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2022-01-11
Everson, Douglas, Cheng, Long.  2021.  Compressing Network Attack Surfaces for Practical Security Analysis. 2021 IEEE Secure Development Conference (SecDev). :23–29.
Testing or defending the security of a large network can be challenging because of the sheer number of potential ingress points that need to be investigated and evaluated for vulnerabilities. In short, manual security testing and analysis do not easily scale to large networks. While it has been shown that clustering can simplify the problem somewhat, the data structures and formats returned by the latest network mapping tools are not conducive to clustering algorithms. In this paper we introduce a hybrid similarity algorithm to compute the distance between two network services and then use those calculations to support a clustering algorithm designed to compress a large network attack surface by orders of magnitude. Doing so allows for new testing strategies that incorporate outlier detection and smart consolidation of test cases to improve accuracy and timeliness of testing. We conclude by presenting two case studies using an organization's network attack surface data to demonstrate the effectiveness of this approach.
Lee, Yun-kyung, Kim, Young-ho, Kim, Jeong-nyeo.  2021.  IoT Standard Platform Architecture That Provides Defense against DDoS Attacks. 2021 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). :1–3.
IoT devices have evolved with the goal of becoming more connected. However, for security it is necessary to reduce the attack surface by allowing only necessary devices to be connected. In addition, as the number of IoT devices increases, DDoS attacks targeting IoT devices also increase. In this paper, we propose a method to apply the zero trust concept of SDP as a way to enhance security and prevent DDoS attacks in the IoT device network to which the OCF platform, one of the IoT standard platforms, is applied. The protocol proposed in this paper needs to perform additional functions in IoT devices, and the processing overhead due to the functions is 62.6ms on average. Therefore, by applying the method proposed in this paper, although there is a small amount of processing overhead, DDoS attacks targeting the IoT network can be defended and the security of the IoT network can be improved.
2022-01-10
Li, Yanjie.  2021.  The Application Analysis of Artificial Intelligence in Computer Network Technology. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :1126–1129.
In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.
Freas, Christopher B., Shah, Dhara, Harrison, Robert W..  2021.  Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy.
Thomas, Diya.  2021.  A Graph-based Approach to Detect DoB Attack. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). :422–423.
Wireless sensor networks (WSNs) are underlying network infrastructure for a variety of surveillance applications. The network should be tolerant of unexpected failures of sensor nodes to meet the Quality of Service (QoS) requirements of these applications. One major cause of failure is active security attacks such as Depletion-of-Battery (DoB) attacks. This paper model the problem of detecting such attacks as an anomaly detection problem in a dynamic graph. The problem is addressed by employing a cluster ensemble approach called the K-Means Spectral and Hierarchical ensemble (KSH) approach. The experimental result shows that KSH detected DoB attacks with better accuracy when compared to baseline approaches.
Guan, Xiaojuan, Ma, Yuanyuan, SHAO, Zhipeng, Cao, Wantian.  2021.  Research on Key Node Method of Network Attack Graph Based on Power Information Physical System. 2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC)2021 IEEE 11th International Conference on Electronics Information and Emergency Communication (ICEIEC). :48–51.
With the increasing scale of network, the scale of attack graph has been becoming larger and larger, and the number of nodes in attack graph is also increasing, which can not directly reflect the impact of nodes on the whole system. Therefore, in this paper, a method was proposed to determine the key nodes of network attack graph of power information physical system to solve the problem of uncertain emphasis of security protection of attack graph.
Yao, Bing, Xie, Jianmin, Wang, Hongyu, Su, Jing.  2021.  Degree-sequence Homomorphisms For Homomorphic Encryption Of Information. 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC). 5:132–136.
The cipher-text homomorphism encryption algorithm (homomorphic encryption) are used for the cloud safe and to solve the integrity, availability and controllability of information. For homomorphic encryption, we, by Topsnut-gpw technique, design: degree-sequence homomorphisms and their inverses, degree-sequence homomorphic chain, graph-set homomorphism, colored degree-sequence matrices and every-zero Cds-matrix groups, degree-coinciding degree-sequence lattice, degree-joining degree-sequence lattice, as well as degree-sequence lattice homomorphism, since number-based strings made by Topsnut-gpws of topological coding are irreversible, and Topsnut-gpws can realize: one public-key corresponds two or more privatekeys, and more public-key correspond one or more private-keys for asymmetric encryption algorithm.
Jiao, Jian, Zhao, Haini, Liu, Yong.  2021.  Analysis and Detection of Android Ransomware for Custom Encryption. 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET). :220–225.
At present, the detection of encrypted ransomware under the Android platform mainly relies on analyzing the API call of the encryption function. But for ransomware that uses a custom encryption algorithm, the method will be invalid. This article analyzed the files before and after encryption by the ransomware, and found that there were obvious changes in the information entropy and file name of the files. Based on this, this article proposed a detection method for encrypted ransomware under the Android platform. Through pre-setting decoy files and the characteristic judgment before and after the execution of the sample to be tested, completed the detection and judgment of the ransomware. Having tested 214 samples, this method can be porved to detect encrypted ransomware accurately under the Android platform, with an accuracy rate of 98.24%.
2021-12-22
Ortega, Alfonso, Fierrez, Julian, Morales, Aythami, Wang, Zilong, Ribeiro, Tony.  2021.  Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment. 2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW). :78–87.
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given blackbox system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.
Guerdan, Luke, Raymond, Alex, Gunes, Hatice.  2021.  Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). :3789–3798.
As machine learning approaches are increasingly used to augment human decision-making, eXplainable Artificial Intelligence (XAI) research has explored methods for communicating system behavior to humans. However, these approaches often fail to account for the affective responses of humans as they interact with explanations. Facial affect analysis, which examines human facial expressions of emotions, is one promising lens for understanding how users engage with explanations. Therefore, in this work, we aim to (1) identify which facial affect features are pronounced when people interact with XAI interfaces, and (2) develop a multitask feature embedding for linking facial affect signals with participants' use of explanations. Our analyses and results show that the occurrence and values of facial AU1 and AU4, and Arousal are heightened when participants fail to use explanations effectively. This suggests that facial affect analysis should be incorporated into XAI to personalize explanations to individuals' interaction styles and to adapt explanations based on the difficulty of the task performed.
Panda, Akash Kumar, Kosko, Bart.  2021.  Bayesian Pruned Random Rule Foams for XAI. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.
Murray, Bryce, Anderson, Derek T., Havens, Timothy C..  2021.  Actionable XAI for the Fuzzy Integral. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
The adoption of artificial intelligence (AI) into domains that impact human life (healthcare, agriculture, security and defense, etc.) has led to an increased demand for explainable AI (XAI). Herein, we focus on an under represented piece of the XAI puzzle, information fusion. To date, a number of low-level XAI explanation methods have been proposed for the fuzzy integral (FI). However, these explanations are tailored to experts and its not always clear what to do with the information they return. In this article we review and categorize existing FI work according to recent XAI nomenclature. Second, we identify a set of initial actions that a user can take in response to these low-level statistical, graphical, local, and linguistic XAI explanations. Third, we investigate the design of an interactive user friendly XAI report. Two case studies, one synthetic and one real, show the results of following recommended actions to understand and improve tasks involving classification.
2021-12-21
Guo, Hao, Dolhansky, Brian, Hsin, Eric, Dinh, Phong, Ferrer, Cristian Canton, Wang, Song.  2021.  Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). :686–696.
Due to respectively limited training data, different entities addressing the same vision task based on certain sensitive images may not train a robust deep network. This paper introduces a new vision task where various entities share task-specific image data to enlarge each other's training data volume without visually disclosing sensitive contents (e.g. illegal images). Then, we present a new structure-based training regime to enable different entities learn task-specific and reconstruction-proof image representations for image data sharing. Specifically, each entity learns a private Deep Poisoning Module (DPM) and insert it to a pre-trained deep network, which is designed to perform the specific vision task. The DPM deliberately poisons convolutional image features to prevent image reconstructions, while ensuring that the altered image data is functionally equivalent to the non-poisoned data for the specific vision task. Given this equivalence, the poisoned features shared from one entity could be used by another entity for further model refinement. Experimental results on image classification prove the efficacy of the proposed method.
Bertino, Elisa, Brancik, Kenneth.  2021.  Services for Zero Trust Architectures - A Research Roadmap. 2021 IEEE International Conference on Web Services (ICWS). :14–20.
The notion of Zero Trust Architecture (ZTA) has been introduced as a fine-grained defense approach. It assumes that no entities outside and inside the protected system can be trusted and therefore requires articulated and high-coverage deployment of security controls. However, ZTA is a complex notion which does not have a single design solution; rather it consists of numerous interconnected concepts and processes that need to be assessed prior to deciding on a solution. In this paper, we outline a ZTA design methodology based on cyber risks and the identification of known high security risks. We then discuss challenges related to the design and deployment of ZTA and related solutions. We also discuss the role that service technology can play in ZTA.
Xiaojian, Zhang, Liandong, Chen, Jie, Fan, Xiangqun, Wang, Qi, Wang.  2021.  Power IoT Security Protection Architecture Based on Zero Trust Framework. 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). :166–170.
The construction of the power Internet of Things has led various terminals to access the corporate network on a large scale. The internal and external business interaction and data exchange are more extensive. The current security protection system is based on border isolation protection. This is difficult to meet the needs of the power Internet of Things connection and open shared services. This paper studies the application scheme of the ``zero trust'' typical business scenario of the power Internet of Things with ``Continuous Identity Authentication and Dynamic Access Control'' as the core, and designs the power internet security protection architecture based on zero trust.
Rodigari, Simone, O'Shea, Donna, McCarthy, Pat, McCarry, Martin, McSweeney, Sean.  2021.  Performance Analysis of Zero-Trust Multi-Cloud. 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). :730–732.
Zero Trust security model permits to secure cloud native applications while encrypting all network communication, authenticating, and authorizing every request. The service mesh can enable Zero Trust using a side-car proxy without changes to the application code. To the best of our knowledge, no previous work has provided a performance analysis of Zero Trust in a multi-cloud environment. This paper proposes a multi-cloud framework and a testing workflow to analyse performance of the data plane under load and the impact on the control plane, when Zero Trust is enabled. The results of preliminary tests show that Istio has reduced latency variability in responding to sequential HTTP requests. Results also reveal that the overall CPU and memory usage can increase based on service mesh configuration and the cloud environment.
Hatakeyama, Koudai, Kotani, Daisuke, Okabe, Yasuo.  2021.  Zero Trust Federation: Sharing Context under User Control towards Zero Trust in Identity Federation. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops). :514–519.
Perimeter models, which provide access control for protecting resources on networks, make authorization decisions using the source network of access requests as one of critical factors. However, such models are problematic because once a network is intruded, the attacker gains access to all of its resources. To overcome the above problem, a Zero Trust Network (ZTN) is proposed as a new security model in which access control is performed by authenticating users who request access and then authorizing such requests using various information about users and devices called contexts. To correctly make authorization decisions, this model must take a large amount of various contexts into account. However, in some cases, an access control mechanism cannot collect enough context to make decisions, e.g., when an organization that enforces access control joins the identity federation and uses systems operated by other organizations. This is because the contexts collected using the systems are stored in individual systems and no federation exists for sharing contexts. In this study, we propose the concept of a Zero Trust Federation (ZTF), which applies the concept of ZTN under the identity federation, and a method for sharing context among systems of organizations. Since context is sensitive to user privacy, we also propose a mechanism for sharing contexts under user control. We also verify context sharing by implementing a ZTF prototype.
Li, Yan, Lu, Yifei, Li, Shuren.  2021.  EZAC: Encrypted Zero-Day Applications Classification Using CNN and K-Means. 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). :378–383.
With the rapid development of traffic encryption technology and the continuous emergence of various network services, the classification of encrypted zero-day applications has become a major challenge in network supervision. More seriously, many attackers will utilize zero-day applications to hide their attack behaviors and make attack undetectable. However, there are very few existing studies on zero-day applications. Existing works usually select and label zero-day applications from unlabeled datasets, and these are not true zero-day applications classification. To address the classification of zero-day applications, this paper proposes an Encrypted Zero-day Applications Classification (EZAC) method that combines Convolutional Neural Network (CNN) and K-Means, which can effectively classify zero-day applications. We first use CNN to classify the flows, and for the flows that may be zero-day applications, we use K-Means to divide them into several categories, which are then manually labeled. Experimental results show that the EZAC achieves 97.4% accuracy on a public dataset (CIC-Darknet2020), which outperforms the state-of-the-art methods.
Kazempour, Narges, Mirmohseni, Mahtab, Aref, Mohammad Reza.  2021.  Anonymous Mutual Authentication: An Information Theoretic Framework. 2021 Iran Workshop on Communication and Information Theory (IWCIT). :1–6.
We consider the anonymous mutual authentication problem, which consists of a certificate authority, single or multiple verifiers, many legitimate users (provers) and any arbitrary number of illegitimate users. The legal verifier and a legitimate user must be mutually authenticated to each other using the user's key, while the identity of the user must stay unrevealed. An attacker (illegitimate prover) as well as an illegal verifier must fail in authentication. A general interactive information theoretic framework in a finite field is proposed, where the normalized total key rate as a metric for reliability is defined. Maximizing this rate has a trade-off with establishing anonymity. The problem is studied in two different scenarios: centralized scenario (one single verifier performs the authentication process) and distributed scenario (authentication is done by N verifiers, distributively). For both scenarios, achievable schemes, which satisfy the completeness, soundness (at both verifier and prover) and anonymity properties, are proposed. Increasing the size of the field, results in the key rate approaching its upper bound.
Kowalski, Dariusz R., Mosteiro, Miguel A..  2021.  Time and Communication Complexity of Leader Election in Anonymous Networks. 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS). :449–460.
We study the problem of randomized Leader Election in synchronous distributed networks with indistinguishable nodes. We consider algorithms that work on networks of arbitrary topology in two settings, depending on whether the size of the network, i.e., the number of nodes \$n\$, is known or not. In the former setting, we present a new Leader Election protocol that improves over previous work by lowering message complexity and making it close to a lower bound by a factor in \$$\backslash$widetildeO($\backslash$sqrtt\_mix$\backslash$sqrt$\backslash$Phi)\$, where $\Phi$ is the conductance and \textsubscriptmix is the mixing time of the network graph. We then show that lacking the network size no Leader Election algorithm can guarantee that the election is final with constant probability, even with unbounded communication. Hence, we further classify the problem as Leader Election (the classic one, requiring knowledge of \$n\$ - as is our first protocol) or Revocable Leader Election, and present a new polynomial time and message complexity Revocable Leader Election algorithm in the setting without knowledge of network size. We analyze time and message complexity of our protocols in the CONGEST model of communication.
2021-12-20
Sun, Ziwen, Zhang, Shuguo.  2021.  Modeling of Security Risk for Industrial Cyber-Physics System under Cyber-Attacks. 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). :361–368.
Due to the insufficient awareness of decision makers on the security risks of industrial cyber-physical systems(ICPS) under cyber-attacks, it is difficult to take effective defensive measures according to the characteristics of different cyber-attacks in advance. To solve the above problem, this paper gives a qualitative analysis method of ICPS security risk from the perspective of defenders. The ICPS being attacked is modeled as a dynamic closed-loop fusion model where the mathematical models of the physical plant and the feedback controller are established. Based on the fusion model, the disruption resources generated by attacks are mathematically described. Based on the designed Kalman filter, the detection of attacks is judged according to the residual value of the system. According to the disruption resources and detectability, a general security risk level model is further established to evaluate the security risk level of the system under attacks. The simulation experiments are conducted by using Matlab to analyze the destructiveness and detectability of attacks, where the results show that the proposed qualitative analysis method can effectively describe the security risk under the cyber-attacks.
Zheng, Shiyuan, Xie, Hong, Lui, John C.S..  2021.  Social Visibility Optimization in OSNs with Anonymity Guarantees: Modeling, Algorithms and Applications. 2021 IEEE 37th International Conference on Data Engineering (ICDE). :2063–2068.
Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his "social visibility". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". Then we design an algorithmic framework named as "AdaExp", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.
Deng, Han, Wang, Zhechon, Zhang, Yazhen.  2021.  Overview of Privacy Protection Data Release Anonymity Technology. 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :151–156.
The collection of digital information by governments, companies and individuals creates tremendous opportunities for knowledge and information-based decision-making. Driven by mutual benefit and laws and regulations, there is a need for data exchange and publication between all parties. However, data in its original form usually contains sensitive information about individuals and publishing such data would violate personal privacy. Privacy Protection Data Distribution (PPDP) provides methods and tools to release useful information while protecting data privacy. In recent years, PPDP has received extensive attention from the research community, and many solutions have been proposed for different data release scenarios. How to ensure the availability of data under the premise of protecting user privacy is the core problem to be solved in this field. This paper studies the existing achievements of privacy protection data release anonymity technology, focusing on the existing anonymity technology in three aspects of high-dimensional, high-deficiency, and complex relational data, and analyzes and summarizes them.
Yang, Yuhan, Zhou, Yong, Wang, Ting, Shi, Yuanming.  2021.  Reconfigurable Intelligent Surface Assisted Federated Learning with Privacy Guarantee. 2021 IEEE International Conference on Communications Workshops (ICC Workshops). :1–6.
In this paper, we consider a wireless federated learning (FL) system concerning differential privacy (DP) guarantee, where multiple edge devices collaboratively train a shared model under the coordination of a central base station (BS) through over-the-air computation (AirComp). However, due to the heterogeneity of wireless links, it is difficult to achieve the optimal trade-off between model privacy and accuracy during the FL model aggregation. To address this issue, we propose to utilize the reconfigurable intelligent surface (RIS) technology to mitigate the communication bottleneck in FL by reconfiguring the wireless propagation environment. Specifically, we aim to minimize the model optimality gap while strictly meeting the DP and transmit power constraints. This is achieved by jointly optimizing the device transmit power, artificial noise, and phase shifts at RIS, followed by developing a two-step alternating minimization framework. Simulation results will demonstrate that the proposed RIS-assisted FL model achieves a better trade-off between accuracy and privacy than the benchmarks.
Mahboob, Jamal, Coffman, Joel.  2021.  A Kubernetes CI/CD Pipeline with Asylo as a Trusted Execution Environment Abstraction Framework. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). :0529–0535.
Modern commercial software development organizations frequently prescribe to a development and deployment pattern for releases known as continuous integration / continuous deployment (CI/CD). Kubernetes, a cluster-based distributed application platform, is often used to implement this pattern. While the abstract concept is fairly well understood, CI/CD implementations vary widely. Resources are scattered across on-premise and cloud-based services, and systems may not be fully automated. Additionally, while a development pipeline may aim to ensure the security of the finished artifact, said artifact may not be protected from outside observers or cloud providers during execution. This paper describes a complete CI/CD pipeline running on Kubernetes that addresses four gaps in existing implementations. First, the pipeline supports strong separation-of-duties, partitioning development, security, and operations (i.e., DevSecOps) roles. Second, automation reduces the need for a human interface. Third, resources are scoped to a Kubernetes cluster for portability across environments (e.g., public cloud providers). Fourth, deployment artifacts are secured with Asylo, a development framework for trusted execution environments (TEEs).