Biblio

This document addresses the issue of the actual security level of PDF documents. Two types of detection approaches are utilized to detect dangerous elements within malware: static analysis and dynamic analysis. Analyzing malware binaries to identify dangerous strings, as well as reverse-engineering is included in static analysis for t1he malware to disassemble it. On the other hand, dynamic analysis monitors malware activities by running them in a safe environment, such as a virtual machine. Each method has its own set of strengths and weaknesses, and it is usually best to employ both methods while analyzing malware. Malware detection could be simplified without sacrificing accuracy by reducing the number of malicious traits. This may allow the researcher to devote more time to analysis. Our worry is that there is no obvious need to identify malware with numerous functionalities when it isn't necessary. We will solve this problem by developing a system that will identify if the given file is infected with malware or not.

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Based on the characteristics of the new power system with many points, wide range and unattended, this paper studies the specific Cyberspace security risks faced by the disease control side, the station side and the site side, and proposes a new power system Cyberspace security assurance system of “integration of collection, network, side, end, industry and people”. The site side security access measures, the site side civil air defense technology integration measures, the whole business endogenous security mechanism, the whole domain communication security mechanism, the integrated monitoring and early warning and emergency response mechanism are specifically adopted to form a comprehensive integrated security mechanism for the new power system, form a sustainable protection model, effectively improve the security capability, while taking into account the cost and operational complexity of specific implementation links, Provide comprehensive guarantee capability for the safe operation of the new power system.

Hash-based data structures and algorithms are currently flourishing on the Internet. It is an effective way to store large amounts of information, especially for applications related to measurement, monitoring and security. At present, there are many hash table algorithms such as: Cuckoo Hash, Peacock Hash, Double Hash, Link Hash and D-left Hash algorithm. However, there are still some problems in these hash table algorithms, such as excessive memory space, long insertion and query operations, and insertion failures caused by infinite loops that require rehashing. This paper improves the kick-out mechanism of the Cuckoo Hash algorithm, and proposes a new hash table structure- Odd-Even Hash (OE Hash) algorithm. The experimental results show that OE Hash algorithm is more efficient than the existing Link Hash algorithm, Linear Hash algorithm, Cuckoo Hash algorithm, etc. OE Hash algorithm takes into account the performance of both query time and insertion time while occupying the least space, and there is no insertion failure that leads to rehashing, which is suitable for massive data storage.

With the development of embedded systems towards networking and intelligence, the security threats they face are becoming more difficult to prevent. Existing protection methods make it difficult to monitor jump instructions and their target addresses for tampering by attackers at the low hardware implementation overhead and performance overhead. In this paper, a hardware-assisted security monitoring module is designed to monitor the integrity of jump instructions and jump addresses when executing programs. The proposed method has been implemented on the Xilinx Kintex-7 FPGA platform. Experiments show that this method is able to effectively monitor tampering attacks on jump instructions as well as target addresses while the embedded system is executing programs.

The growing number of cybersecurity incidents and the always increasing complexity of cybersecurity attacks is forcing the industry and the research community to develop robust and effective methods to detect and respond to network attacks. Many tools are either built upon a large number of rules and signatures which only large third-party vendors can afford to create and maintain, or are based on complex artificial intelligence engines which, in most cases, still require personalization and fine-tuning using costly service contracts offered by the vendors.This paper introduces an open-source network traffic monitoring system based on the concept of cyberscore, a numerical value that represents how a network activity is considered relevant for spotting cybersecurity-related events. We describe how this technique has been applied in real-life networks and present the result of this evaluation.

In this work, we conduct a systematic study on data poisoning attacks to Matrix Factorisation (MF) based Recommender Systems (RS) where a determined attacker injects fake users with false user-item feedback, with an objective to promote a target item by increasing its rating. We explore the capability of a MF based approach to reduce the impact of attack on targeted item in the system. We develop and evaluate multiple techniques to update the user and item feature matrices when incorporating new ratings. We also study the effectiveness of attack under increasing filler items and choice of target item.Our experimental results based on two real-world datasets show that the observations from the study could be used to design a more robust MF based RS.

Networked cyber-physical systems must balance the utility of communication for monitoring and control with the risks of revealing private information. Many of these networks, such as wireless communication, are vulnerable to eavesdrop-ping by illegitimate recipients. Obfuscation can hide information from eaves-droppers by ensuring their observations are ambiguous or misleading. At the same time, coordination with recipients can enable them to interpret obfuscated data. In this way, we propose an obfuscation framework for dynamic systems that ensures privacy against eavesdroppers while maintaining utility for legitimate recipients. We consider eavesdroppers unaware of obfuscation by requiring that their observations are consistent with the original system, as well as eaves-droppers aware of the goals of obfuscation by assuming they learn of the specific obfuscation implementation used. We present a method for bounded synthesis of solutions based upon distributed reactive synthesis and the synthesis of publicly-known obfuscators.

As cyber-physical systems are becoming more wide spread, it is imperative to secure these systems. In the real world these systems produce large amounts of data. However, it is generally impractical to test security techniques on operational cyber-physical systems. Thus, there exists a need to have realistic systems and data for testing security of cyber-physical systems [1]. This is often done in testbeds and cyber ranges. Most cyber ranges and testbeds focus on traditional network systems and few incorporate cyber-physical components. When they do, the cyber-physical components are often simulated. In the systems that incorporate cyber-physical components, generally only the network data is analyzed for attack detection and diagnosis. While there is some study in using physical signals to detect and diagnosis attacks, this data is not incorporated into current testbeds and cyber ranges. This study surveys currents testbeds and cyber ranges and demonstrates a prototype testbed that includes cyber-physical components and sensor data in addition to traditional cyber data monitoring.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.

In the operation of information technology (IT) services, operators monitor the equipment-issued alarms, to locate the cause of a failure and take action. Alarms generate simultaneously from multiple devices with physical/logical connections. Therefore, if the time and location of the alarms are close to each other, it can be judged that the alarms are likely to be caused by the same event. In this paper, we propose a method that takes a novel approach by correlating alarms considering event units using a Bayesian network based on alarm generation time, generation place, and alarm type. The topology information becomes a critical decision element when doing the alarm correlation. However, errors may occur when topology information updates manually during failures or construction. Therefore, we show that event-by-event correlation with 100% accuracy is possible even if the topology information is 25% wrong by taking into location information other than topology information.

Satisfying the growing demand for electricity is a huge challenge for electricity providers without a robust and good infrastructure. For effective electricity management, the infrastructure has to be strengthened from the generation stage to the transmission and distribution stages. In the current electrical infrastructure, the evolution of smart grids provides a significant solution to the problems that exist in the conventional system. Enhanced management visibility and better monitoring and control are achieved by the integration of wireless sensor network technology in communication systems. However, to implement these solutions in the existing grids, the infrastructural constraints impose a major challenge. Along with the choice of technology, it is also crucial to avoid exorbitant implementation costs. This paper presents a self-stabilizing hierarchical algorithm for the existing electrical network. Neighborhood Area Networks (NAN) and Home Area Networks (HAN) layers are used in the proposed architecture. The Home Node (HN), Simple Node (SN) and Cluster Head (CH) are the three types of nodes used in the model. Fraudulent users in the system are identified efficiently using the proposed model based on the observations made through simulation on OMNeT++ simulator.

Anomalous behaviour in subsystems of complex machines often affect overall performance even without failures. We devise unsupervised methods to detect times with degraded performance, and localize correlated signals, evaluated on a system with over 4000 monitored signals. From incidents comprising both downtimes and degraded performance, our approach localizes relevant signals within 1.2% of the parameter space.

Internet of Things (IoT) networks consist of small devices that use a wireless communication to monitor and possibly control the physical world. A common threat to such networks are jamming attacks, a particular type of denial of service attack. Current research highlights the need for the design of more effective and efficient anti-jamming techniques that can handle different types of attacks in IoT networks. In this paper, we propose DeMiJA, short for Detection and Mitigation of Jamming Attacks in IoT, a novel approach to deal with different jamming attacks in IoT networks. DeMiJA leverages architecture-based adaptation and the MAPE-K reference model (Monitor-Analyze-Plan-Execute that share Knowledge). We present the general architecture of DeMiJA and instantiate the architecture to deal with jamming attacks in the DeltaIoT exemplar. The evaluation shows that DeMiJA can handle different types of jamming attacks effectively and efficiently, with neglectable overhead.

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.

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.

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.

Industrial Control System (ICS) transmits control and monitoring data between devices in an industrial environment that includes smart grids, water and gas distribution, or traffic control. Unlike traditional internet communication, ICS traffic is stable, periodical, and with regular communication patterns that can be described using statistical modeling. By observing selected features of ICS transmission, e.g., packet direction and inter-arrival times, we can create a statistical profile of the communication based on distribution of features learned from the normal ICS traffic. This paper demonstrates that using statistical modeling, we can detect various anomalies caused by irregular transmissions, device or link failures, and also cyber attacks like packet injection, scanning, or denial of service (DoS). The paper shows how a statistical model is automatically created from a training dataset. We present two types of statistical profiles: the master-oriented profile for one-to-many communication and the peer-to-peer profile that describes traffic between two ICS devices. The proposed approach is fast and easy to implement as a part of an intrusion detection system (IDS) or an anomaly detection (AD) module. The proof-of-concept is demonstrated on two industrial protocols: IEC 60870-5-104 (aka IEC 104) and IEC 61850 (Goose).

Cloud computing is a modern type of service that provides each consumer with a large-scale computing tool. Different cyber-attacks can potentially target cloud computing systems, as most cloud computing systems offer services to so many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If a strong security is required then a stronger security service using more rules or patterns should be incorporated and then in proportion to the strength of security, it needs much more computing resources. So the amount of resources allocated to customers is decreasing so this research work will introduce a new way of security system in cloud environments to the VM in this research. The main point of Fog computing is to part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change gigantic information measurement because the endeavor apps are relocated to the cloud to keep the framework cost. So the cloud server is devouring and changing huge measures of information step by step so it is rented to keep up the problem and additionally get terrible reactions in a horrible device environment. Cloud computing and Fog computing approaches were combined in this paper to review data movement and safe information about MDHC.

Trust along digitalized supply chains is challenged by the aspect that monitoring equipment may not be trustworthy or unreliable as respective measurements originate from potentially untrusted parties. To allow for dynamic relationships along supply chains, we propose a blockchain-backed supply chain monitoring architecture relying on trusted hardware. Our design provides a notion of secure end-to-end sensing of interactions even when originating from untrusted surroundings. Due to attested checkpointing, we can identify misinformation early on and reliably pinpoint the origin. A blockchain enables long-term verifiability for all (now trustworthy) IoT data within our system even if issues are detected only after the fact. Our feasibility study and cost analysis further show that our design is indeed deployable in and applicable to today’s supply chain settings.

Information security of logistics services. Information security of logistics services is understood as a complex activity aimed at using information and means of its processing in order to increase the level of protection and normal functioning of the object's information environment. At the same time the main recommendations for ensuring information security of logistics processes include: logistics support of processes for ensuring the security of information flows of the enterprise; assessment of the quality and reliability of elements, reliability and efficiency of obtaining information about the state of logistics processes. However, it is possible to assess the level of information security within the organization's controlled part of the supply chain through levels and indicators. In this case, there are four levels and elements of information security of supply chains.

Recently, the informatics infrastructure of INRiM Time and Frequency Laboratory has been completely renewed with particular attention to network security and software architecture aspects, with the aims to improve the reliability, robustness and automation of the overall set-up. This upgraded infrastructure has allowed, since January 2020, a fully automated generation and monitoring of the Italian time scale UTC(IT), based on dedicated software developed in-house [1]. We focus in this work on the network and software aspects of our set-up, which enable a robust and reliable automatic time scale generation with continuous monitoring and minimal human intervention.

Information flow type systems enforce the security property of noninterference by detecting unauthorized data flows at compile-time. However, they require precise type annotations, making them difficult to use in practice as much of the legacy infrastructure is written in untyped or dynamically-typed languages. Gradual typing seamlessly integrates static and dynamic typing, providing the best of both approaches, and has been applied to information flow control, where information flow monitors are derived from gradual security types. Prior work on gradual information flow typing uncovered tensions between noninterference and the dynamic gradual guarantee- the property that less precise security type annotations in a program should not cause more runtime errors.This paper re-examines the connection between gradual information flow types and information flow monitors to identify the root cause of the tension between the gradual guarantees and noninterference. We develop runtime semantics for a simple imperative language with gradual information flow types that provides both noninterference and gradual guarantees. We leverage a proof technique developed for FlowML and reduce noninterference proofs to preservation proofs.

The Internet of Things (IoT) paradigm has been proposed in the last few years with the goal of addressing technical problems in fields such as home and industrial automation, smart lighting systems and traffic monitoring. However, due to the very nature of the IoT devices (generally low-powered and often lacking strong security functionalities), typical deployments pose a great risk in terms of security and privacy. In this respect, the utilization of both a Trusted Execution Environment (TEE) and a Trusted Platform Module (TPM) can serve as a countermeasure against typical attacks. Furthermore, these functional blocks can serve as safe key storage services and provide a robust secure boot implementation and a firmware update mechanism, thus ensuring run-time authentication and integrity. The Common Criteria for Information Technology Security Evaluation allows to determine the degree of attainment of precise security properties in a product. The main objective of this work is to identify, propose and compose bricks of protection profile (PP), as defined by Common Criteria, that are applicable to secure IoT architectures. Moreover, it aims at giving some guiding rules and facilitate future certifications of components and/or their composition. Finally, it also provides a structure for a future methodology of assessment for IoT devices.

Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.