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Donkers, Tim, Loepp, Benedikt, Ziegler, Jürgen.  2016.  Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. :169–173.
To increase transparency and interactive control in Recommender Systems, we extended the Matrix Factorization technique widely used in Collaborative Filtering by learning an integrated model of user-generated tags and latent factors derived from user ratings. Our approach enables users to manipulate their preference profile expressed implicitly in the (intransparent) factor space through explicitly presented tags. Furthermore, it seems helpful in cold-start situations since user preferences can be elicited via meaningful tags instead of ratings. We evaluate this approach and present a user study that to our knowledge is the most extensive empirical study of tag-enhanced recommending to date. Among other findings, we obtained promising results in terms of recommendation quality and perceived transparency, as well as regarding user experience, which we analyzed by Structural Equation Modeling.
Yi-Hui Chen, Chi-Shiang Chan, Po-Yu Hsu, Wei-Lin Huang.  2014.  Tagged visual cryptography with access control. Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on. :1-5.

Visual cryptography is a way to encrypt the secret image into several meaningless share images. Noted that no information can be obtained if not all of the shares are collected. Stacking the share images, the secret image can be retrieved. The share images are meaningless to owner which results in difficult to manage. Tagged visual cryptography is a skill to print a pattern onto meaningless share images. After that, users can easily manage their own share images according to the printed pattern. Besides, access control is another popular topic to allow a user or a group to see the own authorizations. In this paper, a self-authentication mechanism with lossless construction ability for image secret sharing scheme is proposed. The experiments provide the positive data to show the feasibility of the proposed scheme.
 

Saeed, Ahmed, Ahmadinia, Ali, Just, Mike.  2016.  Tag-Protector: An Effective and Dynamic Detection of Out-of-bound Memory Accesses. Proceedings of the Third Workshop on Cryptography and Security in Computing Systems. :31–36.

Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. A light-weight solution is presented in this paper to tackle such illegal memory accesses dynamically in C/C++ based applications. We propose a new and effective method of instrumenting an application's source code at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. The proposed solution is evaluated by instrumenting applications from the BugBench benchmark suite and publicly available benchmark software, Runtime Intrusion Prevention Evaluator (RIPE), detecting all the bugs successfully. The performance and memory overhead is further analysed by instrumenting and executing real world applications.

Liu, Xin, London, Kati.  2016.  T.A.I: A Tangible AI Interface to Enhance Human-Artificial Intelligence (AI) Communication Beyond the Screen. Proceedings of the 2016 ACM Conference on Designing Interactive Systems. :281–285.

Social and emotional intelligence of computer systems is increasingly important in human-AI (Artificial Intelligence) interactions. This paper presents a tangible AI interface, T.A.I, that enhances physical engagement in digital communication between users and a conversational AI agent. We describe a compact, pneumatically shape-changing hardware design with a rich set of physical gestures that actuate on mobile devices during real-time conversations. Our user study suggests that the physical presence provided by T.A.I increased users' empathy for, and social connection with the virtual intelligent system, leading to an improved Human-AI communication experience.

Hiller, Jens, Pennekamp, Jan, Dahlmanns, Markus, Henze, Martin, Panchenko, Andriy, Wehrle, Klaus.  2019.  Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments. 2019 IEEE 27th International Conference on Network Protocols (ICNP). :1–12.
An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-to-end security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.
Chen, Jim Q..  2017.  Take the rein of cyber deterrence. 2017 International Conference on Cyber Conflict (CyCon U.S.). :29–35.
Deterrence is badly needed in the cyber domain but it is hard to be achieved. Why is conventional deterrence not working effectively in the cyber domain? What specific characteristics should be considered when deterrence strategies are developed in this man-made domain? These are the questions that this paper intends to address. The research conducted helps to reveal what cyber deterrence can do and what it cannot do so that focus can be put on the enhancement of what it can do. To include varied perspectives, literature review is conducted. Some research works are specifically examined. Based on these studies, this research proposes a holistic approach in cyber deterrence that is empowered by artificial intelligence and machine learning. This approach is capable of making sudden, dynamic, stealthy, and random changes initiated by different contexts. It is able to catch attackers by surprise. The surprising and changing impact inflicts a cost on attackers and makes them to re-calculate the benefits that they might gain through further attacks, thus discouraging or defeating adversaries both mentally and virtually, and eventually controlling escalation of cyber conflicts.
Thimmaraju, Kashyap, Shastry, Bhargava, Fiebig, Tobias, Hetzelt, Felicitas, Seifert, Jean-Pierre, Feldmann, Anja, Schmid, Stefan.  2018.  Taking Control of SDN-Based Cloud Systems via the Data Plane. Proceedings of the Symposium on SDN Research. :1:1-1:15.

Virtual switches are a crucial component of SDN-based cloud systems, enabling the interconnection of virtual machines in a flexible and "software-defined" manner. This paper raises the alarm on the security implications of virtual switches. In particular, we show that virtual switches not only increase the attack surface of the cloud, but virtual switch vulnerabilities can also lead to attacks of much higher impact compared to traditional switches. We present a systematic security analysis and identify four design decisions which introduce vulnerabilities. Our findings motivate us to revisit existing threat models for SDN-based cloud setups, and introduce a new attacker model for SDN-based cloud systems using virtual switches. We demonstrate the practical relevance of our analysis using a case study with Open vSwitch and OpenStack. Employing a fuzzing methodology, we find several exploitable vulnerabilities in Open vSwitch. Using just one vulnerability we were able to create a worm that can compromise hundreds of servers in a matter of minutes. Our findings are applicable beyond virtual switches: NFV and high-performance fast path implementations face similar issues. This paper also studies various mitigation techniques and discusses how to redesign virtual switches for their integration.

Shillair, Ruth.  2016.  Talking About Online Safety: A Qualitative Study Exploring the Cybersecurity Learning Process of Online Labor Market Workers. Proceedings of the 34th ACM International Conference on the Design of Communication. :21:1–21:9.

Technological changes bring great efficiencies and opportunities; however, they also bring new threats and dangers that users are often ill prepared to handle. Some individuals have training at work or school while others have family or friends to help them. However, there are few widely known or ubiquitous educational programs to inform and motivate users to develop safe cybersecurity practices. Additionally, little is known about learning strategies in this domain. Understanding how active Internet users have learned their security practices can give insight into more effective learning methods. I surveyed 800 online labor workers to discover their learning processes. They shared how they had to construct their own schema and negotiate meaning in a complex domain. Findings suggest a need to help users build a dynamic mental model of security. Participants recommend encouraging participatory and constructive learning, multi-model dissemination, and ubiquitous opportunities for learning security behaviors.

Milton, Richard, Buyuklieva, Boyana, Hay, Duncan, Hudson-Smith, Andy, Gray, Steven.  2018.  Talking to GNOMEs: Exploring Privacy and Trust Around Internet of Things Devices in a Public Space. Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. :LBW632:1–LBW632:6.
Privacy issues can be difficult for end-users to understand and are therefore a key concern for information-sharing systems. This paper describes a deployment of fifteen Bluetooth-beacon-enabled 'creatures' spread across London's Queen Elizabeth Olympic Park, which initiate conversations on mobile phones in their vicinity via push notifications. Playing on the common assumption that neutral public settings promote anonymity, users' willingness to converse with personified chatbots is used as a proxy for understanding their inclination to share personal and potentially disclosing information. Each creature is linked to a conversational agent that asks for users' memories and their responses are then shared with other creatures in the network. This paper presents the design of an interactive device used to test users' awareness of how their information propagates to others.
Porcheron, Martin, Fischer, Joel E., McGregor, Moira, Brown, Barry, Luger, Ewa, Candello, Heloisa, O'Hara, Kenton.  2017.  Talking with Conversational Agents in Collaborative Action. Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. :431–436.

This one-day workshop intends to bring together both academics and industry practitioners to explore collaborative challenges in speech interaction. Recent improvements in speech recognition and computing power has led to conversational interfaces being introduced to many of the devices we use every day, such as smartphones, watches, and even televisions. These interfaces allow us to get things done, often by just speaking commands, relying on a reasonably well understood single-user model. While research on speech recognition is well established, the social implications of these interfaces remain underexplored, such as how we socialise, work, and play around such technologies, and how these might be better designed to support collaborative collocated talk-in-action. Moreover, the advent of new products such as the Amazon Echo and Google Home, which are positioned as supporting multi-user interaction in collocated environments such as the home, makes exploring the social and collaborative challenges around these products, a timely topic. In the workshop, we will review current practices and reflect upon prior work on studying talk-in-action and collocated interaction. We wish to begin a dialogue that takes on the renewed interest in research on spoken interaction with devices, grounded in the existing practices of the CSCW community.

Qiao, Siyi, Hu, Chengchen, Guan, Xiaohong, Zou, Jianhua.  2016.  Taming the Flow Table Overflow in OpenFlow Switch. Proceedings of the 2016 ACM SIGCOMM Conference. :591–592.

SDN has become the wide area network technology, which the academic and industry most concerned about.The limited table sizes of today’s SDN switches has turned to the most prominent short planks in the network design implementation. TCAM based flow table can provide an excellent matching performance while it really costs much. Even the flow table overflow cannot be prevented by a fixed-capacity flow table. In this paper, we design FTS(Flow Table Sharing) mechanism that can improve the performance disaster caused by overflow. We demonstrate that FTS reduces both control messages quantity and RTT time by two orders of magnitude compared to current state-of-the-art OpenFlow table-miss handler.

Noor, Nafisa, Khan, Raihan Sayeed, Muneer, Sadid, Silva, Helena.  2019.  Tamper Evidence of SEM Imaging Attack in Phase Change Memory Nanodevices. 2019 IEEE 19th International Conference on Nanotechnology (IEEE-NANO). :400–404.

Breach of security due to unauthorized access to electronic hardware devices or chips has recently become a serious concern for the internet-connected daily activities. Imaging with electron microscopy is one of the invasive techniques used to gain knowledge about a chip layout and extract secret information by the attackers. Automatic destruction or disturbance of the secret key during such invasive attacks are required to ensure protection against these attacks. We have characterized the disturbance caused to programmed phase change memory (PCM) cells by the imaging electron beam during scanning electron microscopy (SEM) in terms of the measured cell resistance. A sudden increase of resistance is observed on all imaged amorphous cells while the cells programmed to intermediate states show either abrupt increase or erratic decrease. These erratic disturbances of state are promising to mislead an attacker that is trying to acquire a stored key and leave indelible marks of tampering. Since PCM is recently being considered for implementation of various hardware security primitives, these beam-induced state change and tamper-evidence features enhance security of PCM devices against physical attacks.

Nozaki, Yusuke, Yoshikawa, Masaya.  2017.  Tamper Resistance Evaluation of PUF Implementation Against Machine Learning Attack. Proceedings of the 2017 International Conference on Biometrics Engineering and Application. :1–6.
Recently, the semiconductor counterfeiting has become a serious problem. To counter this problem, Physical Unclonable Function (PUF) has been attracted attention. However, the risk of machine learning attacks for PUF is pointed out. To verify the safety of PUF, the evaluation (tamper resistance) against machine learning attacks in the difference of PUF implementations is very important. However, the tamper resistance evaluation in the difference of PUF implementation has barely been reported. Therefore, this study evaluates the tamper resistance of PUF in the difference of field programmable gate array (FPGA) implementations against machine learning attacks. Experiments using an FPGA clarified the arbiter PUF of the lookup table implementation has the tamper resistance against machine learning attacks.
Yoshikawa, M., Nozaki, Y..  2016.  Tamper resistance evaluation of PUF in environmental variations. 2016 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS). :119–121.

The damage caused by counterfeits of semiconductors has become a serious problem. Recently, a physical unclonable function (PUF) has attracted attention as a technique to prevent counterfeiting. The present study investigates an arbiter PUF, which is a typical PUF. The vulnerability of a PUF against machine-learning attacks has been revealed. It has also been indicated that the output of a PUF is inverted from its normal output owing to the difference in environmental variations, such as the changes in power supply voltage and temperature. The resistance of a PUF against machine-learning attacks due to the difference in environmental variation has seldom been evaluated. The present study evaluated the resistance of an arbiter PUF against machine-learning attacks due to the difference in environmental variation. By performing an evaluation experiment using a simulation, the present study revealed that the resistance of an arbiter PUF against machine-learning attacks due to environmental variation was slightly improved. However, the present study also successfully predicted more than 95% of the outputs by increasing the number of learning cycles. Therefore, an arbiter PUF was revealed to be vulnerable to machine-learning attacks even after environmental variation.

Nozaki, Y., Ikezaki, Y., Yoshikawa, M..  2016.  Tamper resistance of IoT devices against electromagnnetic analysis. 2016 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK). :1–2.

Lightweight block ciphers, which are required for IoT devices, have attracted attention. Simeck, which is one of the most popular lightweight block ciphers, can be implemented on IoT devices in the smallest area. Regarding the hardware security, the threat of electromagnetic analysis has been reported. However, electromagnetic analysis of Simeck has not been reported. Therefore, this study proposes a dedicated electromagnetic analysis for a lightweight block cipher Simeck to ensure the safety of IoT devices in the future. To our knowledge, this is the first electromagnetic analysis for Simeck. Experiments using a FPGA prove the validity of the proposed method.

Khan, M. F. F., Sakamura, K..  2017.  A Tamper-Resistant Digital Token-Based Rights Management System. 2017 International Carnahan Conference on Security Technology (ICCST). :1–6.

Use of digital token - which certifies the bearer's rights to some kind of products or services - is quite common nowadays for its convenience, ease of use and cost-effectiveness. Many of such digital tokens, however, are produced with software alone, making them vulnerable to forgery, including alteration and duplication. For a more secure safeguard for both token owner's right and service provider's accountability, digital tokens should be tamper-resistant as much as possible in order for them to withstand physical attacks as well. In this paper, we present a rights management system that leverages tamper-resistant digital tokens created by hardware-software collaboration in our eTRON architecture. The system features the complete life cycle of a digital token from generation to storage and redemption. Additionally, it provides a secure mechanism for transfer of rights in a peer-to-peer manner over the Internet. The proposed system specifies protocols for permissible manipulation on digital tokens, and subsequently provides a set of APIs for seamless application development. Access privileges to the tokens are strictly defined and state-of-the-art asymmetric cryptography is used for ensuring their confidentiality. Apart from the digital tokens being physically tamper-resistant, the protocols involved in the system are proven to be secure against attacks. Furthermore, an authentication mechanism is implemented that invariably precedes any operation involving the digital token in question. The proposed system presents clear security gains compared to existing systems that do not take tamper-resistance into account, and schemes that use symmetric key cryptography.

Luh, Robert, Schrittwieser, Sebastian, Marschalek, Stefan.  2016.  TAON: An Ontology-based Approach to Mitigating Targeted Attacks. Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services. :303–312.

Targeted attacks on IT systems are a rising threat against the confidentiality of sensitive data and the availability of systems and infrastructures. Planning for the eventuality of a data breach or sabotage attack has become an increasingly difficult task with the emergence of advanced persistent threats (APTs), a class of highly sophisticated cyber-attacks that are nigh impossible to detect using conventional signature-based systems. Understanding, interpreting, and correlating the particulars of such advanced targeted attacks is a major research challenge that needs to be tackled before behavior-based approaches can evolve from their current state to truly semantics-aware solutions. Ontologies offer a versatile foundation well suited for depicting the complex connections between such behavioral data and the diverse technical and organizational properties of an IT system. In order to facilitate the development of novel behavior-based detection systems, we present TAON, an OWL-based ontology offering a holistic view on actors, assets, and threat details, which are mapped to individual abstracted events and anomalies that can be detected by today's monitoring data providers. TOAN offers a straightforward means to plan an organization's defense against APTs and helps to understand how, why, and by whom certain resources are targeted. Populated by concrete data, the proposed ontology becomes a smart correlation framework able to combine several data sources into a semantic assessment of any targeted attack.

W. Ketpan, S. Phonsri, R. Qian, M. Sellathurai.  2015.  "On the Target Detection in OFDM Passive Radar Using MUSIC and Compressive Sensing". 2015 Sensor Signal Processing for Defence (SSPD). :1-5.

The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.

Murdock, Austin, Li, Frank, Bramsen, Paul, Durumeric, Zakir, Paxson, Vern.  2017.  Target Generation for Internet-wide IPv6 Scanning. Proceedings of the 2017 Internet Measurement Conference. :242–253.
Fast IPv4 scanning has enabled researchers to answer a wealth of new security and measurement questions. However, while increased network speeds and computational power have enabled comprehensive scans of the IPv4 address space, a brute-force approach does not scale to IPv6. Systems are limited to scanning a small fraction of the IPv6 address space and require an algorithmic approach to determine a small set of candidate addresses to probe. In this paper, we first explore the considerations that guide designing such algorithms. We introduce a new approach that identifies dense address space regions from a set of known "seed" addresses and generates a set of candidates to scan. We compare our algorithm 6Gen against Entropy/IP—the current state of the art—finding that we can recover between 1–8 times as many addresses for the five candidate datasets considered in the prior work. However, during our analysis, we uncover widespread IP aliasing in IPv6 networks. We discuss its effect on target generation and explore preliminary approaches for detecting aliased regions.
Brust, M. R., Zurad, M., Hentges, L., Gomes, L., Danoy, G., Bouvry, P..  2017.  Target Tracking Optimization of UAV Swarms Based on Dual-Pheromone Clustering. 2017 3rd IEEE International Conference on Cybernetics (CYBCONF). :1–8.

Unmanned Aerial Vehicles (UAVs) are autonomous aircraft that, when equipped with wireless communication interfaces, can share data among themselves when in communication range. Compared to single UAVs, using multiple UAVs as a collaborative swarm is considerably more effective for target tracking, reconnaissance, and surveillance missions because of their capacity to tackle complex problems synergistically. Success rates in target detection and tracking depend on map coverage performance, which in turn relies on network connectivity between UAVs to propagate surveillance results to avoid revisiting already observed areas. In this paper, we consider the problem of optimizing three objectives for a swarm of UAVs: (a) target detection and tracking, (b) map coverage, and (c) network connectivity. Our approach, Dual-Pheromone Clustering Hybrid Approach (DPCHA), incorporates a multi-hop clustering and a dual-pheromone ant-colony model to optimize these three objectives. Clustering keeps stable overlay networks, while attractive and repulsive pheromones mark areas of detected targets and visited areas. Additionally, DPCHA introduces a disappearing target model for dealing with temporarily invisible targets. Extensive simulations show that DPCHA produces significant improvements in the assessment of coverage fairness, cluster stability, and connection volatility. We compared our approach with a pure dual- pheromone approach and a no-base model, which removes the base station from the model. Results show an approximately 50% improvement in map coverage compared to the pure dual-pheromone approach.

Hu, Rui, Guo, Yuanxiong, Pan, Miao, Gong, Yanmin.  2019.  Targeted Poisoning Attacks on Social Recommender Systems. 2019 IEEE Global Communications Conference (GLOBECOM). :1–6.
With the popularity of online social networks, social recommendations that rely on one’s social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.
Peter Dinges, University of Illinois at Urbana-Champaign, Gul Agha, University of Illinois at Urbana-Champaign.  2014.  Targeted Test Input Generation using Symbolic-concrete Backward Execution. 29th IEEE/ACM International Conference on Automated Software Engineering (ASE 2014).

Knowing inputs that cover a specific branch or statement in a program is useful for debugging and regression testing. Symbolic backward execution (SBE) is a natural approach to find such targeted inputs. However, SBE struggles with complicated arithmetic, external method calls, and data- dependent loops that occur in many real-world programs. We propose symcretic execution, a novel combination of SBE and concrete forward execution that can efficiently find targeted inputs despite these challenges. An evaluation of our approach on a range of test cases shows that symcretic execution finds inputs in more cases than concolic testing tools while exploring fewer path segments. Integration of our approach will allow test generation tools to fill coverage gaps and static bug detectors to verify candidate bugs with concrete test cases.

Peter Dinges, University of Illinois at Urbana-Champaign, Gul Agha, University of Illinois at Urbana-Champaign.  2014.  Targeted Test Input Generation Using Symbolic-Concrete Backward Execution.

Knowing inputs that cover a specific branch or statement in a program is useful for debugging and regression testing. Symbolic backward execution (SBE) is a natural approach to find such targeted inputs. However, SBE struggles with complicated arithmetic, external method calls, and data-dependent loops that occur in many real-world programs. We propose symcretic execution, a novel combination of SBE and concrete forward execution that can efficiently find targeted inputs despite these challenges. An evaluation of our approach on a range of test cases shows that symcretic execution finds inputs in more cases than concolic testing tools while exploring fewer path segments. Integration of our approach will allow test generation tools to fill coverage gaps and static bug detectors to verify candidate bugs with concrete test cases. This is the full version of an extended abstract that was presented at the 29th IEEE/ACM International Conference on Automated Software Engineering (ASE 2014), September 15–19, 2014, Västerås, Sweden.

Yu, L., Wang, Q., Barrineau, G., Oakley, J., Brooks, R. R., Wang, K. C..  2017.  TARN: A SDN-based traffic analysis resistant network architecture. 2017 12th International Conference on Malicious and Unwanted Software (MALWARE). :91–98.
Destination IP prefix-based routing protocols are core to Internet routing today. Internet autonomous systems (AS) possess fixed IP prefixes, while packets carry the intended destination AS's prefix in their headers, in clear text. As a result, network communications can be easily identified using IP addresses and become targets of a wide variety of attacks, such as DNS/IP filtering, distributed Denial-of-Service (DDoS) attacks, man-in-the-middle (MITM) attacks, etc. In this work, we explore an alternative network architecture that fundamentally removes such vulnerabilities by disassociating the relationship between IP prefixes and destination networks, and by allowing any end-to-end communication session to have dynamic, short-lived, and pseudo-random IP addresses drawn from a range of IP prefixes rather than one. The concept is seemingly impossible to realize in todays Internet. We demonstrate how this is doable today with three different strategies using software defined networking (SDN), and how this can be done at scale to transform the Internet addressing and routing paradigms with the novel concept of a distributed software defined Internet exchange (SDX). The solution works with both IPv4 and IPv6, whereas the latter provides higher degrees of IP addressing freedom. Prototypes based on Open vSwitches (OVS) have been implemented for experimentation across the PEERING BGP testbed. The SDX solution not only provides a technically sustainable pathway towards large-scale traffic analysis resistant network (TARN) support, it also unveils a new business model for customer-driven, customizable and trustable end-to-end network services.
Hayes, Jamie, Troncoso, Carmela, Danezis, George.  2016.  TASP: Towards Anonymity Sets That Persist. Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society. :177–180.

Anonymous communication systems are vulnerable to long term passive "intersection attacks". Not all users of an anonymous communication system will be online at the same time, this leaks some information about who is talking to who. A global passive adversary observing all communications can learn the set of potential recipients of a message with more and more confidence over time. Nearly all deployed anonymous communication tools offer no protection against such attacks. In this work, we introduce TASP, a protocol used by an anonymous communication system that mitigates intersection attacks by intelligently grouping clients together into anonymity sets. We find that with a bandwidth overhead of just 8% we can dramatically extend the time necessary to perform a successful intersection attack.