Defense Model Research Articles

Model Pendidikan Pancasila dan Bela Negara di UPN Veteran Yogyakarta dalam Menjawab Tantangan Kebangsaan

This study aims to analyze the education model of Pancasila and National Defense in UPN Veteran. The research used descriptive method with a qualitative approach. The research was conducted in UPN Veteran Yogyakarta with its university student as the subject of the research. Data of the research were obtained through empirical and critical study and observation of Pancasila and National Defense learning. The primary data were obtained through interviews while the secondary data were obtained through literature reviews. The analysis technique used deductive-inductive analysis technique. The result of the research showed that the Pancasila and National Defense education model developed in UPN Veteran Yogyakarta consisted of two ways which were co-curricular and extracurricular programs.

Deep image prior based defense against adversarial examples

Recently, deep neural networks (DNNs) have shown serious vulnerability to adversarial examples with imperceptible perturbation to clean images. To counter this issue, many powerful defensive methods (e.g., ComDefend) focus on rectifying the adversarial examples with well-trained models from a large training dataset (e.g., clean-adversarial image pairs). However, such methods rely heavily on the learned external priors from an external large training dataset, while neglecting the rich image internal priors of the input itself, thus limiting the generalization of the defense models against the adversarial examples with biased image statistics from the external training dataset. Motivated by deep image prior that can capture rich image statistics from a single image, we propose an effective Deep Image Prior Driven Defense (DIPDefend) method against adversarial examples. With a DIP generator to fit the target/adversarial input, we find that our image reconstruction exhibits quite interesting learning preference from a feature learning perspectives, i.e., the early stage primarily learns the robust features resistant to adversarial perturbation, followed by learning non-robust features that are sensitive to adversarial perturbation. Besides, we develop an adaptive stopping strategy that adapts our method to diverse images. In this way, the proposed model obtains a unique defender for each individual adversarial input, thus being robust to various attackers. Experimental results demonstrate the superiority of our method over the state-of-the-art defense methods against white-box and black-box adversarial attacks.

Network Security Threat Situation Recognition Based on Attack and Defense Game Model

At present, network security is a problem that needs to be solved urgently in the current network era. In order to reduce the loss caused by network security threats, and also use limited computer resources to select the best defense strategy, a set of network attack and defense game models are designed to identify the situation of network security threats. Aiming at the unstable problem of the security quantification process of traditional network node information, a network security threat situation identification based on the offensive and defensive game model is proposed. First, build an offensive and defensive game model, design the corresponding situation identification strategy, and calculate the overall utility and expected output of both offensive and defensive parties while clarifying the changes in the authority of both sides during the offensive and defensive process. This paper takes the offensive and defensive game model as the theoretical basis of the research, uses the algorithm as the auxiliary research, and integrates its important content to analyze and research the optimization of the algorithm to improve the network security threat situation identification. This paper uses the classic offensive and defensive game model as the research object to identify the threat situation of network security. In the process of selecting the offensive and defensive game model, it is necessary to have a better grasp of the relevant algorithms. This article introduces the offensive and defensive game model into the network security threat situation identification, and on this basis, it deeply discusses the applicability and effectiveness of the offensive and defensive game model in the complex network security threat situation identification, and provides better for the establishment of future network security systems. Reference. The experimental results show that this study is effective and feasible in the offensive and defensive game model in the network security threat situation identification. It should be noted that in the defense process, the probability of success and the degree of damage of the attack path must be grasped to ensure that the optimal offensive and defensive decision-making meets the actual needs of network security.

Advanced Generative Adversarial Defense Model using Instance Sparsity Mapping Network

딥 러닝은 자율 자동차, 비디오 감시 시스템 및 의료 이미지 검사 시스템을 포함한 다양한 실제 애플리케이션에서 엄청난 결과를 달성할 수 있음을 보여주었다. 심층 컨볼루션 신경망과 적대적 학습은 이러한 돌파구를 달성하는 데 기여했다. 그러나 적대적 네트워크는 적대적 혼란에 취약하다. 적대적 혼란은 비지도 학습 과정에 영향을 미치며 성능을 크게 감소시킨다. 이는 특히 안전 및 보안 목적으로 사용하는 중요한 애플리케이션의 경우 엄청난 결과를 초래할 수 있다. 적대적 공격은 착시현상을 일으키는 혼란을 만들어 낸다. 이 경우 모델이 샘플을 잘못 분류하고 실수를 통해 원하는 작업을 수행할 수 없다. 그러한 이유로 적대적 공격에 대한 적대적 네트워크의 견고성을 높이는 방어 솔루션을 적용하는 것이 중요하다. 이 문제를 극복하기 위해 적대적 희소성 매핑 네트워크를 채택한 방어 솔루션을 제안한다. 제안된 네트워크는 실제 데이터를 학습하여 공격받아 변형된 이미지를 구별하고, 공격 여부 분류기는 독립적으로 입출력 차이의 변환하여 분류하도록 훈련된다. 생성된 샘플의 적대적인 노이즈가 융합된 경우 방어 모델로 수행한다. 방어 모델이 분류기와 결합되어 공격 수준을 검토한다. 제안된 모델은 적대적 공격의 융합에 대한 높은 견고성을 달성하기 위해 적대적 공격에 대항하는 방어 수단을 제공한다.

Attack-defense game for critical infrastructure considering the cascade effect

The important status of critical infrastructure makes it a target of attacks in the new era. Based on game theory, we analyze the offensive and defensive issues of critical infrastructure from a network perspective, and we propose a network offensive and defensive game model considering the impact of the cascade effect. We first establish a two-player game model for an attacker and a defender acting simultaneously. The nodes are categorized to achieve the transformation of the focus of the game from decision-making for the nodes to resource allocation, thereby simplifying the calculation space of the network game strategy. Both pure strategy and mixed strategy are carried out for the theoretical analysis and simulation of four kinds of optimization problems. With the pure strategy, the initial decision equilibrium is that the attacker's strategy is to attack the Equal Cost-Effective ratio nodes (ECE nodes), and the defender defends the High Cost-Efficient ratio nodes (HCE nodes). At this point, the defender's payoff is 0. With the mixed strategy, different optimization problems have different equilibrium solutions. In particular, the attacker adds a new equilibrium solution for the strategy with the cascading effect, which is related to the structural properties of the network. For the defender, the strategy is different when maximizing his/her own profit or minimizing the attacker's benefit. This can explain how the attacker and the defender can achieve a win-win situation.

Risk Culture Is a Necessary Condition for Enterprise Risk Management to Succeed

Risk culture is essential for the success of enterprise risk management (ERM). Enterprise risk management is different from silo risk management where risks were mainly managed by one department. Enterprise risk management means that the risk management is integrated from Board to the last employee of the organization. This is much easier said than done. This means that every employee of the organization has a role to perform as a risk manager; this is essentially the essence of the three lines of defense model. It is not just important for a business to achieve the sales objective and other key performance parameters, but the real question is long-term sustainability which comes from managing the risks. This article touches upon the different aspects of risk culture which is a building block of the success of ERM currently prevailing essential for developing a good risk management practice.

Pandemic risk management; protecting people while ensuring business continuity

The COVID‐19 pandemic swept across the globe in the latter half of 2019, throughout 2020 and into 2021. In response, many organizations implemented work from home policies, while others stopped operations entirely in an effort to limit the spread throughout their workforce and supporting communities. This containment strategy was not universally viable; long‐term shutdowns impacted the economic viability of companies, and some industries were designated as an “essential service” and thus continued operations. These employers faced the proposition of balancing the needs of the business and the community with a continued responsibility to provide a safe workplace for employees. This paper demonstrates how the application of common risk management methodologies, such as bowtie analysis combined with an appropriate assurance and verification process (e.g., the lines of defense model), can help the risks associated with a resumption or continuation of in‐person operations in a pandemic to be better understood and ensure the measures in place to manage said risk are appropriate and effective.

BDDR: An Effective Defense Against Textual Backdoor Attacks

Deep neural networks (DNNs) have been recently shown to be vulnerable to backdoor attacks. The infected model performs well on benign testing samples, however, the attacker can trigger the infected model to misbehave by the backdoor. In the field of natural language processing (NLP), some backdoor attack methods have been proposed, and achieved high attack success rates on a variety of popular models. However, researches on the defense of textual backdoor attacks are lacking and the defense effects are bad at present. In this paper, we propose an effective textual backdoor defense model, namely BDDR, which contains two steps: (1) detecting suspicious words in the sample and (2) reconstructing the original text by deletion or replacement. In the replacement part, we use the pre-trained masking language model taking BERT as an example to generate replacement words. We conduct exhaustive experiments to evaluate our proposed defense model by defending against various backdoor attacks on two infected models trained using two benchmark datasets. Overall, BDDR reduces the attack success rate of word-level backdoor attacks by more than 90%, and reduces the attack success rate of sentence-level backdoor attacks by more than 60%. The experimental results show that our proposed method can always significantly reduce the attack success rate compared with the baseline method.

An integrative evolutionary framework for psychopathology.

The field of psychopathology is in a transformative phase, and is witnessing a renewed surge of interest in theoretical models of mental disorders. While many interesting proposals are competing for attention in the literature, they tend to focus narrowly on the proximate level of analysis and lack a broader understanding of biological function. In this paper, we present an integrative framework for mental disorders built on concepts from life history theory, and describe a taxonomy of mental disorders based on its principles, the fast-slow-defense model (FSD). The FSD integrates psychopathology with normative individual differences in personality and behavior, and allows researchers to draw principled distinctions between broad clusters of disorders, as well as identify functional subtypes within current diagnostic categories. Simulation work demonstrates that the model can explain the large-scale structure of comorbidity, including the apparent emergence of a general "p factor" of psychopathology. A life history approach also provides novel integrative insights into the role of environmental risk/protective factors and the developmental trajectories of various disorders.

Review on the Quality Attributes of an Integrated Simulation Software for Weapon Systems

This paper describes the quality attributes of an integrated simulation software for weapon systems named Advanced distributed simulation environment(AddSIM). AddSIM is developed as a key enabler for Defense Modeling & Simulation(M&S) systems which simulate battlefields and used for battle experiments, analyses, military exercises, training, etc. AddSIM shall provide a standard simulation framework of the next Defense M&S systems. Therefore AddSIM shall satisfy not only functional but also quality requirements such as availability, modifiability, performance, testability, usability, and others. AddSIM consists of operating softwares of hierarchical components including graphical user interface, simulation engines, and support services(natural environment model, math utility, etc.), and separated weapon system models executable on the operating softwares. The relation between software architectures and their quality attributes are summarized from previous works. And the AddSIM architecture and its achievements in the aspect of quality attributes are reviewed.

Risk Culture Is a Necessary Condition for Enterprise Risk Management to Succeed

Risk culture is essential for the success of enterprise risk management (ERM). Enterprise risk management is different from silo risk management where risks were mainly managed by one department. Enterprise risk management means that the risk management is integrated from Board to the last employee of the organization. This is much easier said than done. This means that every employee of the organization has a role to perform as a risk manager; this is essentially the essence of the three lines of defense model. It is not just important for a business to achieve the sales objective and other key performance parameters, but the real question is long-term sustainability which comes from managing the risks. This article touches upon the different aspects of risk culture which is a building block of the success of ERM currently prevailing essential for developing a good risk management practice.

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

Generation Method of Power Network Security Defense Strategy Based on Markov Decision Process

Aiming at the problem that the current generation method of power networksecurity defense strategy ignores the dependency relationship between nodes,resulting in closed-loop attack graph, which makes the defense strategy notgenerate attack path, resulting in poor defense effect and long generationresponse time of power network security defense strategy, a generationmethod of power network security defense strategy based on Markov decisionprocess is proposed. Based on the generation of network attack and defensediagram, the paper describes the state change of attack network by usingMarkov decision-making process correlation principle, introduces discountfactor, calculates the income value of attack and defense game process,constructs the evolutionary game model of attack and defense, solves theobjective function according to the dynamic programming theory, obtains theoptimal strategy set and outputs the final results, and generates the powernetwork security defense strategy. The experimental results show that the proposed method has good defense effect and can effectively shorten thegeneration response time of power network security defense strategy.

Anti-Interpolation: An Attack Facilitator Hiding Adversaries Into Images

Deep learning models show the vulnerability to adversarial examples, which can be attacked to make incorrect predictions. As one of the key image preprocessing techniques, interpolation may weaken the robustness of the attack and even disable the perturbation. Thus, the malicious images cannot achieve the attack purpose. In this work, anti-interpolation, as an attack facilitator, is proposed, where interpolation does not affect the attack results intended by the perturbation. Based on anti-interpolation, an attack framework is extended to achieve adversarial attacks, including targeted attack, non-targeted attack, and auxiliary attack. Furthermore, gradient derivation is developed based on the interpolation output of large-sized images, which can infer the interpolation mode in the black box attack scenario. Compared with the current adversarial attacks, anti-interpolation can achieve a higher attack success rate. Moreover, anti-interpolation is capable of attacking defensive models with various model corrections.

About the concept of the organizational and technical model of cyber defense

The current threats to cyber security in modern conditions have been identified. The components of the functioning of the national cyber security system are regulated. The powers of the State Special Communications Service in the field of building effective cyber defense in the domestic territory are outlined. The directions of development of organizational and technical model of the national cyber defense system are detailed. The components of the organizational and technical model of cyber defense are generalized. The expediency of accelerating the approval at the state level of the Concept of the organizational and technical model of cyber defense has been updated.

Perceived food safety and food defense responsibility for farmers, transporters, retailers and consumers

PurposeContaminated food is a major source of illnesses around the world. This research seeks to learn how people assign responsibility for two food contamination risks and how they allocate costs to reduce these risks to four members of the food supply chain. The aims are to identify differences between countries and test options to control for cultural differences.Design/methodology/approachA random sample of online panellists from six countries (N = 6,090) was surveyed on how they assigned responsibility for controlling natural and accidental food contamination (traditional food safety) and for controlling intentional contamination (food defense) to farmers, transporters/distributors, retailer grocery stores/restaurants and consumers. They were also asked how they would allocate food safety and defense costs to the four groups. Differences between countries were tested with dummy variables and cultural measures.FindingsIn nearly every country, respondents assigned the least responsibility and allocated the smallest cost shares to consumers. In multivariate models, responsibility and cost-share results differed, suggesting that preferences varied by country and that respondents did not allocate costs the same way they assessed responsibility. The food safety and defense models also differed, implying that the respondents believed the two sources of contamination represented different risks.Originality/valueThis is the first study to examine how adults allocate the responsibility and costs for food safety and defense to farmers, transporters/distributors, retailer grocery stores/restaurants and consumers. Other research did not differentiate between these two food risks. This study also compared Hofstede's cultural measures with the recently developed Minkov's cultural measures.

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition.

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

Boosting Adversarial Attacks on Neural Networks with Better Optimizer

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam iterative fast gradient method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models.

Intrusive Intention Recognition Based on Signaling Game Model

In order to accurately assess the network security situation and identify the intrusive intention of attackers, we propose an intrusive intention identification method based on a signaling game model. Based on the attack path graph, an offensive and defensive signalling game model is established, and the payoffs and costs of attack and defence are quantified using CVSS. By calculating the game equilibrium to quantitatively analyse and predict the attack paths that match the attacker’s intent, administrators can be guided to make defensive decisions.

Boosting the Transferability of Adversarial Examples with Translation Transformation

Although the adversarial examples have achieved an incredible white-box attack rate, they tend to show poor transferability in black-box attacks. Date augmentation is considered to be an effective means of enhancing the adversarial examples transferability. To this end, based on translation transformation we propose a new method to generate more mobile adversarial examples to attack the advanced defense model. By optimizing the original image, the input diversity is improved and the transferability of the adversarial examples generated by further training is enhanced. This method can also be combined with the attack method based on gradient, and the new method can make the attack success rate higher. Experiments on ImageNet data sets show that the proposed method is superior to gradient based methods such as MI-FGSM in black box attack, while maintaining a high success rate of white box attack. We hope that our proposed method of attack can serve as a benchmark for assessing the robustness of networks to opponents and the effectiveness of the different methods of defence.