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2019-11-12
Hu, Yayun, Li, Dongfang.  2019.  Formal Verification Technology for Asynchronous Communication Protocol. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :482-486.

For aerospace FPGA software products, traditional simulation method faces severe challenges to verify product requirements under complicated scenarios. Given the increasing maturity of formal verification technology, this method can significantly improve verification work efficiency and product design quality, by expanding coverage on those "blind spots" in product design which were not easily identified previously. Taking UART communication as an example, this paper proposes several critical points to use formal verification for asynchronous communication protocol. Experiments and practices indicate that formal verification for asynchronous communication protocol can effectively reduce the time required, ensure a complete verification process and more importantly, achieve more accurate and intuitive results.

E.V., Jaideep Varier, V., Prabakar, Balamurugan, Karthigha.  2019.  Design of Generic Verification Procedure for IIC Protocol in UVM. 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA). :1146-1150.

With the growth of technology, designs became more complex and may contain bugs. This makes verification an indispensable part in product development. UVM describe a standard method for verification of designs which is reusable and portable. This paper verifies IIC bus protocol using Universal Verification Methodology. IIC controller is designed in Verilog using Vivado. It have APB interface and its function and code coverage is carried out in Mentor graphic Questasim 10.4e. This work achieved 83.87% code coverage and 91.11% functional coverage.

Xiao, Lili, Xiang, Shuangqing, Zhuy, Huibiao.  2018.  Modeling and Verifying SDN with Multiple Controllers. Proceedings of the 33rd Annual ACM Symposium on Applied Computing. :419-422.

SDN (Software Defined Network) with multiple controllers draws more attention for the increasing scale of the network. The architecture can handle what SDN with single controller is not able to address. In order to understand what this architecture can accomplish and face precisely, we analyze it with formal methods. In this paper, we apply CSP (Communicating Sequential Processes) to model the routing service of SDN under HyperFlow architecture based on OpenFlow protocol. By using model checker PAT (Process Analysis Toolkit), we verify that the models satisfy three properties, covering deadlock freeness, consistency and fault tolerance.

Duan, Zhangbo, Mao, Hongliang, Chen, Zhidong, Bai, Xiaomin, Hu, Kai, Talpin, Jean-Pierre.  2018.  Formal Modeling and Verification of Blockchain System. Proceedings of the 10th International Conference on Computer Modeling and Simulation. :231-235.

As a decentralized and distributed secure storage technology, the notion of blockchain is now widely used for electronic trading in finance, for issuing digital certificates, for copyrights management, and for many other security-critical applications. With applications in so many domains with high-assurance requirements, the formalization and verification of safety and security properties of blockchain becomes essential, and the aim of the present paper. We present the model-based formalization, simulation and verification of a blockchain protocol by using the SDL formalism of Telelogic Tau. We consider the hierarchical and modular SDL model of the blockchain protocol and exercise a methodology to formally simulate and verify it. This way, we show how to effectively increase the security and safety of blockchain in order to meet high assurance requirements demanded by its application domains. Our work also provides effective support for assessing different network consensus algorithms, which are key components in blockchain protocols, as well as on the topology of blockchain networks. In conclusion, our approach contributes to setting up a verification methodology for future blockchain standards in digital trading.

Basin, David, Dreier, Jannik, Hirschi, Lucca, Radomirovic, Sa\v sa, Sasse, Ralf, Stettler, Vincent.  2018.  A Formal Analysis of 5G Authentication. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1383-1396.

Mobile communication networks connect much of the world's population. The security of users' calls, SMSs, and mobile data depends on the guarantees provided by the Authenticated Key Exchange protocols used. For the next-generation network (5G), the 3GPP group has standardized the 5G AKA protocol for this purpose. We provide the first comprehensive formal model of a protocol from the AKA family: 5G AKA. We also extract precise requirements from the 3GPP standards defining 5G and we identify missing security goals. Using the security protocol verification tool Tamarin, we conduct a full, systematic, security evaluation of the model with respect to the 5G security goals. Our automated analysis identifies the minimal security assumptions required for each security goal and we find that some critical security goals are not met, except under additional assumptions missing from the standard. Finally, we make explicit recommendations with provably secure fixes for the attacks and weaknesses we found. 

Pîrlea, George, Sergey, Ilya.  2018.  Mechanising Blockchain Consensus. Proceedings of the 7th ACM SIGPLAN International Conference on Certified Programs and Proofs. :78-90.

We present the first formalisation of a blockchain-based distributed consensus protocol with a proof of its consistency mechanised in an interactive proof assistant. Our development includes a reference mechanisation of the block forest data structure, necessary for implementing provably correct per-node protocol logic. We also define a model of a network, implementing the protocol in the form of a replicated state-transition system. The protocol's executions are modeled via a small-step operational semantics for asynchronous message passing, in which packages can be rearranged or duplicated. In this work, we focus on the notion of global system safety, proving a form of eventual consistency. To do so, we provide a library of theorems about a pure functional implementation of block forests, define an inductive system invariant, and show that, in a quiescent system state, it implies a global agreement on the state of per-node transaction ledgers. Our development is parametric with respect to implementations of several security primitives, such as hash-functions, a notion of a proof object, a Validator Acceptance Function, and a Fork Choice Rule. We precisely characterise the assumptions, made about these components for proving the global system consensus, and discuss their adequacy. All results described in this paper are formalised in Coq.

Katsini, Christina, Raptis, George E., Fidas, Christos, Avouris, Nikolaos.  2018.  Towards Gaze-Based Quantification of the Security of Graphical Authentication Schemes. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. :17:1-17:5.

In this paper, we introduce a two-step method for estimating the strength of user-created graphical passwords based on the eye-gaze behaviour during password composition. First, the individuals' gaze patterns, represented by the unique fixations on each area of interest (AOI) and the total fixation duration per AOI, are calculated. Second, the gaze-based entropy of the individual is calculated. To investigate whether the proposed metric is a credible predictor of the password strength, we conducted two feasibility studies. Results revealed a strong positive correlation between the strength of the created passwords and the gaze-based entropy. Hence, we argue that the proposed gaze-based metric allows for unobtrusive prediction of the strength of the password a user is going to create and enables intervention to the password composition for helping users create stronger passwords.

Werner, Gordon, Okutan, Ahmet, Yang, Shanchieh, McConky, Katie.  2018.  Forecasting Cyberattacks as Time Series with Different Aggregation Granularity. 2018 IEEE International Symposium on Technologies for Homeland Security (HST). :1-7.

Cyber defense can no longer be limited to intrusion detection methods. These systems require malicious activity to enter an internal network before an attack can be detected. Having advanced, predictive knowledge of future attacks allow a potential victim to heighten security and possibly prevent any malicious traffic from breaching the network. This paper investigates the use of Auto-Regressive Integrated Moving Average (ARIMA) models and Bayesian Networks (BN) to predict future cyber attack occurrences and intensities against two target entities. In addition to incident count forecasting, categorical and binary occurrence metrics are proposed to better represent volume forecasts to a victim. Different measurement periods are used in time series construction to better model the temporal patterns unique to each attack type and target configuration, seeing over 86% improvement over baseline forecasts. Using ground truth aggregated over different measurement periods as signals, a BN is trained and tested for each attack type and the obtained results provided further evidence to support the findings from ARIMA. This work highlights the complexity of cyber attack occurrences; each subset has unique characteristics and is influenced by a number of potential external factors.

Zhang, Xian, Ben, Kerong, Zeng, Jie.  2018.  Cross-Entropy: A New Metric for Software Defect Prediction. 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). :111-122.

Defect prediction is an active topic in software quality assurance, which can help developers find potential bugs and make better use of resources. To improve prediction performance, this paper introduces cross-entropy, one common measure for natural language, as a new code metric into defect prediction tasks and proposes a framework called DefectLearner for this process. We first build a recurrent neural network language model to learn regularities in source code from software repository. Based on the trained model, the cross-entropy of each component can be calculated. To evaluate the discrimination for defect-proneness, cross-entropy is compared with 20 widely used metrics on 12 open-source projects. The experimental results show that cross-entropy metric is more discriminative than 50% of the traditional metrics. Besides, we combine cross-entropy with traditional metric suites together for accurate defect prediction. With cross-entropy added, the performance of prediction models is improved by an average of 2.8% in F1-score.

Vizarreta, Petra, Sakic, Ermin, Kellerer, Wolfgang, Machuca, Carmen Mas.  2019.  Mining Software Repositories for Predictive Modelling of Defects in SDN Controller. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :80-88.

In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.

Ferenc, Rudolf, Heged\H us, Péter, Gyimesi, Péter, Antal, Gábor, Bán, Dénes, Gyimóthy, Tibor.  2019.  Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions. 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). :8-14.

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

Wei, Shengjun, Zhong, Hao, Shan, Chun, Ye, Lin, Du, Xiaojiang, Guizani, Mohsen.  2018.  Vulnerability Prediction Based on Weighted Software Network for Secure Software Building. 2018 IEEE Global Communications Conference (GLOBECOM). :1-6.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

Zhang, Tianwei, Zhang, Yinqian, Lee, Ruby B..  2018.  Analyzing Cache Side Channels Using Deep Neural Networks. Proceedings of the 34th Annual Computer Security Applications Conference. :174-186.

Cache side-channel attacks aim to breach the confidentiality of a computer system and extract sensitive secrets through CPU caches. In the past years, different types of side-channel attacks targeting a variety of cache architectures have been demonstrated. Meanwhile, different defense methods and systems have also been designed to mitigate these attacks. However, quantitatively evaluating the effectiveness of these attacks and defenses has been challenging. We propose a generic approach to evaluating cache side-channel attacks and defenses. Specifically, our method builds a deep neural network with its inputs as the adversary's observed information, and its outputs as the victim's execution traces. By training the neural network, the relationship between the inputs and outputs can be automatically discovered. As a result, the prediction accuracy of the neural network can serve as a metric to quantify how much information the adversary can obtain correctly, and how effective a defense solution is in reducing the information leakage under different attack scenarios. Our evaluation suggests that the proposed method can effectively evaluate different attacks and defenses.

2019-11-11
Barrett, Ayodele A., Matthee, Machdel.  2018.  A Critical Analysis of Informed Use of Context-aware Technologies. Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists. :126–134.
There is a move towards a future in which consumers of technology are untethered from the devices and technology recedes to the subconscious. One way of achieving this vision is with context-aware technologies, which smartphones exemplify. Key figures in the creation of modern technologies suggest that consumers are fully informed of the implications of the use of these technologies. Typically, privacy policy documents are used both to inform, and gain consent from users of these technologies, on how their personal data will be used. This paper examines opinions of African-based users of smartphones. There is also an examination of the privacy policy statement of a popular app, using Critical Discourse Analysis. The analysis reveals concerns of consumers regarding absence of choice, a lack of knowledge and information privacy erosion are not unfounded.
Martiny, Karsten, Denker, Grit.  2018.  Expiring Decisions for Stream-based Data Access in a Declarative Privacy Policy Framework. Proceedings of the 2Nd International Workshop on Multimedia Privacy and Security. :71–80.
This paper describes how a privacy policy framework can be extended with timing information to not only decide if requests for data are allowed at a given point in time, but also to decide for how long such permission is granted. Augmenting policy decisions with expiration information eliminates the need to reason about access permissions prior to every individual data access operation. This facilitates the application of privacy policy frameworks to protect multimedia streaming data where repeated re-computations of policy decisions are not a viable option. We show how timing information can be integrated into an existing declarative privacy policy framework. In particular, we discuss how to obtain valid expiration information in the presence of complex sets of policies with potentially interacting policies and varying timing information.
Wang, Xiaoyin, Qin, Xue, Bokaei Hosseini, Mitra, Slavin, Rocky, Breaux, Travis D., Niu, Jianwei.  2018.  GUILeak: Tracing Privacy Policy Claims on User Input Data for Android Applications. 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE). :37–47.
The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.
Subahi, Alanoud, Theodorakopoulos, George.  2018.  Ensuring Compliance of IoT Devices with Their Privacy Policy Agreement. 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). :100–107.
In the past few years, Internet of Things (IoT) devices have emerged and spread everywhere. Many researchers have been motivated to study the security issues of IoT devices due to the sensitive information they carry about their owners. Privacy is not simply about encryption and access authorization, but also about what kind of information is transmitted, how it used and to whom it will be shared with. Thus, IoT manufacturers should be compelled to issue Privacy Policy Agreements for their respective devices as well as ensure that the actual behavior of the IoT device complies with the issued privacy policy. In this paper, we implement a test bed for ensuring compliance of Internet of Things data disclosure to the corresponding privacy policy. The fundamental approach used in the test bed is to capture the data traffic between the IoT device and the cloud, between the IoT device and its application on the smart-phone, and between the IoT application and the cloud and analyze those packets for various features. We test 11 IoT manufacturers and the results reveal that half of those IoT manufacturers do not have an adequate privacy policy specifically for their IoT devices. In addition, we prove that the action of two IoT devices does not comply with what they stated in their privacy policy agreement.
Pierce, James, Fox, Sarah, Merrill, Nick, Wong, Richmond, DiSalvo, Carl.  2018.  An Interface Without A User: An Exploratory Design Study of Online Privacy Policies and Digital Legalese. Proceedings of the 2018 Designing Interactive Systems Conference. :1345–1358.
Privacy policies are critical to understanding one's rights on online platforms, yet few users read them. In this pictorial, we approach this as a systemic issue that is part a failure of interaction design. We provided a variety of people with printed packets of privacy policies, aiming to tease out this form's capabilities and limitations as a design interface, to understand people's perception and uses, and to critically imagine pragmatic revisions and creative alternatives to existing privacy policies.
Al-Hasnawi, Abduljaleel, Mohammed, Ihab, Al-Gburi, Ahmed.  2018.  Performance Evaluation of the Policy Enforcement Fog Module for Protecting Privacy of IoT Data. 2018 IEEE International Conference on Electro/Information Technology (EIT). :0951–0957.
The rapid development of the Internet of Things (IoT) results in generating massive amounts of data. Significant portions of these data are sensitive since they reflect (directly or indirectly) peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be communicated, processed, analyzed, and stored by public networks, servers, and clouds; most of them are untrusted parties for data owners. We propose a solution for protecting sensitive IoT data called Policy Enforcement Fog Module (PEFM). The major task of the PEFM solution is mandatory enforcement of privacy policies for sensitive IoT data-wherever these data are accessed throughout their entire lifecycle. The key feature of PEFM is its placement within the fog computing infrastructure, which assures that PEFM operates as closely as possible to data sources within the edge. PEFM enforces policies directly for local IoT applications. In contrast, for remote applications, PEFM provides a self-protecting mechanism based on creating and disseminating Active Data Bundles (ADBs). ADBs are software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the proposed module, we developed a smart home proof-of-concept scenario. We investigate privacy threats for sensitive IoT data. We run simulation experiments, based on network calculus, for testing performance of the PEFM controls for different network configurations. The results of the simulation show that-even with using from 1 to 5 additional privacy policies for improved data privacy-penalties in terms of execution time and delay are reasonable (approx. 12-15% and 13-19%, respectively). The results also show that PEFM is scalable regarding the number of the real-time constraints for real-time IoT applications.
Martiny, Karsten, Elenius, Daniel, Denker, Grit.  2018.  Protecting Privacy with a Declarative Policy Framework. 2018 IEEE 12th International Conference on Semantic Computing (ICSC). :227–234.
This article describes a privacy policy framework that can represent and reason about complex privacy policies. By using a Common Data Model together with a formal shareability theory, this framework enables the specification of expressive policies in a concise way without burdening the user with technical details of the underlying formalism. We also build a privacy policy decision engine that implements the framework and that has been deployed as the policy decision point in a novel enterprise privacy prototype system. Our policy decision engine supports two main uses: (1) interfacing with user interfaces for the creation, validation, and management of privacy policies; and (2) interfacing with systems that manage data requests and replies by coordinating privacy policy engine decisions and access to (encrypted) databases using various privacy enhancing technologies.
Tesfay, Welderufael B., Hofmann, Peter, Nakamura, Toru, Kiyomoto, Shinsaku, Serna, Jetzabel.  2018.  I Read but Don'T Agree: Privacy Policy Benchmarking Using Machine Learning and the EU GDPR. Companion Proceedings of the The Web Conference 2018. :163–166.
With the continuing growth of the Internet landscape, users share large amount of personal, sometimes, privacy sensitive data. When doing so, often, users have little or no clear knowledge about what service providers do with the trails of personal data they leave on the Internet. While regulations impose rather strict requirements that service providers should abide by, the defacto approach seems to be communicating data processing practices through privacy policies. However, privacy policies are long and complex for users to read and understand, thus failing their mere objective of informing users about the promised data processing behaviors of service providers. To address this pertinent issue, we propose a machine learning based approach to summarize the rather long privacy policy into short and condensed notes following a risk-based approach and using the European Union (EU) General Data Protection Regulation (GDPR) aspects as assessment criteria. The results are promising and indicate that our tool can summarize lengthy privacy policies in a short period of time, thus supporting users to take informed decisions regarding their information disclosure behaviors.
Kunihiro, Noboru, Lu, Wen-jie, Nishide, Takashi, Sakuma, Jun.  2018.  Outsourced Private Function Evaluation with Privacy Policy Enforcement. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :412–423.
We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to enforce two different kinds of privacy policies to the process of function evaluation: evaluator policy and client policy. An evaluator policy restricts entities that can conduct function evaluation with the data. A client policy restricts entities that can obtain the result of function evaluation. We demonstrate our construction with three applications: personalized medication, genetic epidemiology, and prediction by machine learning. Experimental results show that the overhead caused by enforcing the two privacy policies is less than 10% compared to function evaluation by homomorphic encryption without any privacy policy enforcement.
2019-11-04
Abani, Noor, Braun, Torsten, Gerla, Mario.  2018.  Betweenness Centrality and Cache Privacy in Information-Centric Networks. Proceedings of the 5th ACM Conference on Information-Centric Networking. :106-116.

In-network caching is a feature shared by all proposed Information Centric Networking (ICN) architectures as it is critical to achieving a more efficient retrieval of content. However, the default "cache everything everywhere" universal caching scheme has caused the emergence of several privacy threats. Timing attacks are one such privacy breach where attackers can probe caches and use timing analysis of data retrievals to identify if content was retrieved from the data source or from the cache, the latter case inferring that this content was requested recently. We have previously proposed a betweenness centrality based caching strategy to mitigate such attacks by increasing user anonymity. We demonstrated its efficacy in a transit-stub topology. In this paper, we further investigate the effect of betweenness centrality based caching on cache privacy and user anonymity in more general synthetic and real world Internet topologies. It was also shown that an attacker with access to multiple compromised routers can locate and track a mobile user by carrying out multiple timing analysis attacks from various parts of the network. We extend our privacy evaluation to a scenario with mobile users and show that a betweenness centrality based caching policy provides a mobile user with path privacy by increasing an attacker's difficulty in locating a moving user or identifying his/her route.

Kahani, Nafiseh, Fallah, Mehran S..  2018.  A Reactive Defense Against Bandwidth Attacks Using Learning Automata. Proceedings of the 13th International Conference on Availability, Reliability and Security. :31:1-31:6.

This paper proposes a new adaptively distributed packet filtering mechanism to mitigate the DDoS attacks targeted at the victim's bandwidth. The mechanism employs IP traceback as a means of distinguishing attacks from legitimate traffic, and continuous action reinforcement learning automata, with an improved learning function, to compute effective filtering probabilities at filtering routers. The solution is evaluated through a number of experiments based on actual Internet data. The results show that the proposed solution achieves a high throughput of surviving legitimate traffic as a result of its high convergence speed, and can save the victim's bandwidth even in case of varying and intense attacks.

Serror, Martin, Henze, Martin, Hack, Sacha, Schuba, Marko, Wehrle, Klaus.  2018.  Towards In-Network Security for Smart Homes. Proceedings of the 13th International Conference on Availability, Reliability and Security. :18:1-18:8.

The proliferation of the Internet of Things (IoT) in the context of smart homes entails new security risks threatening the privacy and safety of end users. In this paper, we explore the design space of in-network security for smart home networks, which automatically complements existing security mechanisms with a rule-based approach, i. e., every IoT device provides a specification of the required communication to fulfill the desired services. In our approach, the home router as the central network component then enforces these communication rules with traffic filtering and anomaly detection to dynamically react to threats. We show that in-network security can be easily integrated into smart home networks based on existing approaches and thus provides additional protection for heterogeneous IoT devices and protocols. Furthermore, in-network security relieves users of difficult home network configurations, since it automatically adapts to the connected devices and services.