Biblio

Today our world benefits from Internet of Things (IoT) technology; however, new security problems arise when these IoT devices are introduced into our homes. Because many of these IoT devices have access to the Internet and they have little to no security, they make our smart homes highly vulnerable to compromise. Some of the threats include IoT botnets and generic confidentiality, integrity, and availability (CIA) attacks. Our research explores botnet detection by experimenting with supervised machine learning and deep-learning classifiers. Further, our approach assesses classifier performance on unbalanced datasets that contain benign data, mixed in with small amounts of malicious data. We demonstrate that the classifiers can separate malicious activity from benign activity within a small IoT network dataset. The classifiers can also separate malicious activity from benign activity in increasingly larger datasets. Our experiments have demonstrated incremental improvement in results for (1) accuracy, (2) probability of detection, and (3) probability of false alarm. The best performance results include 99.9% accuracy, 99.8% probability of detection, and 0% probability of false alarm. This paper also demonstrates how the performance of these classifiers increases, as IoT training datasets become larger and larger.

Through analysis of sessions in which files were created and downloaded on three Cowrie SSH/Telnet honeypots, we find that IoT botnets are by far the most common source of malware on connected systems with weak credentials. We detail our honeypot configuration and describe a simple method for listing near-identical malicious login sessions using edit distance. A large number of IoT botnets attack our honeypots, but the malicious sessions which download botnet software to the honeypot are almost all nearly identical to one of two common attack patterns. It is apparent that the Mirai worm is still the dominant botnet software, but has been expanded and modified by other hackers. We also find that the same loader devices deploy several different botnet malware strains to the honeypot over the course of a 40 day period, suggesting multiple botnet deployments from the same source. We conclude that Mirai continues to be adapted but can be effectively tracked using medium interaction honeypots such as Cowrie.

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.

This paper proposes a deep learning-based white-hat worm launcher in Botnet Defense System (BDS). BDS uses white-hat botnets to defend an IoT system against malicious botnets. White-hat worm launcher literally launches white-hat worms to create white-hat botnets according to the strategy decided by BDS. The proposed launcher learns with deep learning where is the white-hat worms' right place to successfully drive out malicious botnets. Given a system situation invaded by malicious botnets, it predicts a worms' placement by the learning result and launches them. We confirmed the effect of the proposed launcher through simulating evaluation.

The facial recognition time by time takes more importance, due to the extend kind of applications it has, but it is still challenging when faces big variations in the characteristics of the biometric data used in the process and especially referring to the transportation of information through the internet in the internet of things context. Based on the systematic review and rigorous study that supports the extraction of the most relevant information on this topic [1], a software architecture proposal which contains basic security requirements necessary for the treatment of the data involved in the application of facial recognition techniques, oriented to an IoT environment was generated. Concluding that the security and privacy considerations of the information registered in IoT devices represent a challenge and it is a priority to be able to guarantee that the data circulating on the network are only accessible to the user that was designed for this.

Physical unclonable functions(PUFs) are widely used as hardware root-of-trust to secure IoT devices, data and services. A PUF exploits inherent randomness introduced during manufacturing to give a unique digital fingerprint. Static Random-Access Memory (SRAM) based PUFs can be used as a mature technology for authentication. An SRAM with a number of SRAM cells gives an unrepeatable and random pattern of 0's and 1's during power on. As it is a unique pattern, it can be called as SRAM fingerprint and can be used as a PUF. The chance of producing more number of same values (either zero or one) is higher during power on. If a particular value present at almost all the cell during power on, it will lead to the dominance of either zero or one in the cryptographic key sequence. As the cryptographic key is generated by randomly taking address location of SRAM cells, (the subset of power on values of all the SRAM cells)the probability of occurring the same sequence most of the time is higher. In order to avoid that situation, SRAM should have to produce an equal number of zeros and ones during power on. SRAM PUF is implemented in Cadence Virtuoso tool. To generate equal zeros and ones during power on, variations can be done in the physical dimensions and to increase the stability body biasing can be effectively done.

This work describes the architecture and prototype implementation of a novel trust-aware continuous authorization technology that targets consumer Internet of Things (IoT), e.g., Smart Home. Our approach extends previous authorization models in three complementary ways: (1) By incorporating trust-level evaluation formulae as conditions inside authorization rules and policies, while supporting the evaluation of such policies through the fusion of an Attribute-Based Access Control (ABAC) authorization policy engine with a Trust-Level-Evaluation-Engine (TLEE). (2) By introducing contextualized, continuous monitoring and re-evaluation of policies throughout the authorization life-cycle. That is, mutable attributes about subjects, resources and environment as well as trust levels that are continuously monitored while obtaining an authorization, throughout the duration of or after revoking an existing authorization. Whenever change is detected, the corresponding authorization rules, including both access control rules and trust level expressions, are re-evaluated.(3) By minimizing the computational and memory footprint and maximizing concurrency and modular evaluation to improve performance while preserving the continuity of monitoring. Finally we introduce an application of such model in Zero Trust Architecture (ZTA) for consumer IoT.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.

Internet of Things (IoT) now has extremely wide applications in many areas of life such as urban management, environmental management, smart shopping, and smart home. Because of the wide range of application fields, the IoT infrastructures are built differently. To make an IoT system indoor with high efficiency and more convenience, a case study for smart home security using Bluetooth Mesh approach is introduced. By using Bluetooth Mesh technology in home security, the user can open the door everywhere inside their house. The system work in a flexible way since it can extend the working range of network. In addition, the system can monitor the state of both the lock and any node in network by using a gateway to transfer data to cloud and enable a website-based interface.

This paper investigates the use of BeagleBone Black Wireless single-board Linux computers with Long Range (LoRa) transceivers to send and receive information in a mesh network while one of the transmitting/receiving nodes is acting as a relay in the system. An experiment is conducted to examine how long each LoRa node needed to learn the transmission intervals of any other transmitting nodes on the network and to synchronize with the other nodes prior to transmission. The spread factor, bandwidth, and coding rate are all varied for a total of 18 different combinations. A link to the Python code used on the BeagleBone Black is provided at the end of this paper. The best parameter combinations for each individual node and for the system as a whole is investigated. Additional experiments and applications of this technology are explored in the conclusions.

We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet.

The emerging Internet of Things (IoT) challenges the end-to-end transport of the Internet by low power lossy links and gateways that perform protocol translations. Protocols such as CoAP or MQTT-SN are degraded by the overhead of DTLS sessions, which in common deployment protect content transfer only up to the gateway. To preserve content security end-to-end via gateways and proxies, the IETF recently developed Object Security for Constrained RESTful Environments (OSCORE), which extends CoAP with content object security features commonly known from Information Centric Networks (ICN). This paper presents a comparative analysis of protocol stacks that protect request-response transactions. We measure protocol performances of CoAP over DTLS, OSCORE, and the information-centric Named Data Networking (NDN) protocol on a large-scale IoT testbed in single- and multi-hop scenarios. Our findings indicate that (a) OSCORE improves on CoAP over DTLS in error-prone wireless regimes due to omitting the overhead of maintaining security sessions at endpoints, and (b) NDN attains superior robustness and reliability due to its intrinsic network caches and hop-wise retransmissions.

Named Data Network's (NDN) data-centric approach makes it a suitable solution in a networking scenario where there are connectivity issues as a result of the dynamism of the network. Coupling of this ability with the blockchain's well-documented immutable trustworthy-distributed ledger feature, the union of blockchain and NDN in an Internet-of-Battlefield-Things (IoBT) setting could prove to be the ideal alliance that would guarantee data exchanged in an IoBT environment is trusted and less susceptible to cyber-attacks and packet losses. Various blockchain technologies, however, require that each node has a ledger that stores information or transactions in a chain of blocks. This poses an issue as nodes in an IoBT setting have varying computing and storage resources. Moreover, most of the nodes in the IoT/IoBT network are plagued with limited resources. As such, there needs to be an approach that ensures that the limited resources of these nodes are efficiently utilized. In this paper, we investigate an approach that merges blockchain and NDN to efficiently utilize the resources of these resource-constrained nodes by only storing relevant information on each node's ledger. Furthermore, we propose a sharding technique called an Interest Group and introduce a novel consensus mechanism called Proof of Common Interest. Performance of the proposed approach is evaluated using numerical results.

Inter-networked control systems make smart buildings increasingly efficient but can lead to severe operational disruptions and infrastructure damage. It is vital the security state of smart buildings is properly assessed so that thorough and cost effective risk management can be established. This paper uniquely reports on an actual risk assessment performed in 2018 on one of the world's most densely monitored, state-of-the-art, smart buildings. From our observations, we suggest that current practice may be inadequate due to a number of challenges and deficiencies, including the lack of a recognised smart building risk assessment methodology. As a result, the security posture of many smart buildings may not be as robust as their risk assessments suggest. Crucially, we highlight a number of key recommendations for a more comprehensive risk assessment process for smart buildings. As a whole, we believe this practical experience report will be of interest to a range of smart building stakeholders.

With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.

IoT devices such as Google Home and Amazon Echo provide great convenience to our lives. Many of these IoT devices collect data including Personal Identifiable Information such as names, phone numbers, and addresses and thus IoT security is important. However, conducting security analysis on IoT devices is challenging due to the variety, the volume of the devices, and the special skills required for hardware and software analysis. In this research, we create and demonstrate a dynamic analysis security testing infrastructure for capturing network traffic from IoT devices. The network traffic is automatically mirrored to a server for live traffic monitoring and offline data analysis. Using the dynamic analysis security testing infrastructure, we conduct extensive security analysis on network traffic from Google Home and Amazon Echo. Our testing results indicate that Google Home enforces tighter security controls than Amazon Echo while both Google and Amazon devices provide the desired security level to protect user data in general. The dynamic analysis security testing infrastructure presented in the paper can be utilized to conduct similar security analysis on any IoT devices.

The world is facing a new revolutionary technology transition, Internet of things (IoT). IoT systems requires secure connectivity of distributed entities, including in-field sensors. For such external devices, Side Channel Analysis poses a potential threat as it does not require complete knowledge about the crypto algorithm. In this work, we perform Horizontal Correlation Power Analysis (HCPA) which is a type of Side Channel Analysis (SCA) over the Elliptic Curve Diffie Hellman (ECDH) key exchange protocol. ChipWhisperer (CW) by NewAE Technologies is an open source toolchain which is utilized to perform the HCPA by using CW toolchain. To best of our knowledge, this is the first attempt to implemented ECDH on Artix-7 FPGA for HCPA. We compare our correlation results with the results from AES -128 bits provided by CW. Our point of attack is the Double and Add algorithm which is used to perform Scalar multiplication in ECC. We obtain a maximum correlation of 7% for the key guess using the HCPA. We also discuss about the possible cause for lower correlation and few potentials ways to improve it. In Addition to HCPA we also perform Simple Power Analysis (SPA) (visual) for ECDH, to guess the trailing zeros in the 128-bit secret key for different power traces.

Multi-authority attribute-based encryption (MA-ABE) is a promising technique to achieve fine-grained access control over encrypted data in cross domain applications. However, the dynamic change of users' access privilege brings security problems, and the heavy encryption computational cost is issue for resource-constrained users in IoT. Moreover, the invalid or illegal ciphertext will waste system resources. We propose a large universe MA-CP-ABE scheme with revocation and online/offline encryption. In our scheme, an efficient revocation mechanism is designed to change users' access privilege timely. Most of the encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, the scheme supports ciphertext verification and only valid ciphertext can be stored and transmitted. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource constrained users in edge computing for IoT.

This paper presents an overhead analysis of the use of digital signature mechanisms in the Message Queue Telemetry Transport (MQTT) protocol for three classes of constrained-device. Because the resources provided by constrained-devices are very limited, the purpose of this overhead analysis is to help find out the advantages and disadvantages of each class of constrained-devices after a security mechanism has been applied, namely by applying a digital signature mechanism. The objective of using this digital signature mechanism is for providing integrity, that if the payload sent and received in its destination is still original and not changed during the transmission process. The overhead analysis aspects performed are including analyzing decryption time, signature verification performance, message delivery time, memory and flash usage in the three classes of constrained-device. Based on the overhead analysis result, it can be seen that for decryption time and signature verification performance, the Class-2 device is the fastest one. For message delivery time, the smallest time needed for receiving the payload is Class-l device. For memory usage, the Class-2 device is providing the biggest available memory and flash.

This paper describes a system that comprises of control, safety and security subsystem for industries and homes. The entire system is based on the Bolt IoT platform. Using this system, the user can control the devices such as LEDs, speed of the fan or DC motor, monitor the temperature of the premises with an alert sub-system for critical temperatures through SMS and call, monitor the presence of anyone inside the premises with an alert sub-system about any intrusion through SMS and call. If the system is used specifically in any industry then instead of monitoring the temperature any other physical quantity, which is critical for that industry, can be monitored using suitable sensors. In addition, the cloud connectivity is provided to the system using the Bolt IoT module and temperature data is sent to the cloud where using machine-learning algorithm the future temperature is predicted to avoid any accidents in the future.

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.

Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.

Blockchain technology is the cornerstone of digital trust and systems' decentralization. The necessity of eliminating trust in computing systems has triggered researchers to investigate the applicability of Blockchain to decentralize the conventional security models. Specifically, researchers continuously aim at minimizing trust in the well-known Public Key Infrastructure (PKI) model which currently requires a trusted Certificate Authority (CA) to sign digital certificates. Recently, the Automated Certificate Management Environment (ACME) was standardized as a certificate issuance automation protocol. It minimizes the human interaction by enabling certificates to be automatically requested, verified, and installed on servers. ACME only solved the automation issue, but the trust concerns remain as a trusted CA is required. In this paper we propose decentralizing the ACME protocol by using the Blockchain technology to enhance the current trust issues of the existing PKI model and to eliminate the need for a trusted CA. The system was implemented and tested on Ethereum Blockchain, and the results showed that the system is feasible in terms of cost, speed, and applicability on a wide range of devices including Internet of Things (IoT) devices.

In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.