Biblio

We present CARiSMA, a tool that is originally designed to support model-based security analysis of IT systems. In our recent work, we added several new functionalities to CARiSMA to support the privacy of personal data. Moreover, we introduced a mechanism to assist the system designers to perform a CARiSMA analysis by automatically initializing an appropriate CARiSMA analysis concerning security and privacy requirements. The motivation for our work is Article 25 of Regulation (EU) 2016/679, which requires appropriate technical and organizational controls must be implemented for ensuring that, by default, the processing of personal data complies with the principles on processing of personal data. This implies that initially IT systems must be analyzed to verify if such principles are respected. System models allow the system developers to handle the complexity of systems and to focus on key aspects such as privacy and security. CARiSMA is available at http://carisma.umlsec.de and our screen cast at https://youtu.be/b5zeHig3ARw.

With the growth of Internet in many different aspects of life, users are required to share private information more than ever. Hence, users need a privacy management tool that can enforce complex and customized privacy policies. In this paper, we propose a privacy management system that not only allows users to define complex privacy policies for data sharing actions, but also monitors users' behavior and relationships to generate realistic policies. In addition, the proposed system utilizes formal modeling and model-checking approach to prove that information disclosures are valid and privacy policies are consistent with one another.

Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time to perform fine tuning of hyper-parameters for object classification. We propose a cloud based video analytics system built upon an optimally tuned deep learning model to classify objects from video streams. The tuning of the hyper-parameters including learning rate, momentum, activation function and optimization algorithm is optimized through a mathematical model for efficient analysis of video streams. The system is capable of enhancing its own training data by performing transformations including rotation, flip and skew on the input dataset making it more robust and self-adaptive. The use of in-memory distributed training mechanism rapidly incorporates large number of distinguishing features from the training dataset - enabling the system to perform object classification with least human assistance and external support. The validation of the system is performed by means of an object classification case-study using a dataset of 100GB in size comprising of 88,432 video frames on an 8 node cloud. The extensive experimentation reveals an accuracy and precision of 0.97 and 0.96 respectively after a training of 6.8 hours. The system is scalable, robust to classification errors and can be customized for any real-life situation.

Insider threats can cause immense damage to organizations of different types, including government, corporate, and non-profit organizations. Being an insider, however, does not necessarily equate to being a threat. Effectively identifying valid threats, and assessing the type of threat an insider presents, remain difficult challenges. In this work, we propose a novel breakdown of eight insider threat types, identified by using three insider traits: predictability, susceptibility, and awareness. In addition to presenting this framework for insider threat types, we implement a computational model to demonstrate the viability of our framework with synthetic scenarios devised after reviewing real world insider threat case studies. The results yield useful insights into how further investigation might proceed to reveal how best to gauge predictability, susceptibility, and awareness, and precisely how they relate to the eight insider types.

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the controller area network (CAN) bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper provides the first such algorithm with experiments using three attacks in five (total) scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).

Machine learning has been widely used and achieved considerable results in various research areas. On the other hand, machine learning becomes a big threat when malicious attackers make use it for the wrong purpose. As such a threat, self-evolving botnets have been considered in the past. The self-evolving botnets autonomously predict vulnerabilities by implementing machine learning with computing resources of zombie computers. Furthermore, they evolve based on the vulnerability, and thus have high infectivity. In this paper, we consider several models of Markov chains to counter the spreading of the self-evolving botnets. Through simulation experiments, this paper shows the behaviors of these models.

In this paper, we propose a theoretical framework to investigate the eavesdropping behavior in underwater acoustic sensor networks. In particular, we quantify the eavesdropping activities by the eavesdropping probability. Our derived results show that the eavesdropping probability heavily depends on acoustic signal frequency, underwater acoustic channel characteristics (such as spreading factor and wind speed) and different hydrophones (such as isotropic hydrophones and array hydrophones). Simulation results have further validate the effectiveness and the accuracy of our proposed model.

This paper offers a new approach to modelling the effect of cyber-attacks on reliability of software used in industrial control applications. The model is based on the view that successful cyber-attacks introduce failure regions, which are not present in non-compromised software. The model is then extended to cover a fault tolerant architecture, such as the 1-out-of-2 software, popular for building industrial protection systems. The model is used to study the effectiveness of software maintenance policies such as patching and "cleansing" ("proactive recovery") under different adversary models ranging from independent attacks to sophisticated synchronized attacks on the channels. We demonstrate that the effect of attacks on reliability of diverse software significantly depends on the adversary model. Under synchronized attacks system reliability may be more than an order of magnitude worse than under independent attacks on the channels. These findings, although not surprising, highlight the importance of using an adequate adversary model in the assessment of how effective various cyber-security controls are.

Network motifs are often called the building blocks of networks. Analysis of motifs is found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. In the context of power grids, this phenomenon of the impact of local structure has been recently documented in fragility analysis and power system classification. At the same time, most studies of power system networks still tend to focus on global topo-logical measures of power grids, often failing to unveil hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. In this paper a pilot study of motif-based analysis of power grid robustness under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of power systems.

Supervisory Control and Data Acquisition(SCADA) communications are often subjected to various sophisticated cyber-attacks mostly because of their static system characteristics, enabling an attacker for easier profiling of the target system(s) and thereby impacting the Critical Infrastructures(CI). In this Paper, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, leveraging the existing communication network with an end-to-end IP-Hopping technique among trusted peers. The main contribution involves the design and implementation of MTD Architecture on Iowa State's PowerCyber testbed for targeted cyber-attacks, without compromising the availability of a SCADA system and studying the delay and throughput characteristics for different hopping rates in a realistic environment. Finally, we study two cases and provide mitigations for potential weaknesses of the proposed mechanism. Also, we propose to incorporate port mutation to further increase attack complexity as part of future work.

Conventional intrusion detection systems for smart grid communications rely heavily on static based attack detection techniques. In essence, signatures created from historical data are compared to incoming network traffic to identify abnormalities. In the case of attacks where no historical data exists, static based approaches become ineffective thus relinquishing system resilience and stability. Moving target defense (MTD) has shown to be effective in discouraging attackers by introducing system entropy to increase exploit costs. Increase in exploit cost leads to a decrease in profitability for an attacker. In this paper, a Moving Target Defense Intrusion Detection System (MTDIDS) is proposed for smart grid IPv6 based advanced metering infrastructure. The advantage of MTDIDS is the ability to detect anomalies across moving targets by means of planar keys thereupon increasing detection rate. Evaluation of MTDIDS was carried out in a smart grid advanced metering infrastructure simulated in MATLAB.

Volume of digital data is increasing at a faster rate and the security of the data is at risk while being transit on a network as well as at rest. The execution time of full disk encryption in large servers is significant because of the computational complexity associated with disk encryption. Hence it is necessary to reduce the execution time of full disk encryption from the application point of view. In this work a full disk encryption algorithm namely EME2 AES (Encrypt Mix Encrypt V2 Advanced Encryption Standard) is analyzed. The execution speed of this algorithm is reduced by means of multicore compatible parallel implementation which makes use of available cores. Parallel implementation is executed on a multicore machine with 8 cores and speed up on the multicore implementation is measured. Results show that the multicore implementation of EME2 AES using OpenMP is up to 2.85 times faster than sequential execution for the chosen infrastructure and data range.

Internet of Things (IoT) is an emerging trend that is changing the way devices connect and communicate. Integration of cloud computing with IoT i.e. Cloud of Things (CoT) provide scalability, virtualized control and access to the services provided by IoT. Security issues are a major obstacle in widespread deployment and application of CoT. Among these issues, authentication and identification of user is crucial. In this study paper, survey of various authentication schemes is carried out. The aim of this paper is to study a multifactor authentication system which uses secret splitting in detail. The system uses exclusive-or operations, encryption algorithms and Diffie-Hellman key exchange algorithm to share key over the network. Security analysis shows the resistance of the system against different types of attacks.

Embedded and mobile devices forming part of the Internet-of-Things (IoT) need new authentication technologies and techniques. This requirement is due to the increase in effort and time attackers will use to compromise a device, often remote, based on the possibility of a significant monetary return. This paper proposes exploiting a device's accelerometers in-built functionality to implement multi-factor authentication. An experimental embedded system designed to emulate a typical mobile device is used to implement the ideas and investigated as proof-of-concept.

Electro-hydraulic servo actuation system is a mechanical, electrical and hydraulic mixing complex system. If it can't be repaired for a long time, it is necessary to consider the possibility of occurrence of multiple faults. Considering this possibility, this paper presents an extended Kalman filter (EKF) based method for multiple faults diagnosis. Through analysing the failure modes and mechanism of the electro-hydraulic servo actuation system and modelling selected typical failure modes, the relationship between the key parameters of the system and the faults is obtained. The extended Kalman filter which is a commonly used algorithm for estimating parameters is used to on-line fault diagnosis. Then use the extended Kalman filter to diagnose potential faults. The simulation results show that the multi-fault diagnosis method based on extended Kalman filter is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.

5G, the fifth generation of mobile communication networks, is considered as one of the main IoT enablers. Connecting billions of things, 5G/IoT will be dealing with trillions of GBytes of data. Securing such large amounts of data is a very challenging task. Collected data varies from simple temperature measurements to more critical transaction data. Thus, applying uniform security measures is a waste of resources (processing, memory, and network bandwidth). Alternatively, a multi-level security model needs to be applied according to the varying requirements. In this paper, we present a multi-level security scheme (BLP) applied originally in the information security domain. We review its application in the network domain, and propose a modified version of BLP for the 5G/IoT case. The proposed model is proven to be secure and compliant with the model rules.

Users search for multimedia content with different underlying motivations or intentions. Study of user search intentions is an emerging topic in information retrieval since understanding why a user is searching for a content is crucial for satisfying the user's need. In this paper, we aimed at automatically recognizing a user's intent for image search in the early stage of a search session. We designed seven different search scenarios under the intent conditions of finding items, re-finding items and entertainment. We collected facial expressions, physiological responses, eye gaze and implicit user interactions from 51 participants who performed seven different search tasks on a custom-built image retrieval platform. We analyzed the users' spontaneous and explicit reactions under different intent conditions. Finally, we trained machine learning models to predict users' search intentions from the visual content of the visited images, the user interactions and the spontaneous responses. After fusing the visual and user interaction features, our system achieved the F-1 score of 0.722 for classifying three classes in a user-independent cross-validation. We found that eye gaze and implicit user interactions, including mouse movements and keystrokes are the most informative features. Given that the most promising results are obtained by modalities that can be captured unobtrusively and online, the results demonstrate the feasibility of deploying such methods for improving multimedia retrieval platforms.

A method for the multiple faults diagnosis in linear analog circuits is presented in this paper. The proposed approach is based upon the concept named by the indirect compensation theorem. This theorem is reducing the procedure of fault diagnosis in the analog circuit to the symbolic analysis process. An extension of the indirect compensation theorem for the linear subcircuit is proposed. The indirect compensation provides equivalent replacement of the n-ports subcircuit by n norators and n fixators of voltages and currents. The proposed multiple faults diagnosis techniques can be used for evaluation of any kind of terminal characteristics of the two-port network. For calculation of the circuit determinant expressions, the Generalized Parameter Extraction Method is implemented. The main advantage of the analysis method is that it is cancellation free. It requires neither matrix nor ordinary graph description of the circuit. The process of symbolic circuit analysis is automated by the freeware computer program Cirsym which can be used online. The experimental results are presented to show the efficiency and reliability of the proposed technique.

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty, and very large strategy spaces. For the game model, we propose parameterized heuristic strategies for both players. Our heuristics exploit the topological structure of the attack graphs and employ different sampling methodologies to overcome the computational complexity in determining players' actions. Given the complexity of the game, we employ a simulation-based methodology, and perform empirical game analysis over an enumerated set of these heuristic strategies. Finally, we conduct experiments based on a variety of game settings to demonstrate the advantages of our heuristics in obtaining effective defense strategies which are robust to the uncertainty of the security environment.

With the continued advancement of the internet and relevant programs, the number of exploitable loopholes in security systems increases. One such exploit that is plaguing the software scene is ransomware, a type of malware that weaves its way through these security loopholes and denies access to intellectual property and documents via encryption. The culprits will then demand a ransom as a price for data decryption. Many businesses face the issue of not having stringent security measures that are sufficient enough to negate the threat of ransomware. This jeopardizes the availability of sensitive data as corporations and individuals are at threat of losing data crucial to business or personal operations. Although certain countermeasures to deal with ransomware exist, the fact that a plethora of new ransomware cases keeps appearing every year points to the problem that they aren't effective enough. This paper aims to conceptualize practical solutions that can be used as foundations to build on in hope that more effective and proactive countermeasures to ransomware can be developed in the future.

Unlike traditional routing where packets are only stored and forward, network coding allows packets to mix together. New packets can be formed by combining other packets. This technique can provide benefits to the network. Network coding has been shown to improve network throughput, reduce energy consumption, improve network robustness and achieve the network capacity. 5G Network is foreseen as a novel network paradigm enabling massive device connectivity and enabling device-to-device communication (D2D). It has many potential applications ranging from ultra high definition video to virtual reality applications. In this paper, we present the advantages, benefits, scenarios, and applications of Network coding for 5G Network and device-to-device communication. We present the state-of-art research, the theoretical benefits, and detail how network coding can improve 5G Networks and D2D communication. Our results show that network coding can almost double the network throughput while increasing network robustness and decreasing the overall time to disseminate messages.

Distributed denial of service attacks represent continuous threat to availability of information and communication resources. This research conducted the analysis of relevant scientific literature and synthesize parameters on packet and traffic flow level applicable for detection of infrastructure layer DDoS attacks. It is concluded that packet level detection uses two or more parameters while traffic flow level detection often used only one parameter which makes it more convenient and resource efficient approach in DDoS detection.

In low-power wireless networking, new applications such as cooperative robots or industrial closed-loop control demand for network-wide consensus at low-latency and high reliability. Distributed consensus protocols is a mature field of research in a wired context, but has received little attention in low-power wireless settings. In this paper, we present A2: Agreement in the Air, a system that brings distributed consensus to low-power multi-hop networks. A2 introduces Synchrotron, a synchronous transmissions kernel that builds a robust mesh by exploiting the capture effect, frequency hopping with parallel channels, and link-layer security. A2 builds on top of this reliable base layer and enables the two- and three-phase commit protocols, as well as network services such as group membership, hopping sequence distribution and re-keying. We evaluate A2 on four public testbeds with different deployment densities and sizes. A2 requires only 475 ms to complete a two-phase commit over 180 nodes. The resulting duty cycle is 0.5% for 1-minute intervals. We show that A2 achieves zero losses end-to-end over long experiments, representing millions of data points. When adding controlled failures, we show that two-phase commit ensures transaction consistency in A2 while three-phase commit provides liveness at the expense of inconsistency under specific failure scenarios.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.