Visible to the public Biblio

Found 1408 results

Filters: First Letter Of Title is C  [Clear All Filters]
A B [C] D E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
C
Suksomboon, Kalika, Shen, Zhishu, Ueda, Kazuaki, Tagami, Atsushi.  2019.  C2P2: Content-Centric Privacy Platform for Privacy-Preserving Monitoring Services. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:252–261.
Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.
Giechaskiel, I., Rasmussen, K. B., Szefer, J..  2020.  C3APSULe: Cross-FPGA Covert-Channel Attacks through Power Supply Unit Leakage. 2020 IEEE Symposium on Security and Privacy (SP). :1728—1741.
Field-Programmable Gate Arrays (FPGAs) are versatile, reconfigurable integrated circuits that can be used as hardware accelerators to process highly-sensitive data. Leaking this data and associated cryptographic keys, however, can undermine a system's security. To prevent potentially unintentional interactions that could break separation of privilege between different data center tenants, FPGAs in cloud environments are currently dedicated on a per-user basis. Nevertheless, while the FPGAs themselves are not shared among different users, other parts of the data center infrastructure are. This paper specifically shows for the first time that powering FPGAs, CPUs, and GPUs through the same power supply unit (PSU) can be exploited in FPGA-to-FPGA, CPU-to-FPGA, and GPU-to-FPGA covert channels between independent boards. These covert channels can operate remotely, without the need for physical access to, or modifications of, the boards. To demonstrate the attacks, this paper uses a novel combination of "sensing" and "stressing" ring oscillators as receivers on the sink FPGA. Further, ring oscillators are used as transmitters on the source FPGA. The transmitting and receiving circuits are used to determine the presence of the leakage on off-the-shelf Xilinx boards containing Artix 7 and Kintex 7 FPGA chips. Experiments are conducted with PSUs by two vendors, as well as CPUs and GPUs of different generations. Moreover, different sizes and types of ring oscillators are also tested. In addition, this work discusses potential countermeasures to mitigate the impact of the cross-board leakage. The results of this paper highlight the dangers of shared power supply units in local and cloud FPGAs, and therefore a fundamental need to re-think FPGA security for shared infrastructures.
Figueira, Nina, Pochmann, Pablo, Oliveira, Abel, de Freitas, Edison Pignaton.  2022.  A C4ISR Application on the Swarm Drones Context in a Low Infrastructure Scenario. 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET). :1—7.
The military operations in low communications infrastructure scenarios employ flexible solutions to optimize the data processing cycle using situational awareness systems, guaranteeing interoperability and assisting in all processes of decision-making. This paper presents an architecture for the integration of Command, Control, Computing, Communication, Intelligence, Surveillance and Reconnaissance Systems (C4ISR), developed within the scope of the Brazilian Ministry of Defense, in the context of operations with Unmanned Aerial Vehicles (UAV) - swarm drones - and the Internet-to-the-battlefield (IoBT) concept. This solution comprises the following intelligent subsystems embedded in UAV: STFANET, an SDN-Based Topology Management for Flying Ad Hoc Network focusing drone swarms operations, developed by University of Rio Grande do Sul; Interoperability of Command and Control (INTERC2), an intelligent communication middleware developed by Brazilian Navy; A Mission-Oriented Sensors Array (MOSA), which provides the automatization of data acquisition, data fusion, and data sharing, developed by Brazilian Army; The In-Flight Awareness Augmentation System (IFA2S), which was developed to increase the safety navigation of Unmanned Aerial Vehicles (UAV), developed by Brazilian Air Force; Data Mining Techniques to optimize the MOSA with data patterns; and an adaptive-collaborative system, composed of a Software Defined Radio (SDR), to solve the identification of electromagnetic signals and a Geographical Information System (GIS) to organize the information processed. This research proposes, as a main contribution in this conceptual phase, an application that describes the premises for increasing the capacity of sensing threats in the low structured zones, such as the Amazon rainforest, using existing communications solutions of Brazilian defense monitoring systems.
Phu, T. N., Hoang, L., Toan, N. N., Tho, N. Dai, Binh, N. N..  2019.  C500-CFG: A Novel Algorithm to Extract Control Flow-based Features for IoT Malware Detection. 2019 19th International Symposium on Communications and Information Technologies (ISCIT). :568—573.

{Static characteristic extraction method Control flow-based features proposed by Ding has the ability to detect malicious code with higher accuracy than traditional Text-based methods. However, this method resolved NP-hard problem in a graph, therefore it is not feasible with the large-size and high-complexity programs. So, we propose the C500-CFG algorithm in Control flow-based features based on the idea of dynamic programming, solving Ding's NP-hard problem in O(N2) time complexity, where N is the number of basic blocks in decom-piled executable codes. Our algorithm is more efficient and more outstanding in detecting malware than Ding's algorithm: fast processing time, allowing processing large files, using less memory and extracting more feature information. Applying our algorithms with IoT data sets gives outstanding results on 2 measures: Accuracy = 99.34%

Sepulveda, J., Zankl, A., Mischke, O..  2017.  Cache attacks and countermeasures for NTRUEncrypt on MPSoCs: Post-quantum resistance for the IoT. 2017 30th IEEE International System-on-Chip Conference (SOCC). :120–125.

Public-key cryptography (PKC), widely used to protect communication in the Internet of Things (IoT), is the basis for establishing secured communication channels between multiple parties. The foreseeable breakthrough of quantum computers represents a risk for many PKC ecosystems. Almost all approaches in use today rely on the hardness of factoring large integers or computing (elliptic-curve) discrete logarithms. It is known that cryptography based on these problems can be broken in polynomial time by Shors algorithm, once a large enough quantum computer is built. In order to prepare for such an event, the integration of quantum-resistant cryptography on devices operating in the IoT is mandatory to achieve long-term security. Due to their limited resources, tight performance requirements and long-term life-cycles, this is especially challenging for Multi-Processor System-on-Chips (MPSoCs) operating in this context. At the same time, it must be provided that well-known implementation attacks, such as those targeting a cipher's execution time or its use of the processor cache, are inhibited, as they've successfully been used to attack cryptosystems in the pre-quantum era. Hence, this work presents an analysis of the security-critical polynomial multiplication routine within the NTRU algorithm and its susceptibility to timing and cache attacks. We also propose two different countermeasures to harden systems with or without caches against said attacks, and include the evaluation of the respective overheads. We demonstrate that security against timing and cache attacks can be achieved with reasonable overheads depending on the chosen parameters of NTRU.

Tong, Zhongkai, Zhu, Ziyuan, Wang, Zhanpeng, Wang, Limin, Zhang, Yusha, Liu, Yuxin.  2020.  Cache side-channel attacks detection based on machine learning. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :919—926.
Security has always been one of the main concerns in the field of computer architecture and cloud computing. Cache-based side-channel attacks pose a threat to almost all existing architectures and cloud computing. Especially in the public cloud, the cache is shared among multiple tenants, and cache attacks can make good use of this to extract information. Cache side-channel attacks are a problem to be solved for security, in which how to accurately detect cache side-channel attacks has been a research hotspot. Because the cache side-channel attack does not require the attacker to physically contact the target device and does not need additional devices to obtain the side channel information, the cache-side channel attack is efficient and hidden, which poses a great threat to the security of cryptographic algorithms. Based on the AES algorithm, this paper uses hardware performance counters to obtain the features of different cache events under Flush + Reload, Prime + Probe, and Flush + Flush attacks. Firstly, the random forest algorithm is used to filter the cache features, and then the support vector machine algorithm is used to model the system. Finally, high detection accuracy is achieved under different system loads. The detection accuracy of the system is 99.92% when there is no load, the detection accuracy is 99.85% under the average load, and the detection accuracy under full load is 96.57%.
Bender, Michael A., Demaine, Erik D., Ebrahimi, Roozbeh, Fineman, Jeremy T., Johnson, Rob, Lincoln, Andrea, Lynch, Jayson, McCauley, Samuel.  2016.  Cache-Adaptive Analysis. Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures. :135–144.
Memory efficiency and locality have substantial impact on the performance of programs, particularly when operating on large data sets. Thus, memory- or I/O-efficient algorithms have received significant attention both in theory and practice. The widespread deployment of multicore machines, however, brings new challenges. Specifically, since the memory (RAM) is shared across multiple processes, the effective memory-size allocated to each process fluctuates over time. This paper presents techniques for designing and analyzing algorithms in a cache-adaptive setting, where the RAM available to the algorithm changes over time. These techniques make analyzing algorithms in the cache-adaptive model almost as easy as in the external memory, or DAM model. Our techniques enable us to analyze a wide variety of algorithms — Master-Method-style algorithms, Akra-Bazzi-style algorithms, collections of mutually recursive algorithms, and algorithms, such as FFT, that break problems of size N into subproblems of size Theta(Nc). We demonstrate the effectiveness of these techniques by deriving several results: 1. We give a simple recipe for determining whether common divide-and-conquer cache-oblivious algorithms are optimally cache adaptive. 2. We show how to bound an algorithm's non-optimality. We give a tight analysis showing that a class of cache-oblivious algorithms is a logarithmic factor worse than optimal. 3. We show the generality of our techniques by analyzing the cache-oblivious FFT algorithm, which is not covered by the above theorems. Nonetheless, the same general techniques can show that it is at most O(loglog N) away from optimal in the cache adaptive setting, and that this bound is tight. These general theorems give concrete results about several algorithms that could not be analyzed using earlier techniques. For example, our results apply to Fast Fourier Transform, matrix multiplication, Jacobi Multipass Filter, and cache-oblivious dynamic-programming algorithms, such as Longest Common Subsequence and Edit Distance. Our results also give algorithm designers clear guidelines for creating optimally cache-adaptive algorithms.
Gulmezoglu, Berk, Eisenbarth, Thomas, Sunar, Berk.  2017.  Cache-Based Application Detection in the Cloud Using Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :288–300.

Cross-VM attacks have emerged as a major threat on commercial clouds. These attacks commonly exploit hardware level leakages on shared physical servers. A co-located machine can readily feel the presence of a co-located instance with a heavy computational load through performance degradation due to contention on shared resources. Shared cache architectures such as the last level cache (LLC) have become a popular leakage source to mount cross-VM attack. By exploiting LLC leakages, researchers have already shown that it is possible to recover fine grain information such as cryptographic keys from popular software libraries. This makes it essential to verify implementations that handle sensitive data across the many versions and numerous target platforms, a task too complicated, error prone and costly to be handled by human beings. Here we propose a machine learning based technique to classify applications according to their cache access profiles. We show that with minimal and simple manual processing steps feature vectors can be used to train models using support vector machines to classify the applications with a high degree of success. The profiling and training steps are completely automated and do not require any inspection or study of the code to be classified. In native execution, we achieve a successful classification rate as high as 98% (L1 cache) and 78$\backslash$% (LLC) over 40 benchmark applications in the Phoronix suite with mild training. In the cross-VM setting on the noisy Amazon EC2 the success rate drops to 60$\backslash$% for a suite of 25 applications. With this initial study we demonstrate that it is possible to train meaningful models to successfully predict applications running in co-located instances.

Kim, Taewoo, Thirumaraiselvan, Vidhyasagar, Jia, Jianfeng, Li, Chen.  2017.  Caching Geospatial Objects in Web Browsers. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. :92:1–92:4.

Map-based services are becoming increasingly important in many applications. These services often need to show geospatial objects (e.g., cities and parks) in Web browsers, and being able to retrieve such objects efficiently is critical to achieving a low response time for user queries. In this demonstration we present a browser-based caching technique to store and load geospatial objects on a map in a Web page. The technique employs a hierarchical structure to store and index polygons, and does intelligent prefetching and cache replacement by utilizing the information about the user's recent browser activities. We demonstrate the usage of the technique in an application called TwitterMap for visualizing more than 1 billion tweets in real time. We show its effectiveness by using different replacement policies. The technique is implemented as a general-purpose Javascript library, making it suitable for other applications as well.

Shvidkiy, A. A., Savelieva, A. A., Zarubin, A. A..  2021.  Caching Methods Analysis for Improving Distributed Storage Systems Performance. 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO. :1—5.
The object of the research is distributed software-defined storage systems, as well as methods of caching disk devices. It is important for improving the performance of storage systems, which is relevant in modern conditions. In this article, an assessment of the possibility of improving performance through the use of various caching methods is made, as well as experimental research and analysis of the results obtained. The parameters of the application's operation with the disk subsystem have been determined. The results of experiments are presented - testing was carried out on a deployed architecture of a distributed storage with two types of caching, the results are combined in graphs. Conclusions are drawn, including on the prospects for further research.
Melati, Seshariana Rahma, Yovita, Leanna Vidya, Mayasari, Ratna.  2021.  Caching Performance of Named Data Networking with NDNS. 2021 International Conference on Information Networking (ICOIN). :261–266.
Named Data Networking, a future internet network architecture design that can change the network's perspective from previously host-centric to data-centric. It can reduce the network load, especially on the server part, and can provide advantages in multicast cases or re-sending of content data to users due to transmission errors. In NDN, interest messages are sent to the router, and if they are not immediately found, they will continue to be forwarded, resulting in a large load. NDNS or a DNS-Like Name Service for NDN is needed to know exactly where the content is to improve system performance. NDNS is a database that provides information about the zone location of the data contained in the network. In this study, a simulation was conducted to test the NDNS mechanism on the NDN network to support caching on the NDN network by testing various topologies with changes in the size of the content store and the number of nodes used. NDNS is outperform compared to NDN without NDNS for cache hit ratio and load parameters.
Harrison, Willie K., Shoushtari, Morteza.  2021.  On Caching with Finite Blocklength Coding for Secrecy over the Binary Erasure Wiretap Channel. 2021 Wireless Telecommunications Symposium (WTS). :1–6.
In this paper, we show that caching can aid in achieving secure communications by considering a wiretap scenario where the transmitter and legitimate receiver share access to a secure cache, and an eavesdropper is able to tap transmissions over a binary erasure wiretap channel during the delivery phase of a caching protocol. The scenario under consideration gives rise to a new channel model for wiretap coding that allows the transmitter to effectively choose a subset of bits to erase at the eavesdropper by caching the bits ahead of time. The eavesdropper observes the remainder of the coded bits through the wiretap channel for the general case. In the wiretap type-II scenario, the eavesdropper is able to choose a set of revealed bits only from the subset of bits not cached. We present a coding approach that allows efficient use of the cache to realize a caching gain in the network, and show how to use the cache to optimize the information theoretic security in the choice of a finite blocklength code and the choice of the cached bit set. To our knowledge, this is the first work on explicit algorithms for secrecy coding in any type of caching network.
M. Moradi, F. Qian, Q. Xu, Z. M. Mao, D. Bethea, M. K. Reiter.  2015.  "Caesar: high-speed and memory-efficient forwarding engine for future internet architecture". 2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS). :171-182.

In response to the critical challenges of the current Internet architecture and its protocols, a set of so-called clean slate designs has been proposed. Common among them is an addressing scheme that separates location and identity with self-certifying, flat and non-aggregatable address components. Each component is long, reaching a few kilobits, and would consume an amount of fast memory in data plane devices (e.g., routers) that is far beyond existing capacities. To address this challenge, we present Caesar, a high-speed and length-agnostic forwarding engine for future border routers, performing most of the lookups within three fast memory accesses. To compress forwarding states, Caesar constructs scalable and reliable Bloom filters in Ternary Content Addressable Memory (TCAM). To guarantee correctness, Caesar detects false positives at high speed and develops a blacklisting approach to handling them. In addition, we optimize our design by introducing a hashing scheme that reduces the number of hash computations from k to log(k) per lookup based on hash coding theory. We handle routing updates while keeping filters highly utilized in address removals. We perform extensive analysis and simulations using real traffic and routing traces to demonstrate the benefits of our design. Our evaluation shows that Caesar is more energy-efficient and less expensive (in terms of total cost) compared to optimized IPv6 TCAM-based solutions by up to 67% and 43% respectively. In addition, the total cost of our design is approximately the same for various address lengths.

Singi, Kapil, Kaulgud, Vikrant, Bose, R.P. Jagadeesh Chandra, Podder, Sanjay.  2019.  CAG: Compliance Adherence and Governance in Software Delivery Using Blockchain. 2019 IEEE/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB). :32—39.

The software development life cycle (SDLC) starts with business and functional specifications signed with a client. In addition to this, the specifications also capture policy / procedure / contractual / regulatory / legislation / standard compliances with respect to a given client industry. The SDLC must adhere to service level agreements (SLAs) while being compliant to development activities, processes, tools, frameworks, and reuse of open-source software components. In today's world, global software development happens across geographically distributed (autonomous) teams consuming extraordinary amounts of open source components drawn from a variety of disparate sources. Although this is helping organizations deal with technical and economic challenges, it is also increasing unintended risks, e.g., use of a non-complaint license software might lead to copyright issues and litigations, use of a library with vulnerabilities pose security risks etc. Mitigation of such risks and remedial measures is a challenge due to lack of visibility and transparency of activities across these distributed teams as they mostly operate in silos. We believe a unified model that non-invasively monitors and analyzes the activities of distributed teams will help a long way in building software that adhere to various compliances. In this paper, we propose a decentralized CAG - Compliance Adherence and Governance framework using blockchain technologies. Our framework (i) enables the capturing of required data points based on compliance specifications, (ii) analyzes the events for non-conformant behavior through smart contracts, (iii) provides real-time alerts, and (iv) records and maintains an immutable audit trail of various activities.

Xiao-Bing Hu, Ming Wang, Leeson, M.S..  2014.  Calculating the complete pareto front for a special class of continuous multi-objective optimization problems. Evolutionary Computation (CEC), 2014 IEEE Congress on. :290-297.

Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.

Burnashev, I..  2021.  Calculation of Risk Parameters of Threats for Protected Information System. 2021 International Russian Automation Conference (RusAutoCon). :89–93.
A real or potential threat to various large and small security objects, which comes from both internal and external attackers, determines one or another activities to ensure internal and external security. These actions depend on the spheres of life of state and society, which are targeted by the security threats. These threats can be conveniently classified into political threats (or threats to the existing constitutional order), economic, military, informational, technogenic, environmental, corporate, and other threats. The article discusses a model of an information system, which main criterion is the system security based on the concept of risk. When considering the model, it was determined that it possess multi-criteria aspects. Therefore the establishing the quantitative and qualitative characteristics is a complex and dynamic task. The paper proposes to use the mathematical apparatus of the teletraffic theory in one of the elements of the protected system, namely, in the end-to-end security subsystem.
Jia, Kaige, Liu, Zheyu, Wei, Qi, Qiao, Fei, Liu, Xinjun, Yang, Yi, Fan, Hua, Yang, Huazhong.  2018.  Calibrating Process Variation at System Level with In-Situ Low-Precision Transfer Learning for Analog Neural Network Processors. Proceedings of the 55th Annual Design Automation Conference. :12:1–12:6.

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN) processors, and make it hard to be scaled down, as well as feasibility degrading. This paper first analyses the impact of PV on the performance of ANN chips. Then proposes an in-situ transfer learning method at system level to reduce PV's influence with low-precision back-propagation. Simulation results show the proposed method could increase 50% tolerance of operating point drift and 70% $\sim$ 100% tolerance of mismatch with less than 1% accuracy loss of benchmarks. It also reduces 66.7% memories and has about 50× energy-efficiency improvement of multiplication in the learning stage, compared with the conventional full-precision (32bit float) training system.

Wang, Kai, Zhang, Yuqing, Liu, Peng.  2016.  Call Me Back!: Attacks on System Server and System Apps in Android Through Synchronous Callback. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :92–103.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.

Zhang, Zhikun, Wang, Tianhao, Li, Ninghui, He, Shibo, Chen, Jiming.  2018.  CALM: Consistent Adaptive Local Marginal for Marginal Release Under Local Differential Privacy. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :212–229.
Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.
Srivastava, Animesh, Jain, Puneet, Demetriou, Soteris, Cox, Landon P., Kim, Kyu-Han.  2017.  CamForensics: Understanding Visual Privacy Leaks in the Wild. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. :30:1–30:13.

Many mobile apps, including augmented-reality games, bar-code readers, and document scanners, digitize information from the physical world by applying computer-vision algorithms to live camera data. However, because camera permissions for existing mobile operating systems are coarse (i.e., an app may access a camera's entire view or none of it), users are vulnerable to visual privacy leaks. An app violates visual privacy if it extracts information from camera data in unexpected ways. For example, a user might be surprised to find that an augmented-reality makeup app extracts text from the camera's view in addition to detecting faces. This paper presents results from the first large-scale study of visual privacy leaks in the wild. We build CamForensics to identify the kind of information that apps extract from camera data. Our extensive user surveys determine what kind of information users expected an app to extract. Finally, our results show that camera apps frequently defy users' expectations based on their descriptions.

Zhuo Lu, Wenye Wang, Wang, C..  2015.  Camouflage Traffic: Minimizing Message Delay for Smart Grid Applications under Jamming. Dependable and Secure Computing, IEEE Transactions on. 12:31-44.

Smart grid is a cyber-physical system that integrates power infrastructures with information technologies. To facilitate efficient information exchange, wireless networks have been proposed to be widely used in the smart grid. However, the jamming attack that constantly broadcasts radio interference is a primary security threat to prevent the deployment of wireless networks in the smart grid. Hence, spread spectrum systems, which provide jamming resilience via multiple frequency and code channels, must be adapted to the smart grid for secure wireless communications, while at the same time providing latency guarantee for control messages. An open question is how to minimize message delay for timely smart grid communication under any potential jamming attack. To address this issue, we provide a paradigm shift from the case-by-case methodology, which is widely used in existing works to investigate well-adopted attack models, to the worst-case methodology, which offers delay performance guarantee for smart grid applications under any attack. We first define a generic jamming process that characterizes a wide range of existing attack models. Then, we show that in all strategies under the generic process, the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of traffic can in fact improve the worst-case delay performance. As a result, we demonstrate a lightweight yet promising system, transmitting adaptive camouflage traffic (TACT), to combat jamming attacks. TACT minimizes the message delay by generating extra traffic called camouflage to balance the network load at the optimum. Experiments show that TACT can decrease the probability that a message is not delivered on time in order of magnitude.

Kumar, Sachin, Gupta, Garima, Prasad, Ranjitha, Chatterjee, Arnab, Vig, Lovekesh, Shroff, Gautam.  2020.  CAMTA: Causal Attention Model for Multi-touch Attribution. 2020 International Conference on Data Mining Workshops (ICDMW). :79–86.
Advertising channels have evolved from conventional print media, billboards and radio-advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a causal attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict per-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real-world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modeling on the predicted channel attribution.
Anwar, Z., Malik, A.W..  2014.  Can a DDoS Attack Meltdown My Data Center? A Simulation Study and Defense Strategies Communications Letters, IEEE. 18:1175-1178.

The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud provider's worst nightmare: meltdown of the data center as a result of a DDoS attack.

Alan, Hasan Faik, Kaur, Jasleen.  2016.  Can Android Applications Be Identified Using Only TCP/IP Headers of Their Launch Time Traffic? Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks. :61–66.

The ability to identify mobile apps in network traffic has significant implications in many domains, including traffic management, malware detection, and maintaining user privacy. App identification methods in the literature typically use deep packet inspection (DPI) and analyze HTTP headers to extract app fingerprints. However, these methods cannot be used if HTTP traffic is encrypted. We investigate whether Android apps can be identified from their launch-time network traffic using only TCP/IP headers. We first capture network traffic of 86,109 app launches by repeatedly running 1,595 apps on 4 distinct Android devices. We then use supervised learning methods used previously in the web page identification literature, to identify the apps that generated the traffic. We find that: (i) popular Android apps can be identified with 88% accuracy, by using the packet sizes of the first 64 packets they generate, when the learning methods are trained and tested on the data collected from same device; (ii) when the data from an unseen device (but similar operating system/vendor) is used for testing, the apps can be identified with 67% accuracy; (iii) the app identification accuracy does not drop significantly even if the training data are stale by several days, and (iv) the accuracy does drop quite significantly if the operating system/vendor is very different. We discuss the implications of our findings as well as open issues.

Zhu, Lu, Wei, Yehua, Jiang, Haoran, Long, Jing.  2022.  CAN FD Message Authentication Enhances Parallel in-vehicle Applications Security. 2022 2nd International Conference on Intelligent Technology and Embedded Systems (ICITES). :155–160.
Controller Area Network with Flexible Data-rate(CAN FD) has the advantages of high bandwidth and data field length to meet the higher communication requirements of parallel in-vehicle applications. If the CAN FD lacking the authentication security mechanism is used, it is easy to make it suffer from masquerade attack. Therefore, a two-stage method based on message authentication is proposed to enhance the security of it. In the first stage, an anti-exhaustive message exchange and comparison algorithm is proposed. After exchanging the message comparison sequence, the lower bound of the vehicle application and redundant message space is obtained. In the second stage, an enhanced round accumulation algorithm is proposed to enhance security, which adds Message Authentication Codes(MACs) to the redundant message space in a way of fewer accumulation rounds. Experimental examples show that the proposed two-stage approach enables both small-scale and large-scale parallel in-vehicle applications security to be enhanced. Among them, in the Adaptive Cruise Control Application(ACCA), when the laxity interval is 1300μs, the total increased MACs is as high as 388Bit, and the accumulation rounds is as low as 40 rounds.