Biblio

Wear and tear are essential in establishing the market value of an asset. From shutter counters on DSLRs to odometers inside cars, specific counters, that encode the degree of wear, exist on most products. But malicious modification of the information that they report was always a concern. Our work explores a solution to this problem by using the blockchain technology, a layered encoding of product attributes and identity-based cryptography. Merging such technologies is essential since blockchains facilitate the construction of a distributed database that is resilient to adversarial modifications, while identity-based signatures set room for a more convenient way to check the correctness of the reported values based on the name of the product and pseudonym of the owner alone. Nonetheless, we reinforce security by using ownership cards deployed around NFC tokens. Since odometer fraud is still a major practical concern, we discuss a practical scenario centered on vehicles, but the framework can be easily extended to many other assets.

Cyber attacks against automobiles have increased over the last decade due to the expansion in attack surfaces. This is the result of modern automobiles having connections such as Bluetooth, WiFi, and other broadband services. While there has been numerous proposed solutions in the literature, none have been widely adopted as maintaining real-time message deliverability in the Controller Area Networks (CAN) outweighs proposed security solutions. Through iterative research, we have developed a solution which mitigates an attacker's impact on the CAN bus by using CAN's inherent features of arbitration, error detection and signaling, and fault confinement mechanism. The solution relies on an access controller and message priority thresholds added to the CAN data-link layer. The results provide no time delay for non-malicious traffic and mitigates bus impact of a subverted node attempting to fabricate messages at an unauthorized priority level.

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

The burgeoning Internet of Things (IoT) has offered unprecedented opportunities for innovations and applications that are continuously changing our life. At the same time, the large amount of pervasive IoT applications have posed paramount threats to the user's security and privacy. While a lot of efforts have been dedicated to deal with such threats from the hardware, the software, and the applications, in this paper, we argue and envision that more effective and comprehensive protection for IoT systems can only be achieved via a cross-layer approach. As such, we present our initial design of XLF, a cross-layer framework towards this goal. XLF can secure the IoT systems not only from each individual layer of device, network, and service, but also through the information aggregation and correlation of different layers.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human-machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.

Physically Unclonable Functions (PUFs) promise to be a critical hardware primitive to provide unique identities to billions of connected devices in Internet of Things (IoTs). In traditional authentication protocols a user presents a set of credentials with an accompanying proof such as password or digital certificate. However, IoTs need more evolved methods as these classical techniques suffer from the pressing problems of password dependency and inability to bind access requests to the “things” from which they originate. Additionally, the protocols need to be lightweight and heterogeneous. Although PUFs seem promising to develop such mechanism, it puts forward an open problem of how to develop such mechanism without needing to store the secret challenge-response pair (CRP) explicitly at the verifier end. In this paper, we develop an authentication and key exchange protocol by combining the ideas of Identity based Encryption (IBE), PUFs and Key-ed Hash Function to show that this combination can help to do away with this requirement. The security of the protocol is proved formally under the Session Key Security and the Universal Composability Framework. A prototype of the protocol has been implemented to realize a secured video surveillance camera using a combination of an Intel Edison board, with a Digilent Nexys-4 FPGA board consisting of an Artix-7 FPGA, together serving as the IoT node. We show, though the stand-alone video camera can be subjected to man-in-the-middle attack via IP-spoofing using standard network penetration tools, the camera augmented with the proposed protocol resists such attacks and it suits aptly in an IoT infrastructure making the protocol deployable for the industry.

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.

Botnets are responsible for many large scale attacks happening on the Internet. Their weak point, which is usually targeted to take down a botnet, is the command and control infrastructure: the foundation for the diffusion of the botmaster's instructions. Hence, botmasters employ stealthy communication methods to remain hidden and retain control of the botnet. Recent research has shown that blockchains can be leveraged for under the radar communication with bots, however these methods incur fees for transaction broadcasting. This paper discusses the use of a novel technology, Whisper, for command and control instruction dissemination. Whisper allows a botmaster to control bots at virtually zero cost, while providing a peer-to-peer communication infrastructure, as well as privacy and encryption as part of its dark communication strategy. It is therefore well suited for bidirectional botnet command and control operations, and creating a botnet that is very difficult to take down.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by enabling vehicles to broadcast information-such as speed, location and heading-through inter-vehicular communications to proactively avoid collisions. However, the attacks targeting these networks might overshadow their advantages if not protected against. One powerful threat against VANETs is vehicular botnets. In our earlier work, we demonstrated several vehicular botnet attacks that can have damaging impacts on the security and privacy of VANETs. In this paper, we present SHIELDNET, the first detection mechanism against vehicular botnets. Similar to the detection approaches against Internet botnets, we target the vehicular botnet communication and use several machine learning techniques to identify vehicular bots. We show via simulation that SHIELDNET can identify 77 percent of the vehicular bots. We propose several improvements on the VANET standards and show that their existing vulnerabilities make an effective defense against vehicular botnets infeasible.

Over the last decade, a technology called Internet of Things (IoT) has been evolving at a rapid pace. It enables the development of endless applications in view of availability of affordable components which provide smart ecosystems. The IoT devices are constrained devices which are connected to the internet and perform sensing tasks. Each device is identified by their unique address and also makes use of the Constrained Application Protocol (CoAP) as one of the main web transfer protocols. It is an application layer protocol which does not maintain secure channels to transfer information. For authentication and end-to-end security, Datagram Transport Layer Security (DTLS) is one of the possible approaches to boost the security aspect of CoAP, in addition to which there are many suggested ways to protect the transmission of sensitive information. CoAP uses DTLS as a secure protocol and UDP as a transfer protocol. Therefore, the attacks on UDP or DTLS could be assigned as a CoAP attack. An attack on DTLS could possibly be launched in a single session and a strong authentication mechanism is needed. Man-In-The-Middle attack is one the peak security issues in CoAP as cited by Request For Comments(RFC) 7252, which encompasses attacks like Sniffing, Spoofing, Denial of Service (DoS), Hijacking, Cross-Protocol attacks and other attacks including Replay attacks and Relay attacks. In this work, a client-server architecture is setup, whose end devices communicate using CoAP. Also, a proxy system was installed across the client side to launch an active interception between the client and the server. The work will further be enhanced to provide solutions to mitigate these attacks.

With the gradual progress of cloud computing, big data, network virtualization and other network technology. The traditional network architecture can no longer support this huge business. At this time, the clean slate team defined a new network architecture, SDN (Software Defined Network). It has brought about tremendous changes in the development of today's networks. The controller sends the flow table down to the switch, and the data flow is forwarded through matching flow table items. However, the current flow table resources of the SDN switch are very limited. Therefore, this paper studies the technology of the latest SDN Flow table optimization at home and abroad, proposes an efficient optimization scheme of Flow table item on the betweenness centrality through the main road selection algorithm, and realizes related applications by setting up experimental topology. Experiments show that this scheme can greatly reduce the number of flow table items of switches, especially the more hosts there are in the topology, the more obvious the experimental effect is. And the experiment proves that the optimization success rate is over 80%.

With the advent of fifth generation (5G) network and increasingly powerful mobile devices, people can conveniently obtain network resources wherever they are and whenever they want. However, the problem of mobility support in current network has not been adequately solved yet, especially in inter-domain mobile scenario, which leads to poor experience for mobile consumers. MobilityFirst is a clean slate future Internet architecture which adopts a clean separation between identity and network location. It provides new mechanisms to address the challenge of wireless access and mobility at scale. However, MobilityFirst lacks effective ways to deal with multicast service over mobile networks. In this paper, we design an efficient multicast mechanism based on MobilityFirst architecture and present the deployment in current network at scale. Furthermore, we propose a hierarchical multicast packet header with additional destinations to achieve low-cost dynamic multicast routing and provide solutions for both the multicast source and the multicast group members moving in intra- or inter-domain. Finally, we deploy a multicast prototype system to evaluate the performance of the proposed multicast mechanism.

In network communication domain, one of the most widely used protocol for encrypting data and securing communications is the IPSec protocol. The design of this protocol is based on two main phases which are: exchanging keys phase and transferring data phase. In this paper we focus on enhancing the exchanging keys phase which is included in the security association (SA), using a chaotic cryptosystem. Initially IPSec is based on the Internet Key Exchange (IKE) protocol for establishing the SA. Actually IKE protocol is in charge for negotiating the connection and for authenticating both nodes. However; using IKE gives rise to a major problem related to security attack such as the Man in the Middle Attack. In this paper, we propose a chaotic cryptosystem solution to generate SA file for the connected nodes of the network. By solving a 4-Dimension chaotic system, a SA file that includes 128-bit keys will be established. The proposed solution is implemented and tested using FPGA boards.

PRIME protocol is a narrowband power line communication protocol whose security is based on Advanced Encryption Standard. However, the key expansion process of AES algorithm is not unidirectional, and each round of keys are linearly related to each other, it is less difficult for eavesdroppers to crack AES encryption algorithm, leading to threats to the security of PRIME protocol. To solve this problem, this paper proposes an improvement of PRIME protocol based on chaotic cryptography. The core of this method is to use Chebyshev chaotic mapping and Logistic chaotic mapping to generate each round of key in the key expansion process of AES algorithm, In this way, the linear correlation between the key rounds can be reduced, making the key expansion process unidirectional, increasing the crack difficulty of AES encryption algorithm, and improving the security of PRIME protocol.

{In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances' intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol}.

In recent years, the application of Bluetooth has always been the highly debated topic among the researches. Through the Bluetooth protocol, Bluetooth can implement the data switching in short distance between various devices. Nevertheless, BlueBorne Attack makes the seemingly stable Bluetooth protocols full of vulnerabilities. Our research will concentrate on predicting the BlueBorne Attack with the following directions: the working mechanism, the working methods and effective range of BlueBorne. Based on the comprehensive review of recent peer-reviewed researches, this project provides a new model based on application layer to solve the security problem of BlueBorne. The paper asserts that compared with the previous research, the unique model has better consequence with highly stability.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

Wireless Mesh Networks (WMN) are becoming inevitable in this world of high technology as it provides low cost access to broadband services. Moreover, the technologists are doing research to make WMN more reliable and secure. Subsequently, among wireless ad-hoc networking technologies, Bluetooth Low Energy (BLE) is gaining high degree of importance among researchers due to its easy availability in the gadgets and low power consumption. BLE started its journey from version 4.0 and announced the latest version 5 with mesh support capability. BLE being a low power and mesh supported technology is nowadays among the hot research topics for the researchers. Many of the researchers are working on BLE mesh technology to make it more efficient and smart. Apart from other variables of efficiency, like all communication networks, mesh network security is also of a great concern. In view of the aforesaid, this paper provides a comprehensive review on several works associated to the security in WMN and BLE mesh networks and the research related to the BLE security protocols. Moreover, after the detailed research on related works, this paper has discussed the pros and cons of the present developed mesh security mechanisms. Also, at the end after extracting the curx from the present research on WMN and BLE mesh security, this research study has devised some solutions as how to mitigate the BLE mesh network security lapses.

Internet of Things devices have spread to near ubiquity this decade. All around us now lies an invisible mesh of communication from devices embedded in seemingly everything. Inevitably some of that communication flying around our heads will contain data that must be protected or otherwise shielded from tampering. The responsibility to protect this sensitive information from malicious actors as it travels through the air then falls upon the standards used to communicate this data. Bluetooth Low Energy (BLE) is one of these standards, the aim of this paper is to put its security standards to test. By attempting to exploit its vulnerabilities we can see how secure this standard really is. In this paper, we present steps for analyzing the security of BLE devices using open-source hardware and software.

We present TendrilStaller, an eclipse attack targeting at Bitcoin's peer-to-peer network. TendrilStaller enables an adversary to delay block propagation to a victim for 10 minutes. The adversary thus impedes the victim from getting the latest blockchain state. It only takes as few as one Bitcoin full node and two light weight nodes to perform the attack. The light weight nodes perform a subset of the functions of a full Bitcoin node. The attack exploits a recent block propagation protocol introduced in April 2016. The protocol prescribes a Bitcoin node to select 3 neighbors that can send new blocks unsolicited. These neighbors are selected based on their recent performance in providing blocks quickly. The adversary induces the victim to select 3 attack nodes by having attack nodes send valid blocks to the victim more quickly than other neighbors. For this purpose, the adversary deploys a handful of light weight nodes so that the adversary itself receives new blocks faster. The adversary then performs the attack to delay blocks propagated to the victim. We implement the attack on top of current default Bitcoin protocol We deploy the attack nodes in multiple locations around the globe and randomly select victim nodes. Depending on the round-trip time between the adversary and the victim, 50%-85% of the blocks could be delayed to the victim. We further show that the adoption of light weight nodes greatly increases the attack probability by 15% in average. Finally, we propose several countermeasures to mitigate this eclipse attack.

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.

In this work, we perform security analysis of using an e-mail as a self-service password reset point, and exploit some of the vulnerabilities of e-mail servers' forgotten password reset paths. We perform and illustrate three different attacks on a personal Email account, using a variety of tools such as: public knowledge attainable through social media or public records to answer security questions and execute a social engineering attack, hardware available to the public to perform a man in the middle attack, and free software to perform a brute-force attack on the login of the email account. Our results expose some of the inherent vulnerabilities in using emails as password reset points. The findings are extremely relevant to the security of mobile devices since users' trend has leaned towards usage of mobile devices over desktops for Internet access.

Modern operating systems for personal computers (including Linux, MAC, and Windows) provide user-level APIs for an application to access the I/O paths of another application. This design facilitates information sharing between applications, enabling applications such as screenshots. However, it also enables user-level malware to log a user's keystrokes or scrape a user's screen output. In this work, we explore a design called SwitchMan to protect a user's I/O paths against user-level malware attacks. SwitchMan assigns each user with two accounts: a regular one for normal operations and a protected one for inputting and outputting sensitive data. Each user account runs under a separate virtual terminal. Malware running under a user's regular account cannot access sensitive input/output under a user's protected account. At the heart of SwitchMan lies a secure protocol that enables automatic account switching when an application requires sensitive input/output from a user. Our performance evaluation shows that SwitchMan adds acceptable performance overhead. Our security and usability analysis suggests that SwitchMan achieves a better tradeoff between security and usability than existing solutions.