Biblio

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.

In order to improve the security and reliability of information system and reduce the risk of vulnerability intrusion and attack, an automatic patch installation method of operating systems based on deep learning is proposed, If the installation is successful, the basic information of the system will be returned to the visualization server. If the installation fails, it is recommended to upgrading manually and display it on the patch detection visualization server. Through the practical application of statistical analysis, the statistical results show that the proposed method is significantly better than the original and traditional installation methods, which can effectively avoid the problem of client repeated download, and greatly improve the success rate of patch automatic upgrades. It effectively saves the upgrade cost and ensures the security and reliability of the information system.

The power communication network generates a large amount of data. The existing security monitoring method needs to use a large transmission bandwidth in the process of data processing, which leads to the decrease of real-time response. Therefore, an automatic monitoring method of power communication network security based on edge computing is proposed. The paper establishes the power communication monitoring network architecture by combining RFID identification sensor network and wireless communication network. The edge calculation is embedded to the edge side of the power communication network, and the data processing model of power communication is established. Based on linear discriminant analysis, the paper designs a network security situation awareness assessment model, and uses this model to evaluate the real-time data collected by the power communication network. According to the evaluation results, the probability of success of intrusion attack is calculated and the security risk monitoring is carried out for the intrusion attack. The experimental results show that compared with the existing monitoring methods, the edge based security monitoring method can effectively reduce communication delay, improve the real-time response, and then improve the intelligent level of power communication network.

Elliptic curve is a major area of research due to its application in elliptic curve cryptography. Due to their small key sizes, they offer the twofold advantage of reduced storage and transmission requirements. This also results in faster execution times. The authors propose an architecture to automatically generate test cases, for verification of elliptic curve operational circuits, based on user-defined prime field and the parameters used in the circuit to be tested. The ECC test case generations are based on the Galois field arithmetic operations which were the subject of previous work by the authors. One of the strengths of elliptic curve mathematics is its simplicity, which involves just three points (P, Q, and R), which pass through a line on the curve. The test cases generate points for a user-defined prime field which sequentially selects the input vector points (P and/or Q), to calculate the resultant output vector (R) easily. The testbench proposed here targets field programmable gate array (FPGAs) platforms and experimental results for ECC test case generation on different prime fields are presented, while ModelSim is used to validate the correctness of the ECC operations.

Automatic Static Analysis Tools (ASATs) detect coding rule violations, including mistakes and bad practices that frequently occur during programming. While ASATs are widely used in both OSS and industry, the developers do not resolve more than 80% of the detected violations. As one of the reasons, most ASATs users do not customize their ASATs to their projects after installation; the ASATs with the default configuration report many rule violations that confuse developers. To reduce the ratio of such uninteresting warning messages, we propose a method to customize ASATs according to the product source code automatically. Our fundamental hypothesis is: A software project has interesting ASAT rules that are consistent over time. Our method takes source code as input and generates an ASAT configuration. In particular, the method enables optional (i.e., disabled by default) rules that detected no violations on the version because developers are likely to follow the rules in future development. Our method also disables violated rules because developers were unlikely to follow them. To evaluate the method, we applied our method to 643 versions of four JavaScript projects. The generated configurations for all four projects increased the ASAT precision. They also increased recall for two projects. The result shows that our method helps developers to focus on their attractive rule violations. Our implementation of the proposed method is available at https://github.com/devreplay/linter-maintainer

The risk posed by high-profile data breaches has raised the stakes for adhering to data access policies for many organizations, but the complexity of both the policies themselves and the applications that must obey them raises significant challenges. To mitigate this risk, fine-grained audit of access to private data has become common practice, but this is a costly, time-consuming, and error-prone process.We propose an approach for automating much of the work required for fine-grained audit of private data access. Starting from the assumption that the auditor does not have an explicit, formal description of the correct policy, but is able to decide whether a given policy fragment is partially correct, our approach gradually infers a policy from audit log entries. When the auditor determines that a proposed policy fragment is appropriate, it is added to the system's mechanized policy, and future log entries to which the fragment applies can be dealt with automatically. We prove that for a general class of attribute-based data policies, this inference process satisfies a monotonicity property which implies that eventually, the mechanized policy will comprise the full set of access rules, and no further manual audit is necessary. Finally, we evaluate this approach using a case study involving synthetic electronic medical records and the HIPAA rule, and show that the inferred mechanized policy quickly converges to the full, stable rule, significantly reducing the amount of effort needed to ensure compliance in a practical setting.

The main technology of traditional information security is firewall, intrusion detection and anti-virus software, which is used in the first anti-outer defence, the first anti-service terminal defence terminal passive defence ideas, the complexity and complexity of these security technologies not only increase the complexity of the autonomous system, reduce the efficiency of the system, but also cannot solve the security problem of the information system, and cannot satisfy the security demand of the information system. After a significant stretch of innovative work, individuals utilize the secret word innovation, network security innovation, set forward the idea “confided in figuring” in view of the equipment security module support, Trusted processing from changing the customary protection thoughts, center around the safety efforts taken from the terminal to forestall framework assaults, from the foundation of the stage, the acknowledgment of the security of data frameworks. Believed figuring is chiefly worried about the security of the framework terminal, utilizing a progression of safety efforts to ensure the protection of clients to work on the security of independent frameworks. Its principle plan thought is implanted in a typical machine to oppose altering the equipment gadget - confided in stage module as the base of the trust, the utilization of equipment and programming innovation to join the trust of the base of trust through the trust bind level to the entire independent framework, joined with the security of information stockpiling insurance, client validation and stage respectability of the three significant safety efforts guarantee that the terminal framework security and unwavering quality, to guarantee that the terminal framework is consistently in a condition of conduct anticipated.

Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.

The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered. This group were exposed to a number of Deepfake videos and asked subsequent questions to verify improvement on their level of awareness and detection in context of Deepfake videos. This study has been performed in two phases, where second phase was performed to validate any generalization. The fake videos are tailored for the specific audience and where suited, are created from scratch. Finally, the results are analyzed, and the study’s goals are inferred from the obtained data.

The highly dynamic nature of the cyber domain demands that cyber operators are capable of rapidly evolving and adapting with exquisite timing. These forces, in turn, pressure acquisition specialists to accoutre cyber warfighters to keep pace with both cyber domain advancement and adversary progression. However, in the Department of Defense (DoD), a vigorous tug of war exists between time and risk pressures. Risk reduction is a crucial element of managing any complex enterprise and this is particularly true for the DoD and its acquisition program [1]. This risk aversion comes at significant cost, as obsolescence by risk minimization is a real phenomenon in DoD acquisition programs and significantly limits the adaptability of its operational cyber forces. Our previous research generated three recommendations for reforming policy to deliver performance at the “speed of relevance” [3]. In this paper we focus on one of the recommendations: “Manage rather than avoid risk—especially time-based risks”. While this advice can apply to many areas of human endeavor, it has elevated urgency in cyberspace. Incomplete risk metrics lead to overly conservative acquisition efforts that imperil timely procurement of advanced cyber capabilities and repel innovators. Effective cyber defense operations require acquisition risk models to be extended beyond fiscal and technical risk metrics of performance, to include risks associated with the cost of failing to meet immediate mission requirements. This paper proposes a time-shifting approach to simultaneously (a) accelerate capability delivery while maintaining traditional rigor, and (b) achieve optimal balance between fiscal, performance, and time risks.

Edge computing supports the deployment of ubiquitous, smart services by providing computing and storage closer to terminal devices. However, ensuring the full security and privacy of computations performed at the edge is challenging due to resource limitation. This paper responds to this challenge and proposes an adaptive approach to defense randomization among the edge data centers via a stochastic game, whose solution corresponds to the optimal security deployment at the network's edge. Moreover, security risk is evaluated subjectively based on Prospect Theory to reflect realistic scenarios where the attacker and the edge system do not similarly perceive the status of the infrastructure. The results show that a non-deterministic defense policy yields better security compared to a static defense strategy.

Cross-site scripting (XSS) is a common security vulnerability found in web applications. DOM-based XSS, one of the variants, is becoming particularly more prevalent with the boom of single-page applications where most of the UI changes are achieved by modifying the DOM through in-browser scripting. It is very easy for developers to introduce XSS vulnerabilities into web applications since there are many ways for user-controlled, unsanitized input to flow into a Web API and get interpreted as HTML markup and JavaScript code. An emerging Web API proposal called Trusted Types aims to prevent DOM XSS by making Web APIs secure by default. Different from other XSS mitigations that mostly focus on post-development protection, Trusted Types direct developers to write XSS-free code in the first place. A common concern when adopting a new security mechanism is how much effort is required to refactor existing code bases. In this paper, we report a case study on adopting Trusted Types in a well-established web framework. Our experience can help the web community better understand the benefits of making web applications compatible with Trusted Types, while also getting to know the related challenges and resolutions. We focused our work on Angular, which is one of the most popular web development frameworks available on the market.

Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.

AI technologies have demonstrated great value to missions as diverse as space-based imagery analysis, cyberattack warning, supply chain logistics and analysis of microbiologic systems. At the same time, the failure modes of AI technologies are poorly understood. DARPA is working to address this shortfall, with focused R&D, both analytic and empirical. DARPA’s success is essential for the Department to deploy AI technologies, particularly to the tactical edge, where reliable performance is required.

The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an expression of algorithmic accountability, formalized using structural causal models (SCMs). However, causality itself is a concept that needs further exploration. Therefore, in this contribution we confront ideas of SCMs with insights from social theory, more explicitly pragmatism, and argue that formal expressions of causality must always be seen in the context of the social system in which they are applied. This results in the formulation of further research questions and directions.

The United States needs resilient, diverse, and secure supply chains to ensure our economic prosperity and national security. Pandemics and other biological threats, cyber-attacks, climate shocks and extreme weather events, terrorist attacks, geopolitical and economic competition, and other conditions can reduce critical manufacturing capacity and the availability and integrity of critical goods, products, and services.

The cluster-featured conurbation cyber-physical power system (CPPS) interconnected with tie-lines facing the hazards from catastrophic cascading failures. To achieve better real-time performance, enhance the autonomous ability and improve resilience for the clustered conurbation CPPS, the decentralized cyber structure and the corresponding distributed security control strategy is proposed. Facing failures, the real-time security control is incorporated to mitigate cascading failures. The distributed security control problem is solved reliably based on alternating direction method of multipliers (ADMM). The system overall resilience degradation index(SORDI) adopted reflects the influence of cascading failures on both the topological integrity and operational security. The case study illustrates the decentralized cyber layer and distributed control will decrease the data congestion and enhance the autonomous ability for clusters, thus perform better effectiveness in mitigating the cascading failures, especially in topological perspective. With the proposed distributed security control strategy, curves of SORDI show more characteristics of second-order percolation transition and the cascading failure threshold increase, which is more efficient when the initial failure size is near the threshold values or step-type inflection point. Because of the feature of geological aggregation under cluster-based attack, the efficiency of the cluster-focused distributed security control strategy is more obvious than other nodes attack circumstances.

Due to its minimal price and expandable wireless open system interconnection options for the coming years, wireless mesh networking is appealing, developing, and novel medium of speech, which is why it is becoming a somewhat widely used communication field. In all network types, one of the essential factors for prevalent and trustworthy communication is cybersecurity. The IEEE 802.11 working gathering has created various correspondence guidelines. Yet, they are by and by focusing on the 802.11s standard because of its dynamic setup and geography learning abilities. Information, voice, and directions are steered between hubs employing remote lattice organising. WMNs incidentally give nearby 802.11g admittance to customers and connection neighbours utilising 802.11a "backhaul," but this isn’t generally the situation because of changing requirements, for example, top information rate and inclusion range. The small cross-sectional organisation emerged as a fundamental innovation to enable broadband system management in large regions. It benefits specialised organisations by reducing the cost of sending networks and end customers by providing ubiquitous Internet access anywhere, anytime. Given the idea of wireless mesh networking and the lack of integrated organisational technology, small grid networks are powerless against malicious attacks. In the meantime, the limit of multi-radio multi-channel correspondence, the need for heterogeneous organisation coordination, and the interest for multi-bounce remote equality often render conventional security strategies ineffectual or challenging to carry out. Thus, wireless mesh networking presents new issues that require more viable and relevant arrangements. WMNs have piqued the curiosity of both scholastics and industry because of their promising future. Numerous testbeds are built for research purposes, and business items for veritable WMNs are accessible. Anyway, a few concerns should be cleared up before they can very well become widespread. For example, the accessible MAC and routing conventions are not customisable; the throughput drops impressively with an increasing number of hubs or bounces in WMNs. Because of the weakness of WMNs against various malicious attacks, the security and protection of correspondence is a serious concern. For example, enemies can sniff long-distance correspondence to obtain sensitive data. Attackers can carry out DoS attacks and control the substance of the information sent through compromised hubs, thereby endangering the company’s secret, accessibility authenticity, and integrity. WMNs, like compact Impromptu Organisations (MANETs), share a typical medium, no traffic aggregate point, and incredible topography. Due to these restrictions, normal safety frameworks in wired associations can’t be quickly applied to WMNs. Also, the techniques utilised in MANETs are not viable with WMNs. This is because of the manner in which WMNs expand MANETs in different ways. Framework centres are generally outfitted with an assortment of radios. Then, at that point, many channels are doled out to every centre to work with concurrent data move and diversity.

Focusing on security management for supply chain under emergencies, this paper analyzes the characteristics of supply chain risk, clarifies the relationship between business continuity management and security management for supply chain, organizational resilience and security management for supply chain separately, so as to propose suggestions to promote the realization of security management for supply chain combined these two concepts, which is of guiding significance for security management for supply chain and quality assurance of products and services under emergencies.

This paper focuses on the typical business scenario of intelligent factory, it includes the manufacturing process, carries out hierarchical security protection, forms a full coverage industrial control security protection network, completes multi-means industrial control security direct protection, at the same time, it utilizes big data analysis, dynamically analyzes the network security situation, completes security early warning, realizes indirect protection, and finally builds a self sensing and self-adjusting industrial network security protection system It provides a reliable reference for the development of intelligent manufacturing industry.

Intelligent networked vehicles are rapidly developing in intelligence and networking. The communication architecture is becoming more complex, external interfaces are richer, and data types are more complex. Different from the information security of the traditional Internet of Things, the scenarios that need to be met for the security of the Internet of Vehicles are more diverse and the security needs to be more stable. Based on the security technology of traditional Internet of Things, password application is the main protection method to ensure the privacy and non-repudiation of data communication. This article mainly elaborates the application of security protection methods using password-related protection technologies in car-side scenarios and summarizes the security protection recommendations of contemporary connected vehicles in combination with the secure communication architecture of the Internet of Vehicles.

Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed.

We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements.