against malicious attacks that target distributed fog servers. One effective defense mechanism here against cyber attacks is the use of honeypots. The latter acts as a potential target for attackers by diverting malicious traffic away from the servers that are dedicated to legitimate users. However, one main limitation of honeypots is the lack of real traffic and network activities. Therefore, it is important to implement a solution that simulates the behavior of the real system to lure attackers without the risk of being exposed. Hence this paper proposes a practical approach to generate network traffic by introducing decoy virtual network functions (VNF) embedded on fog servers, which make the network traffic on honeypots resemble a legitimate, vulnerable fog system to attract cyber attackers. The use of virtualization allows for robust scalability and modification of network functions based on incoming attacks, without the need for dedicated hardware. Moreover, deep learning is leveraged here to build fingerprints for each real VNF, which is subsequently used to support its decoy counterpart against active probes. The proposed framework is evaluated based on CPU utilization, memory usage, disk input/output access, and network latency.

Smart city technologies promise reduced congestion by optimizing transportation movements. Increased connectivity, however, may increase the attack surface of a municipality's critical functions. Increased supply chain attacks (up nearly 80 % in 2019) and municipal ransomware attacks (up 60 % in 2019) motivate the need for holistic approaches to risk assessment. Therefore, we present a methodology to quantify the degree to which supply-chain movements may be observed or disrupted via compromised smart-city devices. Our data-processing pipeline uses publicly available datasets to model intermodal commodity flows within and surrounding a municipality. Using a hierarchy tree to adaptively sample spatial networks within geographic regions of interest, we bridge the gap between grid- and network-based risk assessment frameworks. Results based on fieldwork for the Jack Voltaic exercises sponsored by the Army Cyber Institute demonstrate our approach on intermodal movements through Charleston, SC and San Diego, CA.

Blockchain is becoming more popular because of its decentralized, secured, and transparent nature. Supply chain and its management is indispensable to improve customer services, reduce operating costs and improve financial position of a firm. Integration of blockchain and supply chain is substantial, but it alone is not enough for the sustainability of supply chain systems. The proposed mechanism speaks about the method of rewarding the supply chain parties with incentives so as to improve the security and make the integration of supply chain with blockchain sustainable. The proposed incentive mechanism employs the co-operative approach of game theory where all the supply chain parties show a cooperative behavior of following the blockchain-based supply chain protocols and also this mechanism makes a fair attempt in rewarding the supply chain parties with incentives.

Supply chain plays a significant job in an organization making systems between an organization and its supplier to deliver and disperse items and administrations to the last purchasers. Digitization alludes to the way toward moving physical reports into physical documents. Digitization will make incredible open doors for associations and supply chain rehearses. Numerous associations need to turn out to be progressively “advanced” since they have watched the criticality and value of computerized advances for their development and their own organizations. This research study topic presents a review of the supply chain management digitization practices and dreams with a merged image of digitization and stream of data between the Supplier and Manufacturer chain. Value management, in value analysis, assumes a huge job in a viable Digital Supply Chain Management, it is progressively centered around mechanization, digitizing the procedure, and the coordination and reconciliation of the considerable number of components associated with the supply chain. In view of how value-chain management has developed, it assumes an urgent job in managing the ever-expanding unpredictability in supply chains all inclusive. This study presents an overview of the supply chain management digitization practices and visions with a consolidated picture of digitization and flow of information between the Supplier and Manufacturer chain. This study can be further improved by integrating the latest technology and tools AI and IoT-as a future study.