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Kapusta, K., Memmi, G., Noura, H..  2017.  Secure and resilient scheme for data protection in unattended wireless sensor networks. 2017 1st Cyber Security in Networking Conference (CSNet). :1–8.

Unattended Wireless Sensor Networks (UWSN) are usually deployed in human-hostile environments. Such architectures raise a challenge to data protection for two main reasons. First, sensors have limited capacities in terms of performance and memory, so not all cryptographic mechanisms can be applied. Moreover, the measurements cannot be immediately gathered, so they have to be kept inside the devices until a mobile sink comes to collect them. This paper introduces a new method for secure and resilient data protection inside UWSN. It is based on a lightweight fragmentation scheme that transforms data collected by a sensor into multiple secure fragments that are distributed over sensor's neighboring nodes in a way that only a certain amount of these fragments is required for data recovery. Moreover, data security is reinforced by the use of a dynamic key refreshed after each visit of the mobile sink. Authentication and integrity information are dispersed within the fragments to protected data from active attacks. Homomorphic properties of the algorithm allow to significantly reduce storage space inside the nodes. Performance and empirical security evaluation results show that the proposed scheme achieves a good trade-off between performance, data protection and memory occupation.

Kar, Diptendu Mohan, Ray, Indrajit, Gallegos, Jenna, Peccoud, Jean.  2018.  Digital Signatures to Ensure the Authenticity and Integrity of Synthetic DNA Molecules. Proceedings of the New Security Paradigms Workshop. :110–122.

DNA synthesis has become increasingly common, and many synthetic DNA molecules are licensed intellectual property (IP). DNA samples are shared between academic labs, ordered from DNA synthesis companies and manipulated for a variety of different purposes, mostly to study their properties and improve upon them. However, it is not uncommon for a sample to change hands many times with very little accompanying information and no proof of origin. This poses significant challenges to the original inventor of a DNA molecule, trying to protect her IP rights. More importantly, following the anthrax attacks of 2001, there is an increased urgency to employ microbial forensic technologies to trace and track agent inventories. However, attribution of physical samples is next to impossible with existing technologies. In this paper, we describe our efforts to solve this problem by embedding digital signatures in DNA molecules synthesized in the laboratory. We encounter several challenges that we do not face in the digital world. These challenges arise primarily from the fact that changes to a physical DNA molecule can affect its properties, random mutations can accumulate in DNA samples over time, DNA sequencers can sequence (read) DNA erroneously and DNA sequencing is still relatively expensive (which means that laboratories would prefer not to read and re-read their DNA samples to get error-free sequences). We address these challenges and present a digital signature technology that can be applied to synthetic DNA molecules in living cells.

Kar, Monodeep, Singh, Arvind, Mathew, Sanu, Rajan, Anand, De, Vivek, Mukhopadhyay, Saibal.  2016.  Exploiting Fully Integrated Inductive Voltage Regulators to Improve Side Channel Resistance of Encryption Engines. Proceedings of the 2016 International Symposium on Low Power Electronics and Design. :130–135.

This paper explores fully integrated inductive voltage regulators (FIVR) as a technique to improve the side channel resistance of encryption engines. We propose security aware design modes for low passive FIVR to improve robustness of an encryption-engine against statistical power attacks in time and frequency domain. A Correlation Power Analysis is used to attack a 128-bit AES engine synthesized in 130nm CMOS. The original design requires \textasciitilde250 Measurements to Disclose (MTD) the 1st byte of key; but with security-aware FIVR, the CPA was unsuccessful even after 20,000 traces. We present a reversibility based threat model for the FIVR-based protection improvement and show the robustness of security aware FIVR against such threat.

Kar, N., Aman, M. A. A. A., Mandal, K., Bhattacharya, B..  2017.  Chaos-based video steganography. 2017 8th International Conference on Information Technology (ICIT). :482–487.

In this paper a novel data hiding method has been proposed which is based on Non-Linear Feedback Shift Register and Tinkerbell 2D chaotic map. So far, the major work in Steganography using chaotic map has been confined to image steganography where significant restrictions are there to increase payload. In our work, 2D chaotic map and NLFSR are used to developed a video steganography mechanism where data will be embedded in the segregated frames. This will increase the data hiding limit exponentially. Also, embedding position of each frame will be different from others frames which will increase the overall security of the proposed mechanism. We have achieved this randomized data hiding points by using a chaotic map. Basically, Chaotic theory which is non-linear dynamics physics is using in this era in the field of Cryptography and Steganography and because of this theory, little bit changes in initial condition makes the output totally different. So, it is very hard to get embedding position of data without knowing the initial value of the chaotic map.

Kara, I., Aydos, M..  2018.  Static and Dynamic Analysis of Third Generation Cerber Ransomware. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT). :12–17.

Cyber criminals have been extensively using malicious Ransomware software for years. Ransomware is a subset of malware in which the data on a victim's computer is locked, typically by encryption, and payment is demanded before the ransomed data is decrypted and access returned to the victim. The motives for such attacks are not only limited to economical scumming. Illegal attacks on official databases may also target people with political or social power. Although billions of dollars have been spent for preventing or at least reducing the tremendous amount of losses, these malicious Ransomware attacks have been expanding and growing. Therefore, it is critical to perform technical analysis of such malicious codes and, if possible, determine the source of such attacks. It might be almost impossible to recover the affected files due to the strong encryption imposed on such files, however the determination of the source of Ransomware attacks have been becoming significantly important for criminal justice. Unfortunately, there are only a few technical analysis of real life attacks in the literature. In this work, a real life Ransomware attack on an official institute is investigated and fully analyzed. The analysis have been performed by both static and dynamic methods. The results show that the source of the Ransomware attack has been shown to be traceable from the server's whois information.

Karadoğan, İsmail, Karci, Ali.  2019.  Detection of Covert Timing Channels with Machine Learning Methods Using Different Window Sizes. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). :1—5.

In this study, delays between data packets were read by using different window sizes to detect data transmitted from covert timing channel in computer networks, and feature vectors were extracted from them and detection of hidden data by some classification algorithms was achieved with high performance rate.

Karaküçük, Ahmet, Dirik, A. Emir.  2019.  Source Device Attribution of Thermal Images Captured with Handheld IR Cameras. 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). :547—551.

Source camera attribution of digital images has been a hot research topic in digital forensics literature. However, the thermal cameras and the radiometric data they generate stood as a nascent topic, as such devices are expensive and tailored for specific use-cases - not adapted by the masses. This has changed dramatically, with the low-cost, pluggable thermal-camera add-ons to smartphones and similar low-cost pocket-size thermal cameras introduced to consumers recently, which enabled the use of thermal imaging devices for the masses. In this paper, we are going to investigate the use of an established source device attribution method on radiometric data produced with a consumer-level, low-cost handheld thermal camera. The results we represent in this paper are promising and show that it is quite possible to attribute thermal images with their source camera.

Karam, R., Hoque, T., Ray, S., Tehranipoor, M., Bhunia, S..  2017.  MUTARCH: Architectural diversity for FPGA device and IP security. 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC). :611–616.
Field Programmable Gate Arrays (FPGAs) are being increasingly deployed in diverse applications including the emerging Internet of Things (IoT), biomedical, and automotive systems. However, security of the FPGA configuration file (i.e. bitstream), especially during in-field reconfiguration, as well as effective safeguards against unauthorized tampering and piracy during operation, are notably lacking. The current practice of bitstreram encryption is only available in high-end FPGAs, incurs unacceptably high overhead for area/energy-constrained devices, and is susceptible to side channel attacks. In this paper, we present a fundamentally different and novel approach to FPGA security that can protect against all major attacks on FPGA, namely, unauthorized in-field reprogramming, piracy of FPGA intellectual property (IP) blocks, and targeted malicious modification of the bitstream. Our approach employs the security through diversity principle to FPGA, which is often used in the software domain. We make each device architecturally different from the others using both physical (static) and logical (time-varying) configuration keys, ensuring that attackers cannot use a priori knowledge about one device to mount an attack on another. It therefore mitigates the economic motivation for attackers to reverse engineering the bitstream and IP. The approach is compatible with modern remote upgrade techniques, and requires only small modifications to existing FPGA tool flows, making it an attractive addition to the FPGA security suite. Our experimental results show that the proposed approach achieves provably high security against tampering and piracy with worst-case 14% latency overhead and 13% area overhead.
Karame, Ghassan.  2016.  On the Security and Scalability of Bitcoin's Blockchain. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1861–1862.

The blockchain emerges as an innovative tool which proves to be useful in a number of application scenarios. A number of large industrial players, such as IBM, Microsoft, Intel, and NEC, are currently investing in exploiting the blockchain in order to enrich their portfolio of products. A number of researchers and practitioners speculate that the blockchain technology can change the way we see a number of online applications today. Although it is still early to tell for sure, it is expected that the blockchain will stimulate considerable changes to a large number of products and will positively impact the digital experience of many individuals around the globe. In this tutorial, we overview, detail, and analyze the security provisions of Bitcoin and its underlying blockchain-effectively capturing recently reported attacks and threats in the system. Our contributions go beyond the mere analysis of reported vulnerabilities of Bitcoin; namely, we describe and evaluate a number of countermeasures to deter threats on the system-some of which have already been incorporated in the system. Recall that Bitcoin has been forked multiple times in order to fine-tune the consensus (i.e., the block generation time and the hash function), and the network parameters (e.g., the size of blocks). As such, the results reported in this tutorial are not only restricted to Bitcoin, but equally apply to a number of "altcoins" which are basically clones/forks of the Bitcoin source code. Given the increasing number of alternative blockchain proposals, this tutorial extracts the basic security lessons learnt from the Bitcoin system with the aim to foster better designs and analysis of next-generation secure blockchain currencies and technologies.

Karami, Mohammad, Park, Youngsam, McCoy, Damon.  2016.  Stress Testing the Booters: Understanding and Undermining the Business of DDoS Services. Proceedings of the 25th International Conference on World Wide Web. :1033–1043.

DDoS-for-hire services, also known as booters, have commoditized DDoS attacks and enabled abusive subscribers of these services to cheaply extort, harass and intimidate businesses and people by taking them offline. However, due to the underground nature of these booters, little is known about their underlying technical and business structure. In this paper, we empirically measure many facets of their technical and payment infrastructure. We also perform an analysis of leaked and scraped data from three major booters–-Asylum Stresser, Lizard Stresser and VDO–-which provides us with an in-depth view of their customers and victims. Finally, we conduct a large-scale payment intervention in collaboration with PayPal and evaluate its effectiveness as a deterrent to their operations. Based on our analysis, we show that these booters are responsible for hundreds of thousands of DDoS attacks and identify potentially promising methods to undermine these services by increasing their costs of operation.

Karamollaoglu, H., Dogru, İ A., Dorterler, M..  2018.  Detection of Spam E-mails with Machine Learning Methods. 2018 Innovations in Intelligent Systems and Applications Conference (ASYU). :1–5.

E-mail communication is one of today's indispensable communication ways. The widespread use of email has brought about some problems. The most important one of these problems are spam (unwanted) e-mails, often composed of advertisements or offensive content, sent without the recipient's request. In this study, it is aimed to analyze the content information of e-mails written in Turkish with the help of Naive Bayes Classifier and Vector Space Model from machine learning methods, to determine whether these e-mails are spam e-mails and classify them. Both methods are subjected to different evaluation criteria and their performances are compared.

Karande, Vishal, Chandra, Swarup, Lin, Zhiqiang, Caballero, Juan, Khan, Latifur, Hamlen, Kevin.  2018.  BCD: Decomposing Binary Code Into Components Using Graph-Based Clustering. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :393-398.

Complex software is built by composing components implementing largely independent blocks of functionality. However, once the sources are compiled into an executable, that modularity is lost. This is unfortunate for code recipients, for whom knowing the components has many potential benefits, such as improved program understanding for reverse-engineering, identifying shared code across different programs, binary code reuse, and authorship attribution. A novel approach for decomposing such source-free program executables into components is here proposed. Given an executable, the approach first statically builds a decomposition graph, where nodes are functions and edges capture three types of relationships: code locality, data references, and function calls. It then applies a graph-theoretic approach to partition the functions into disjoint components. A prototype implementation, BCD, demonstrates the approach's efficacy: Evaluation of BCD with 25 C++ binary programs to recover the methods belonging to each class achieves high precision and recall scores for these tested programs.

Karasevich, Aleksandr M., Tutnov, Igor A., Baryshev, Gennady K..  2016.  The Prospects of Application of Information Technologies and the Principles of Intelligent Automated Systems to Manage the Security Status of Objects of Energy Supply of Smart Cities. Proceedings of the International Conference on Electronic Governance and Open Society: Challenges in Eurasia. :9–14.

The paper focuses on one of the methods of designing a highly-automated hardware-software complex aimed at controlling the security of power grids and units that support both central heating and power systems of smart cities. We understand this condition as a situation when any energy consumers of smart cities will be provided with necessary for their living amounts of energy and fuel at any time, including possible periods of techno genic and natural hazards. Two main scientific principles lie in the base of the approach introduced. The first one is diversification of risks of energy security of smart cities by rational choosing the different energy generation sources ratio for fuel-energy balance of a smart city, including large fuel electric power plants and small power autonomous generators. For example, they can be wind energy machinery of sun collectors, heat pipes, etc. The second principle is energy efficiency and energy saving of smart cities. In our case this principle is realized by the high level of automation of monitoring and operation of security status of energy systems and complexes that provide the consumers of smart cities with heat, hot water and electricity, as well as by preventive alert of possible emergencies and high reliability of functioning of all energy facilities. We formulate the main principle governing the construction of a smart hardware-software complex used to maintain a highly-automated control over risks connected with functioning of both power sources and transmission grids. This principle is for open block architecture, including highly autonomous block-modules of primary registration of measuring information, data analysis and systems of automated operation. It also describes general IT-tools used to control the risks of supplying smart cities with energy and shows the structure of a highly-automated system designed to select technological and managerial solutions for a smart city's energy supply system.

Karatas, G., Demir, O., Sahingoz, O. K..  2019.  A Deep Learning Based Intrusion Detection System on GPUs. 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1—6.

In recent years, almost all the real-world operations are transferred to cyber world and these market computers connect with each other via Internet. As a result of this, there is an increasing number of security breaches of the networks, whose admins cannot protect their networks from the all types of attacks. Although most of these attacks can be prevented with the use of firewalls, encryption mechanisms, access controls and some password protections mechanisms; due to the emergence of new type of attacks, a dynamic intrusion detection mechanism is always needed in the information security market. To enable the dynamicity of the Intrusion Detection System (IDS), it should be updated by using a modern learning mechanism. Neural Network approach is one of the mostly preferred algorithms for training the system. However, with the increasing power of parallel computing and use of big data for training, as a new concept, deep learning has been used in many of the modern real-world problems. Therefore, in this paper, we have proposed an IDS system which uses GPU powered Deep Learning Algorithms. The experimental results are collected on mostly preferred dataset KDD99 and it showed that use of GPU speed up training time up to 6.48 times depending on the number of the hidden layers and nodes in them. Additionally, we compare the different optimizers to enlighten the researcher to select the best one for their ongoing or future research.

Karatas, Nihan, Yoshikawa, Soshi, Okada, Michio.  2016.  NAMIDA: Sociable Driving Agents with Multiparty Conversation. Proceedings of the Fourth International Conference on Human Agent Interaction. :35–42.

We propose a multi party conversational social interface NAMIDA through a pilot study. The system consists of three robots that can converse with each other about environment throughout the road. Through this model, the directed utterances towards the driver diminishes by utilizing turn-taking process between the agents, and the mental workload of the driver can be reduced compared to the conventional one-to-one communication based approach that directly addresses the driver. We set up an experiment to compare the both approaches to explore their effects on the workload and attention behaviors of drivers. The results indicated that the multi-party conversational approach has a better effect on reducing certain workload factors. Also, the analysis of attention behaviors of drivers revealed that our method can better promote the drivers to focus on the road.

Karati, Arijit, Biswas, G. P..  2016.  Cryptanalysis and Improvement of a Certificateless Short Signature Scheme Using Bilinear Pairing. Proceedings of the International Conference on Advances in Information Communication Technology & Computing. :19:1–19:6.

Recently, various certificate-less signature (CLS) schemes have been developed using bilinear pairing to provide authenticity of message. In 2015, Jia-Lun Tsai proposed a certificate-less pairing based short signature scheme using elliptic curve cryptography (ECC) and prove its security under random oracle. However, it is shown that the scheme is inappropriate for its practical use as there is no message-signature dependency present during signature generation and verification. Thus, the scheme is vulnerable. To overcome these attacks, this paper aims to present a variant of Jia-Lun Tsai's short signature scheme. Our scheme is secured under the hardness of collusion attack algorithm with k traitors (k–-CAA). The performance analysis demonstrates that proposed scheme is efficient than other related signature schemes.

Karavaev, I. S., Selivantsev, V. I., Shtern, Y. I., Shtern, M. Y..  2018.  The development of the data transfer protocol in the intelligent control systems of the energy carrier parameters. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1305–1308.
For the control of the parameters and for the accounting of the energy consumption in buildings and structures the intelligent control system has been developed that provides: the continuous monitoring of the thermodynamic parameters of the energy carriers measured by wireless smart sensors; the calculation and transmission of the measured parameters via the radio channel to the database for their accumulation and storage; control signals delivery for the control devices of the energy consumption and for the security devices; the maintaining of a database of the energy consumption accounting. For the interaction of the hardware and software in the control system, the SimpliciTI-based protocol and algorithms for the reliable data transmission over the radio channel in a dense urban environment have been developed.
Karbab, ElMouatez Billah, Debbabi, Mourad.  2018.  ToGather: Automatic Investigation of Android Malware Cyber-Infrastructures. Proceedings of the 13th International Conference on Availability, Reliability and Security. :20:1-20:10.

The popularity of Android, not only in handsets but also in IoT devices, makes it a very attractive target for malware threats, which are actually expanding at a significant rate. The state-of-the-art in malware mitigation solutions mainly focuses on the detection of malicious Android apps using dynamic and static analysis features to segregate malicious apps from benign ones. Nevertheless, there is a small coverage for the Internet/network dimension of Android malicious apps. In this paper, we present ToGather, an automatic investigation framework that takes Android malware samples as input and produces insights about the underlying malicious cyber infrastructures. ToGather leverages state-of-the-art graph theory techniques to generate actionable, relevant and granular intelligence to mitigate the threat effects induced by the malicious Internet activity of Android malware apps. We evaluate ToGather on a large dataset of real malware samples from various Android families, and the obtained results are both interesting and promising.

Karbab, ElMouatez Billah, Debbabi, Mourad, Derhab, Abdelouahid, Mouheb, Djedjiga.  2016.  Cypider: Building Community-based Cyber-defense Infrastructure for Android Malware Detection. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :348–362.

The popularity of Android OS has dramatically increased malware apps targeting this mobile OS. The daily amount of malware has overwhelmed the detection process. This fact has motivated the need for developing malware detection and family attribution solutions with the least manual intervention. In response, we propose Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building an efficient and scalable similarity network infrastructure of malicious apps. Our detection method is based on a novel concept, namely malicious community, in which we consider, for a given family, the instances that share common features. Under this concept, we assume that multiple similar Android apps with different authors are most likely to be malicious. Cypider leverages this assumption for the detection of variants of known malware families and zero-day malware. It is important to mention that Cypider does not rely on signature-based or learning-based patterns. Alternatively, it applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and most likely malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a learning model for each malicious community. Cypider shows excellent results by detecting about 50% of the malware dataset in one detection iteration. Besides, the preliminary results of the community fingerprint are promising as we achieved 87% of the detection.

Kargaard, J., Drange, T., Kor, A., Twafik, H., Butterfield, E..  2018.  Defending IT Systems against Intelligent Malware. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). :411-417.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

Karim, Hassan, Rawat, Danda.  2019.  A Trusted Bluetooth Performance Evaluation Model for Brain Computer Interfaces. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). :47—52.
Bluetooth enables excellent mobility in Brain Computer Interface (BCI) research and other use cases including ambulatory care, telemedicine, fitness tracking and mindfulness training. Although significant research exists for an all-encompassing BCI performance rating, almost all the literature addresses performance in terms of brain state or brain function classification accuracy. For the few published experiments that address BCI hardware performance, they too, focused on improving classification accuracy. This paper explores some of the more recent studies and proposes a trusted performance rating for BCI applications based on the enhanced privacy, yet reduced bandwidth needs of mobile EEG-based BCI applications. This paper proposes a set of Bluetooth operating parameters required to meet the performance, usability and privacy requirements of reliable and secure mobile neuro-feedback applications. It presents a rating model, "Trusted Mobile BCI", based on those operating parameters, and validated the model with studies that leveraged mobile BCI technology.
Karimian, Nima, Wortman, Paul A., Tehranipoor, Fatemeh.  2016.  Evolving Authentication Design Considerations for the Internet of Biometric Things (IoBT). Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis. :10:1–10:10.

The Internet of Things (IoT) is a design implementation of embedded system design that connects a variety of devices, sensors, and physical objects to a larger connected network (e.g. the Internet) which requires human-to-human or human-to-computer interaction. While the IoT is expected to expand the user's connectivity and everyday convenience, there are serious security considerations that come into account when using the IoT for distributed authentication. Furthermore the incorporation of biometrics to IoT design brings about concerns of cost and implementing a 'user-friendly' design. In this paper, we focus on the use of electrocardiogram (ECG) signals to implement distributed biometrics authentication within an IoT system model. Our observations show that ECG biometrics are highly reliable, more secure, and easier to implement than other biometrics.

Karimov, Madjit, Tashev, Komil, Rustamova, Sanobar.  2020.  Application of the Aho-Corasick algorithm to create a network intrusion detection system. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—5.
One of the main goals of studying pattern matching techniques is their significant role in real-world applications, such as the intrusion detection systems branch. The purpose of the network attack detection systems NIDS is to protect the infocommunication network from unauthorized access. This article provides an analysis of the exact match and fuzzy matching methods, and discusses a new implementation of the classic Aho-Korasik pattern matching algorithm at the hardware level. The proposed approach to the implementation of the Aho-Korasik algorithm can make it possible to ensure the efficient use of resources, such as memory and energy.
Kariyappa, S., Qureshi, M. K..  2020.  Defending Against Model Stealing Attacks With Adaptive Misinformation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :767—775.

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation" to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker's clone model (by up to 40%), while minimally impacting the accuracy (\textbackslashtextless; 0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.

Karlsson, J., Dooley, L. S., Pulkkis, G..  2018.  Secure Routing for MANET Connected Internet of Things Systems. 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). :114-119.

This paper presents a contemporary review of communication architectures and topographies for MANET-connected Internet-of-Things (IoT) systems. Routing protocols for multi-hop MANETs are analyzed with a focus on the standardized Routing Protocol for Low-power and Lossy Networks. Various security threats and vulnerabilities in current MANET routing are described and security enhanced routing protocols and trust models presented as methodologies for supporting secure routing. Finally, the paper identifies some key research challenges in the emerging domain of MANET-IoT connectivity.