Biblio

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2021-01-25
Yoon, S., Cho, J.-H., Kim, D. S., Moore, T. J., Free-Nelson, F., Lim, H..  2020.  Attack Graph-Based Moving Target Defense in Software-Defined Networks. IEEE Transactions on Network and Service Management. 17:1653–1668.
Moving target defense (MTD) has emerged as a proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing the attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. Recently, the significant advance of software-defined networking (SDN) technology has enabled several complex system operations to be highly flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. In this paper, by leveraging the advanced SDN technology, we developed an attack graph-based MTD technique that shuffles a host's network configurations (e.g., MAC/IP/port addresses) based on its criticality, which is highly exploitable by attackers when the host is on the attack path(s). To this end, we developed a hierarchical attack graph model that provides a network's vulnerability and network topology, which can be utilized for the MTD shuffling decisions in selecting highly exploitable hosts in a given network, and determining the frequency of shuffling the hosts' network configurations. The MTD shuffling with a high priority on more exploitable, critical hosts contributes to providing adaptive, proactive, and affordable defense services aiming to minimize attack success probability with minimum MTD cost. We validated the out performance of the proposed MTD in attack success probability and MTD cost via both simulation and real SDN testbed experiments.
Niu, L., Ramasubramanian, B., Clark, A., Bushnell, L., Poovendran, R..  2020.  Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks*. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :162–173.
This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.
2021-03-15
Toma, A., Krayani, A., Marcenaro, L., Gao, Y., Regazzoni, C. S..  2020.  Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. :1–7.
Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.
2021-01-18
Liu, J., Tong, X., Zhang, M., Wang, Z..  2020.  The Design of S-box Based on Combined Chaotic Map. 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :350–353.
The strength of the substitution box (S-box) determines the security of the cryptographic algorithm because it's the only nonlinear component in the block cipher. Because of the disadvantages of non-uniformity sequence and limited range in the one-dimension (1D) chaotic map, this paper constructs the logistic map and the sine map into a combined chaotic map, and a new S-box construction method based on this combined chaotic map is presented. Performance tests were performed on the S-box, including nonlinearity, linear probability, differential probability, strict avalanche criterion, bits independence criterion. Compared with others S-box, this result indicates that the S-box has more excellent cryptographic performance and can be used as a nonlinear component in the lightweight block cipher algorithm.
2021-02-16
Liu, F., Eugenio, E., Jin, I. H., Bowen, C..  2020.  Differentially Private Generation of Social Networks via Exponential Random Graph Models. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1695—1700.
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.
2021-03-29
Dai, Q., Shi, L..  2020.  A Game-Theoretic Analysis of Cyber Attack-Mitigation in Centralized Feeder Automation System. 2020 IEEE Power Energy Society General Meeting (PESGM). :1–5.
The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.
2020-12-21
Raza, A., Ulanskyi, V..  2020.  A General Approach to Assessing the Trustworthiness of System Condition Prognostication. 2020 IEEE Aerospace Conference. :1–8.
This paper proposes a mathematical model for assessing the trustworthiness of the system condition prognosis. The set of mutually exclusive events at the time of predictive checking are analyzed. Correct and incorrect decisions correspond to events such as true-positive, false-positive, true-negative, and false-negative. General expressions for computing the probabilities of possible decisions when predicting the system condition at discrete times are proposed. The paper introduces the effectiveness indicators of predictive maintenance in the form of average operating costs, total error probability, and a posteriori probability of failure-free operation in the upcoming interval. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions for a specific stochastic deterioration process.
2021-03-15
Nieto-Chaupis, H..  2020.  Hyper Secure Cognitive Radio Communications in an Internet of Space Things Network Based on the BB84 Protocol. 2020 Intermountain Engineering, Technology and Computing (IETC). :1–5.
Once constellation of satellites are working in a collaborative manner, the security of their messages would have to be highly secure from all angles of scenarios by which the praxis of eavesdropping constitutes a constant thread for the instability of the different tasks and missions. In this paper we employ the Bennet-Brassard commonly known as the BB84 protocol in conjunction to the technique of Cognitive Radio applied to the Internet of Space Things to build a prospective technology to guarantee the communications among geocentric orbital satellites. The simulations have yielded that for a constellation of 5 satellites, the probability of successful of completion the communication might be of order of 75% ±5%.
Bouzegag, Y., Teguig, D., Maali, A., Sadoudi, S..  2020.  On the Impact of SSDF Attacks in Hard Combination Schemes in Cognitive Radio Networks. 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). :19–24.
One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.
2021-02-08
Nisperos, Z. A., Gerardo, B., Hernandez, A..  2020.  Key Generation for Zero Steganography Using DNA Sequences. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1–6.
Some of the key challenges in steganography are imperceptibility and resistance to detection of steganalysis algorithms. Zero steganography is an approach to data hiding such that the cover image is not modified. This paper focuses on the generation of stego-key, which is an essential component of this steganographic approach. This approach utilizes DNA sequences and shifting and flipping operations in its binary code representation. Experimental results show that the key generation algorithm has a low cracking probability. The algorithm satisfies the avalanche criterion.
2021-02-03
He, S., Lei, D., Shuang, W., Liu, C., Gu, Z..  2020.  Network Security Analysis of Industrial Control System Based on Attack-Defense Tree. 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). :651—655.
In order to cope with the network attack of industrial control system, this paper proposes a quantifiable attack-defense tree model. In order to reduce the influence of subjective factors on weight calculation and the probability of attack events, the Fuzzy Analytic Hierarchy Process and the Attack-Defense Tree model are combined. First, the model provides a variety of security attributes for attack and defense leaf nodes. Secondly, combining the characteristics of leaf nodes, a fuzzy consistency matrix is constructed to calculate the security attribute weight of leaf nodes, and the probability of attack and defense leaf nodes. Then, the influence of defense node on attack behavior is analyzed. Finally, the network risk assessment of typical airport oil supply automatic control system has been undertaken as a case study using this attack-defense tree model. The result shows that this model can truly reflect the impact of defense measures on the attack behavior, and provide a reference for the network security scheme.
2021-02-10
Bendary, A., Koksal, C. E..  2020.  Order-Optimal Scaling of Covert Communication over MIMO AWGN Channels. 2020 IEEE Conference on Communications and Network Security (CNS). :1—9.
Covert communication, i.e., communication with a low probability of detection (LPD), has attracted a huge body of work. Recent studies have concluded that the maximal covert coding rate of the discrete memoryless channels and the additive white Gaussian noise (AWGN) channels is diminishing with the blocklength: the maximum information nats that can be transmitted covertly and reliably over such channels is only on the order of the square root of the blocklength. In this paper, we study covert communication over multiple-input multiple-output (MIMO) AWGN channels. We derive the order-optimal scaling law of the number of covert nats when the maximal covert coding rate of MIMO AWGN channels is diminishing with the blocklength. Furthermore, we provide a comparative discussion for the case in which secrecy and energy undetectability constraints are combined.
2021-05-13
Wang, Xiaoyu, Gao, Yuanyuan, Zhang, Guangna, Guo, Mingxi.  2020.  Prediction of Optimal Power Allocation for Enhancing Security-Reliability Tradeoff with the Application of Artificial Neural Networks. 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC). :40–45.
In this paper, we propose a power allocation scheme in order to improve both secure and reliable performance in the wireless two-hop threshold-selection decode-and-forward (DF) relaying networks, which is so crucial to set a threshold value related the signal-to-noise ratio (SNR) of the source signal at relay nodes for perfect decoding. We adapt the maximal-ratio combining (MRC) receiving SNR from the direct and relaying paths both at the destination and at the eavesdropper. Particularly worth mentioning is that the closed expression form of outage probability and intercept probability is driven, which can quantify the security and reliability, respectively. We also make endeavors to utilize a metric to tradeoff the security and the reliability (SRT) and find out the relevance between them in the balanced case. But beyond that, in the pursuit of tradeoff performance, power allocation tends to depend on the threshold value. In other words, it provides a new method optimizing total power to the source and the relay by the threshold value. The results are obtained from analysis, confirmed by simulation, and predicted by artificial neural networks (ANNs), which is trained with back propagation (BP) algorithm, and thus the feasibility of the proposed method is verified.
2021-01-25
Hu, W., Zhang, L., Liu, X., Huang, Y., Zhang, M., Xing, L..  2020.  Research on Automatic Generation and Analysis Technology of Network Attack Graph. 2020 IEEE 6th 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). :133–139.
In view of the problem that the overall security of the network is difficult to evaluate quantitatively, we propose the edge authority attack graph model, which aims to make up for the traditional dependence attack graph to describe the relationship between vulnerability behaviors. This paper proposed a network security metrics based on probability, and proposes a network vulnerability algorithm based on vulnerability exploit probability and attack target asset value. Finally, a network security reinforcement algorithm with network vulnerability index as the optimization target is proposed based on this metric algorithm.
2020-12-14
Arjoune, Y., Salahdine, F., Islam, M. S., Ghribi, E., Kaabouch, N..  2020.  A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication. 2020 International Conference on Information Networking (ICOIN). :459–464.
Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.
2021-02-16
Kowalski, P., Zocholl, M., Jousselme, A.-L..  2020.  Explainability in threat assessment with evidential networks and sensitivity spaces. 2020 IEEE 23rd International Conference on Information Fusion (FUSION). :1—8.
One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.
2021-03-18
Bi, X., Liu, X..  2020.  Chinese Character Captcha Sequential Selection System Based on Convolutional Neural Network. 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). :554—559.

To ensure security, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is widely used in people's online lives. This paper presents a Chinese character captcha sequential selection system based on convolutional neural network (CNN). Captchas composed of English and digits can already be identified with extremely high accuracy, but Chinese character captcha recognition is still challenging. The task we need to complete is to identify Chinese characters with different colors and different fonts that are not on a straight line with rotation and affine transformation on pictures with complex backgrounds, and then perform word order restoration on the identified Chinese characters. We divide the task into several sub-processes: Chinese character detection based on Faster R-CNN, Chinese character recognition and word order recovery based on N-Gram. In the Chinese character recognition sub-process, we have made outstanding contributions. We constructed a single Chinese character data set and built a 10-layer convolutional neural network. Eventually we achieved an accuracy of 98.43%, and completed the task perfectly.

2021-01-20
Aman, W., Haider, Z., Shah, S. W. H., Rahman, M. M. Ur, Dobre, O. A..  2020.  On the Effective Capacity of an Underwater Acoustic Channel under Impersonation Attack. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—7.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

2021-02-23
Wang, L., Guo, D..  2020.  Secure Communication Based on Reliability-Based Hybrid ARQ and LDPC Codes. 2020 Prognostics and Health Management Conference (PHM-Besançon). :304—308.
This paper designs a re-transmission strategy to intensify the security of communication over the additive white Gaussian noise (AWGN) wire-tap channel. In this scheme, irregular low-density parity-check (LDPC) codes work with reliability-based hybrid automatic repeat-request (RB-HARQ). For irregular LDPC codes, the variable nodes have different degrees, which means miscellaneous protection for the nodes. In RB-HARQ protocol, the legitimate receiver calls for re-transmissions including the most unreliable bits at decoder's outputting. The bits' reliability can be evaluated by the average magnitude of a posteriori probability log-likelihood ratios (APP LLRs). Specifically, this scheme utilizes the bit-error rate (BER) to assess the secrecy performance. Besides, the paper gives close analyses of BER through theoretical arguments and simulations. Results of numerical example demonstrate that RB-HARQ protocol with irregular LDPC codes can hugely reinforce the security performance of the communication system.
2020-12-28
Slavic, G., Campo, D., Baydoun, M., Marin, P., Martin, D., Marcenaro, L., Regazzoni, C..  2020.  Anomaly Detection in Video Data Based on Probabilistic Latent Space Models. 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). :1—8.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

2021-03-09
Hegde, M., Kepnang, G., Mazroei, M. Al, Chavis, J. S., Watkins, L..  2020.  Identification of Botnet Activity in IoT Network Traffic Using Machine Learning. 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA). :21—27.

Today our world benefits from Internet of Things (IoT) technology; however, new security problems arise when these IoT devices are introduced into our homes. Because many of these IoT devices have access to the Internet and they have little to no security, they make our smart homes highly vulnerable to compromise. Some of the threats include IoT botnets and generic confidentiality, integrity, and availability (CIA) attacks. Our research explores botnet detection by experimenting with supervised machine learning and deep-learning classifiers. Further, our approach assesses classifier performance on unbalanced datasets that contain benign data, mixed in with small amounts of malicious data. We demonstrate that the classifiers can separate malicious activity from benign activity within a small IoT network dataset. The classifiers can also separate malicious activity from benign activity in increasingly larger datasets. Our experiments have demonstrated incremental improvement in results for (1) accuracy, (2) probability of detection, and (3) probability of false alarm. The best performance results include 99.9% accuracy, 99.8% probability of detection, and 0% probability of false alarm. This paper also demonstrates how the performance of these classifiers increases, as IoT training datasets become larger and larger.

2021-09-16
Lemeshko, Oleksandr, Yeremenko, Oleksandra, Yevdokymenko, Maryna, Ageyev, Dmytro.  2020.  Redundancy Cyber Resiliency Technique Based on Fast ReRouting under Security Metric. 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S T). :815–818.
The paper is devoted to the development and research of the redundancy cyber resiliency technique based on fast rerouting under security metric with the implementation of the basic schemes for network elements protection, namely node, link, path, and bandwidth. Within the model, the secure fast rerouting task is formulated as an optimization problem of nonlinear programming. The model is configured in order to calculate primary and backup paths that contain links with the minimum values of the probability of compromise that is achieved by using the appropriate weights in the objective function, the value of which is minimized. Numerical research has been conducted, results of which proved the proposed model efficiency and adequacy for the practical application.
2021-01-11
Shin, H. C., Chang, J., Na, K..  2020.  Anomaly Detection Algorithm Based on Global Object Map for Video Surveillance System. 2020 20th International Conference on Control, Automation and Systems (ICCAS). :793—795.

Recently, smart video security systems have been active. The existing video security system is mainly a method of detecting a local abnormality of a unit camera. In this case, it is difficult to obtain the characteristics of each local region and the situation for the entire watching area. In this paper, we developed an object map for the entire surveillance area using a combination of surveillance cameras, and developed an algorithm to detect anomalies by learning normal situations. The surveillance camera in each area detects and tracks people and cars, and creates a local object map and transmits it to the server. The surveillance server combines each local maps to generate a global map for entire areas. Probability maps were automatically calculated from the global maps, and normal and abnormal decisions were performed through trained data about normal situations. For three reporting status: normal, caution, and warning, and the caution report performance shows that normal detection 99.99% and abnormal detection 86.6%.

2021-03-17
Sadu, A., Stevic, M., Wirtz, N., Monti, A..  2020.  A Stochastic Assessment of Attacks based on Continuous-Time Markov Chains. 2020 6th IEEE International Energy Conference (ENERGYCon). :11—16.

With the increasing interdependence of critical infrastructures, the probability of a specific infrastructure to experience a complex cyber-physical attack is increasing. Thus it is important to analyze the risk of an attack and the dynamics of its propagation in order to design and deploy appropriate countermeasures. The attack trees, commonly adopted to this aim, have inherent shortcomings in representing interdependent, concurrent and sequential attacks. To overcome this, the work presented here proposes a stochastic methodology using Petri Nets and Continuous Time Markov Chain (CTMC) to analyze the attacks, considering the individual attack occurrence probabilities and their stochastic propagation times. A procedure to convert a basic attack tree into an equivalent CTMC is presented. The proposed method is applied in a case study to calculate the different attack propagation characteristics. The characteristics are namely, the probability of reaching the root node & sub attack nodes, the mean time to reach the root node and the mean time spent in the sub attack nodes before reaching the root node. Additionally, the method quantifies the effectiveness of specific defenses in reducing the attack risk considering the efficiency of individual defenses.

2021-01-15
Khodabakhsh, A., Busch, C..  2020.  A Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Photo- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.