Biblio

The existing research on the Internet of Things(IoT) security mainly focuses on attack and defense on a single protocol layer. Increasing and ubiquitous use of loT also makes it vulnerable to many attacks. An attacker try to performs the intelligent, brutal and stealthy attack that can reduce the risk of being detected. In these kinds of attacks, the attackers not only restrict themselves to a single layer of protocol stack but they also try to decrease the network performance and throughput by a simultaneous and coordinated attack on different layers. A new class of attacks, termed as cross-layer attack became prominent due to lack of interaction between MAC, routing and upper layers. These attacks achieve the better effect with reduced cost. Research has been done on cross-layer attacks in other domains like Cognitive Radio Network(CRN), Wireless Sensor Networks(WSN) and ad-hoc networks. However, our proposed scheme of cross-layer attack in IoT is the first paper to the best of our knowledge. In this paper, we have proposed Rank Manipulation and Drop Delay(RMDD) cross-layer attack in loT, we have investigated how small intensity attack on Routing protocol for low power lossy networks (RPL) degrades the overall application throughput. We have exploited the Rank system of the RPL protocol to implement the attacks. Rank is given to each node in the graph, and it shows its position in the network. If the rank could be manipulated in some manner, then the network topology can be modified. Simulation results demonstrate that the proposed attacks degrade network performance very much in terms of the throughput, latency, and connectivity.

Internet of Things (IoT) in military setting generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices to wearable combat uniforms), which are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and bening application. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. for evaluation of proposed malware detection approaches).

Exploits based on ROP (Return-Oriented Programming) are increasingly present in advanced attack scenarios. Testing systems for ROP-based attacks can be valuable for improving the security and reliability of software. In this paper, we propose ROPMATE, the first Visual Analytics system specifically designed to assist human red team ROP exploit builders. In contrast, previous ROP tools typically require users to inspect a puzzle of hundreds or thousands of lines of textual information, making it a daunting task. ROPMATE presents builders with a clear interface of well-defined and semantically meaningful gadgets, i.e., fragments of code already present in the binary application that can be chained to form fully-functional exploits. The system supports incrementally building exploits by suggesting gadget candidates filtered according to constraints on preserved registers and accessed memory. Several visual aids are offered to identify suitable gadgets and assemble them into semantically correct chains. We report on a preliminary user study that shows how ROPMATE can assist users in building ROP chains.

Public cloud data storage services were considered as a potential alternative to store low-cost digital data in the short term. They are offered by different providers on the Internet. Some providers offer limited free plans for the users who are starting the service. However, data security concern arises when data stored are considered as a valuable asset. This study explores the usage of secret sharing scheme: Rabin's IDA and Shamir's SSA to implement a tool called dCloud for file protection stored in public cloud storage in a seamless way. It addresses data security by hiding its complexities when targeting ordinary non-technical users. The secret key is automatically generated by dCloud in a secure random way on Rabin's IDA. Shamir's SSA completes the process through dispersing the key into each of Rabin's IDA output files. Moreover, the Hash value of the original file is added to each of those output files to confirm the integrity of the file during reconstruction. Besides, the authentication key is used to communicate with all of the defined service providers during storage and reconstruction as well. It is stored into local secure key-store. By having a key to access the key-store, an ordinary non-technical user will be able to use dCloud to store and retrieve targeted file within defined public cloud storage services securely.

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.

Design for Testability (DfT) techniques allow devices to be tested at various levels of the manufacturing process. Scan architecture is a dominantly used DfT technique, which supports a high level of fault coverage, observability and controllability. However, scan architecture can be used by hardware attackers to gain critical information stored within the device. The security threats due to an unrestricted access provided by scan architecture has to be addressed to ensure hardware security. In this work, a solution based on the Clock and Data Recovery (CDR) method has been presented to authenticate users and limit the access to the scan architecture to authorized users. As compared to the available solution the proposed method presents a robust performance and reduces the area overhead by more than 10%.

This study examines the secure transition for robotic surgery session. Surgeon sends set of instructions as data. The data is encapsulated with surgeon secure signature to conform surgeon identity. At the same time, patient information sends to the surgeon as a secure row of frames to estimate patient situation dependent on the real medical reports. Elliptic Curve Diffie-Hellman is use as an asymmetric encryption method. Here the session between surgeon console and interactive robot arm was achieved and supported with four secret keys. Two private keys are chosen on each side and two public keys are calculated from these private keys. These results indicate that the level of the security was improved by use asymmetric encryption rather than symmetric encryption. And by contributed four secret keys the patient information must be safer.

Implementing a secure development lifecycle (SDL) presents increasing challenges to software developers as they must ensure software correctly integrates both underlying operating system security features while also managing dependencies on third-party libraries or executables. There are a growing number of security functions that require a close integration between the OS security features and software builds to ensure strong protection. Furthermore, as software platforms grow in complexity, they present many opportunities for misconfigurations and inadequate defenses. This challenge is especially prevalent for industrial control systems (ICS), which oten depend on both legacy sotware platforms, or out of date operating systems. This paper presents the AttackSurface Host Analyzer (AHA) tool, which is used to assess the security of a software platform through its integration with a host operating system. The tool collects data from the various platforms running on an OS, evaluates an array of security properties, and then introduces metrics and visualizations to provide feedback on the system's attack surface based on the external interconnections and the completeness of the available security protections. The paper then explores the attack surface of a variety of industry-standard ICS platforms to provide insight into the current degree of protection enabled by them.

Nowadays, Information Technology is one of the important parts of human life and also of organizations. Organizations face problems such as IT problems. To solve these problems, they have to improve their security sections. Thus there is a need for security assessments within organizations to ensure security conditions. The use of security standards and general metric can be useful for measuring the safety of an organization; however, it should be noted that the general metric which are applied to businesses in general cannot be effective in this particular situation. Thus it's important to select metric standards for different businesses to improve both cost and organizational security. The selection of suitable security measures lies in the use of an efficient way to identify them. Due to the numerous complexities of these metric and the extent to which they are defined, in this paper that is based on comparative study and the benchmarking method, taxonomy for security measures is considered to be helpful for a business to choose metric tailored to their needs and conditions.

Public key cryptography or asymmetric keys are widely used in the implementation of data security on information and communication systems. The RSA algorithm (Rivest, Shamir, and Adleman) is one of the most popular and widely used public key cryptography because of its less complexity. RSA has two main functions namely the process of encryption and decryption process. Digital Signature Algorithm (DSA) is a digital signature algorithm that serves as the standard of Digital Signature Standard (DSS). DSA is also included in the public key cryptography system. DSA has two main functions of creating digital signatures and checking the validity of digital signatures. In this paper, the authors compare the computational times of RSA and DSA with some bits and choose which bits are better used. Then combine both RSA and DSA algorithms to improve data security. From the simulation results, the authors chose RSA 1024 for the encryption process and added digital signatures using DSA 512, so the messages sent are not only encrypted but also have digital signatures for the data authentication process.

Quantum technology is a new field of physics and engineering. In emerging areas like Quantum Cryptography, Quantum Computing etc, Quantum circuits play a key role. Quantum circuit is a model for Quantum computation, the computation process of Quantum gates are based on reversible logic. Encoder and Decoder are designed using Quantum gates, and synthesized in the QCAD simulator. Quantum error correction (QEC) is essential to protect quantum information from errors due to quantum noise and decoherence. It is also use to achieve fault-tolerant quantum computation that deals with noise on stored information, faulty quantum gates and faulty measurements.

This paper deals with the modeling and control of the NEREIDA wave generation power plant installed in Mutriku, Spain. This kind of Oscillating Water Column (OWC) plants usually employ a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The stalling behavior of the Wells turbine limits the generated power. In this context, a sliding mode rotational speed control is proposed to help avoiding this phenomenon. This will regulate the speed by means of the Rotor Side Converter (RSC) of the Back-to-Back converter governing the generator. The results of the comparative study show that the proposed control provides a higher generated power compared to the uncontrolled case.

Evaluating new technological developments for energy systems is becoming more and more complex. The overall application environment is a continuously growing and interconnected cyber-physical system so that analytical assessment is practically impossible to realize. Consequently, new solutions must be evaluated in simulation studies. Due to the interdisciplinarity of the simulation scenarios, various heterogeneous tools must be connected. This approach is known as co-simulation. During the last years, different approaches have been developed or adapted for applications in energy systems. In this paper, two co-simulation approaches are compared that follow generic, versatile concepts. The tool MOSAIK, which has been explicitly developed for the purpose of co-simulation in complex energy systems, is compared to the High Level Architecture (HLA), which possesses a domain-independent scope but is often employed in the energy domain. The comparison is twofold, considering the tools’ conceptual architectures as well as results from the simulation of representative test cases. It suggests that MOSAIK may be the better choice for entry-level, prototypical co-simulation while HLA is more suited for complex and extensive studies.

Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.

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.

The Internet-of-things (IoT) holds a lot of benefits to our lives by removing menial tasks and improving efficiency of everyday objects. You are trusting your personal data and device control to the manufactures and you may not be aware of how much risk your putting your privacy at by sending your data over the internet. The internet-of-things may not be as secure as you think when the devices used are constrained by a lot of variables which attackers can exploit to gain access to your data / device and anything they connected to and as the internet-of-things is all about connecting devices together one weak point can be all it takes to gain full access. In this paper we have a look at the current advances in IoT security and the most efficient methods to protect IoT devices.

Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers\guillemotright solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

The storage efficiency of hash codes and their application in the fast approximate nearest neighbor search, along with the explosion in the size of available labeled image datasets caused an intensive interest in developing learning based hash algorithms recently. In this paper, we present a learning based hash algorithm that utilize ordinal information of feature vectors. We have proposed a novel mathematically differentiable approximation of argmax function for this hash algorithm. It has enabled seamless integration of hash function with deep neural network architecture which can exploit the rich feature vectors generated by convolutional neural networks. We have also proposed a loss function for the case that the hash code is not binary and its entries are digits of arbitrary k-ary base. The resultant model comprised of feature vector generation and hashing layer is amenable to end-to-end training using gradient descent methods. In contrast to the majority of current hashing algorithms that are either not learning based or use hand-crafted feature vectors as input, simultaneous training of the components of our system results in better optimization. Extensive evaluations on NUS-WIDE, CIFAR-10 and MIRFlickr benchmarks show that the proposed algorithm outperforms state-of-art and classical data agnostic, unsupervised and supervised hashing methods by 2.6% to 19.8% mean average precision under various settings.

The gap is widening between the processor clock speed of end-system architectures and network throughput capabilities. It is now physically possible to provide single-flow throughput of speeds up to 100 Gbps, and 400 Gbps will soon be possible. Most current research into high-speed data networking focuses on managing expanding network capabilities within datacenter Local Area Networks (LANs) or efficiently multiplexing millions of relatively small flows through a Wide Area Network (WAN). However, datacenter hyper-convergence places high-throughput networking workloads on general-purpose hardware, and distributed High-Performance Computing (HPC) applications require time-sensitive, high-throughput end-to-end flows (also referred to as ``elephant flows'') to occur over WANs. For these applications, the bottleneck is often the end-system and not the intervening network. Since the problem of the end-system bottleneck was uncovered, many techniques have been developed which address this mismatch with varying degrees of effectiveness. In this survey, we describe the most promising techniques, beginning with network architectures and NIC design, continuing with operating and end-system architectures, and concluding with clean-slate protocol design.

For the past decade, security experts have warned that malicious engineers could modify hardware designs to include hardware backdoors (trojans), which, in turn, could grant attackers full control over a system. Proposed defenses to detect these attacks have been outpaced by the development of increasingly small, but equally dangerous, trojans. To thwart trojan-based attacks, we propose a novel architecture that maps the security-critical portions of a processor design to a one-time programmable, LUT-free fabric. The programmable fabric is automatically generated by analyzing the HDL of targeted modules. We present our tools to generate the fabric and map functionally equivalent designs onto the fabric. By having a trusted party randomly select a mapping and configure each chip, we prevent an attacker from knowing the physical location of targeted signals at manufacturing time. In addition, we provide decoy options (canaries) for the mapping of security-critical signals, such that hardware trojans hitting a decoy are thwarted and exposed. Using this defense approach, any trojan capable of analyzing the entire configurable fabric must employ complex logic functions with a large silicon footprint, thus exposing it to detection by inspection. We evaluated our solution on a RISC-V BOOM processor and demonstrated that, by providing the ability to map each critical signal to 6 distinct locations on the chip, we can reduce the chance of attack success by an undetectable trojan by 99%, incurring only a 27% area overhead.

Vehicular Ad-hoc Networks (VANETs) are a subset of Mobile Ad-hoc Networks (MANETs). They are deployed to introduce the ability of inter-communication among vehicles in order to guarantee safety and provide services for people while driving. VANETs are exposed to many types of attacks like denial of service, spoofing, ID disclosure and Sybil attacks. In this paper, a novel lightweight approach for preventing Sybil attack in VANETs is proposed. The presented protocol scheme uses symmetric key encryption and authentication between Road Side Units (RSUs) and vehicles on the road so that no malicious vehicle could gain more than one identity inside the network. This protocol does not need managers for Road Side Units (RSUs) or Certification Authority (CA) and uses minimum amount of messages exchanged with RSU making the scheme efficient and effective.

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present two case studies in water distribution and transportation a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

In Diffie-Hellman Key Exchange (DHKE), two parties need to communicate to each other by sharing their secret key (cipher text) over an unsecure communication channel. An adversary or cryptanalyst can easily get their secret keys but cannot get the information (plaintext). Brute force is one the common tools used to obtain the secret key, but when the key is too large (etc. 1024 bits and 2048 bits) this tool is no longer suitable. Thus timing attacks have become more attractive in the new cryptographic era where networked embedded systems security present several vulnerabilities such as lower processing power and high deployment scale. Experiments on timing attacks are useful in helping cryptographers make security schemes more resistant. In this work, we timed the computations of the Discrete Log Hard Problem of the Diffie Hellman Key Exchange (DHKE) protocol implemented on an embedded system network and analyzed the timing patterns of 1024-bit and 2048-bit keys that was obtained during the attacks. We have chosen to implement the protocol on the Raspberry-pi board over U-BOOT Bare Metal and we used the GMP bignum library to compute numbers greater than 64 bits on the embedded system.

5G is envisioned as a transformation of the communications architecture towards multi-tenant, scalable and flexible infrastructure, which heavily relies on virtualised network functions and programmable networks. In particular, orchestration will advance one step further in blending both compute and data resources, usually dedicated to virtualisation technologies, and network resources into so-called slices. Although 5G security is being developed in current working groups, slice security is seldom addressed. In this work, we propose to integrate security in the slice life cycle, impacting its management and orchestration that relies on the virtualization/softwarisation infrastructure. The proposed security architecture connects the demands specified by the tenants through as-a-service mechanisms with built-in security functions relying on the ability to combine enforcement and monitoring functions within the software-defined network infrastructure. The architecture exhibits desirable properties such as isolating slices down to the hardware resources or monitoring service-level performance.