Biblio

In state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.

Recently, several Internet of Things Tools have been created, contributing to growing network loads. To refrain from IoT should use the idea of cognitive radio networks because of the lack of bandwidth. This article presents much of the research discusses the distribution of channels and preparation of packets when combining cognitive radio networks with IoT technology and we are further discussing the spectrum-based Features and heterogeneity in cognitive IoT Security. Surveying the research performed in this field reveals that the work performed is still developing. A variety of inventions and experiments are part of its initial phases.

Physical-layer security (PLS) for an underlay cognitive radio network (CRN)-based simultaneous wireless information and power transfer (SWIPT) over cascaded κ-µ fading channels is investigated. The network is composed of a pair of secondary users (SUs), a primary user (PU) receiver, and an eavesdropper attempting to intercept the data shared by the SUs. To improve the SUs’ data transmission security, we assume a full-duplex (FD) SU destination, which employs energy harvesting (EH) to extract the power required for generating jamming signals to be emitted to confound the eavesdropper. Two scenarios are presented and compared; harvesting and non-harvesting eavesdropper. Moreover, a trade-off between the system’s secrecy and reliability is explored. PLS is studied in terms of the probability of non-zero secrecy capacity and the intercept probability, whereas the reliability is studied in terms of the outage probability. Results reveal the great impact of jamming over the improvement of the SUs’ secrecy. Additionally, our work indicates that studying the system’s secrecy over cascaded channels has an influence on the system’s PLS that cannot be neglected.

We investigate cognitive radio networks where the unlicensed sender operates in the overlay mode to relay the information of the licensed transmitter as well as send its individual information. To secure information broadcasted by the unlicensed sender against the wire-tapper, we invoke jammers to limit eavesdropping. Also, to exploit efficiently radio frequency energy in licensed signals, we propose the unlicensed sender and all jammers to scavenge this energy source. To assess the security measures of both licensed and unlicensed networks, we first derive rigorous closed-form formulas of licensed/unlicensed secrecy outage probabilities. Next, we validate these formulas with Monte-Carlo simulations before using them to achieve insights into the security capability of the proposed jamming-aided energy harvesting cognitive radio networks in crucial system parameters.

Based on the structure of turbo-polar codes, a secure symmetric encryption scheme is proposed to enhance information transmission security in this paper. This scheme utilizes interleaving at information bits and puncturing at parity bits for several times in the encoder. Correspondingly, we need to do the converse interleaving and fill zeros accurately at punctured position. The way of interleaving and puncturing is controlled by the private key of symmetric encryption, making sure the security of the system. The security of Secure Turbo-Polar Codes (STPC) is analyzed at the end of this paper. Simulation results are given to shown that the performance and complexity of Turbo-Polar Codes have little change after symmetric encryption. We also investigate in depth the influence of different remaining parity bit ratios on Frame Error Rate (FER). At low Signal to Noise Rate (SNR), we find it have about 0.6dB advantage when remaining parity bit ratio is between 1/20 and 1/4.

With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.

This paper for the first time investigate the security and reliability performance of ultra-reliable low-latency communication (URLLC) systems in the presence of randomly distributed eavesdroppers, where the impact of short blocklength codes and imperfect channel estimation are jointly considered. Based on the finite-blocklength information theory, we first derive a closed-form approximation of transmission error probability to describe the degree of reliability loss. Then, we also derive an asymptotic expression of intercept probability to characterize the security performance, where the impact of secrecy protected zone is also considered. Simulation and numerical results validate the accuracy of theoretical approximations, and illustrate the tradeoff between security and reliability. That is, the intercept probability of URLLC systems can be suppressed by loosening the reliability requirement, and vice versa. More importantly, the theoretical analysis and methodologies presented in this paper can offer some insights and design guidelines for supporting secure URLLC applications in the future 6G wireless networks.

Random number generators (RNGs) has an extensive application area from cryptography to simulation software. Piecewise linear one-dimensional (PL1D) maps are commonly preferred structures used as the basis of RNGs due to their theoretically proven chaotic behavior and ease of implementation. In this work, a skew-tent map based RNG is designed by using the chaotic sampling method in TSMC 180 nm CMOS process. Simulation data of the designed RNG is validated by the statistical randomness tests of the FIPS-140-2 and NIST 800-22 suites. The proposed RNG has three key features: the generated bitstreams can fulfill the randomness tests without using any post processing methods; the proposed RNG has immunity against external interference thanks to the chaotic sampling method; and higher bitrates (4.8 Mbit/s) can be achieved with relatively low power consumption (9.8 mW). Thus, robust RNG systems can be built for high-speed security applications with low power by using the proposed architecture.

In this paper, we propose a twice chaotic encryption scheme to improve the security of CO-OFDM/OQAM system. Simulation results show that the proposed scheme enhance the physical-layer security within the acceptable performance penalty.

We present an alternate-channel chaotic laser secure communication system to enhance information communication security and study its technical solution via combining chaos shift keying (CSK) and chaos masking (CM). Two coupled lasers and other two single lasers are introduced as a novel alternate-channel secure communication system, where one of two coupled lasers is modulated via CSK to encode a digital signal and the other of coupled lasers is used to emit a chaotic carrier to mask an information using CM. The two single lasers are used to decode CSK and CM information, respectively. And such CSK performance results in enhancement of CM secure performance because of in-time variation of the emitter' parameter as secret keys. The obtained numerical results show that the encoding and decoding can be successfully performed. The study is beneficial to chaotic cryptography and optics secure communication.

Because Bitcoin P2P networking is permissionless by the application requirement, it is vulnerable against networking threats based on identity/credential manipulations such as Sybil and spoofing attacks. The current Bitcoin implementation keeps track of its peer's networking misbehaviors through ban score. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS attacks but also vulnerable to a Defamation attack. In the Defamation attack, the network adversary can exploit the ban-score mechanism to defame innocent peers.

Mobile healthcare (mHealth) application and technologies have promised their cost-effectiveness to enhance healthcare quality, particularly in rural areas. However, the increased security incidents and leakage of patient data raise the concerns to address security risks and privacy issues of mhealth applications urgently. While recent mobile health applications that rely on password-based authentication cannot withstand password guessing and cracking attacks, several countermeasures such as One-Time Password (OTP), grid-based password, and biometric authentication have recently been implemented to protect mobile health applications. These countermeasures, however, can be thwarted by brute force attacks, man-in-the-middle attacks and persistent malware attacks. This paper proposed grid-based honey encryption by hybridising honey encryption with grid-based authentication. Compared to recent honey encryption limited in the hardening password attacks process, the proposed grid-based honey encryption can be further employed against shoulder surfing, smudge and replay attacks. Instead of rejecting access as a recent security defence mechanism in mobile healthcare applications, the proposed Grid-based Honey Encryption creates an indistinct counterfeit patient's record closely resembling the real patients' records in light of each off-base speculation legitimate password.

Direct-access attacks were initially considered as un-realistic threats in cyber security because the attacker can more easily mount other non-computerized attacks like cutting a brake line. In recent years, some research into direct-access attacks have been conducted especially in the automotive field, for example, research on an attack method that makes the ECU stop functioning via the CAN bus. The problem with existing risk quantification methods is that direct-access attacks seem not to be recognized as serious threats. To solve this problem, we propose a new risk quantification method by applying vulnerability evaluation criteria and by setting metrics. We also confirm that direct-access attacks not recognized by conventional methods can be evaluated appropriately, using the case study of an automotive system as an example of a cyber-physical system.

The era of big data continues to progress, and many new practices and applications are being advanced. One such application is big data in healthcare. In this application, big data, which includes patient information and measurements, must be transmitted and managed in smart and secure ways. In this study, we propose a novel big data cloud system, s2Cloud, standing for Smart and Secure Cloud. s2Cloud can enable health care systems to improve patient monitoring and help doctors gain crucial insights into their patients' health. This system provides an interactive website that allows doctors to effectively manage patients and patient records. Furthermore, both real-time and historical functions for big data management are supported. These functions provide visualizations of patient measurements and also allow for historic data retrieval so further analysis can be conducted. The security is achieved by protecting access and transmission of data via sign up and log in portals. Overall, the proposed s2Cloud system can effectively manage healthcare big data applications. This study will also help to advance other big data applications such as smart home and smart world big data practices.

Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline – grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team’s powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.

Throughout the years, honeypots have been very useful in tracking down attackers and preventing different types of cyber attacks on a very large scale. It's been almost 3 decades since the discover of honeypots and still more than 80% of the companies rely on this system because of intrusion detection features and low false positive rate. But with time, the attackers tend to start discovering loopholes in the system. Hence it is very important to be up to date with the technology when it comes to protecting a computing device from the emerging cyber attacks. Timely advancements in the security model provided by the honeypots helps in a more efficient use of the resource and also leads to better innovations in that field. The following paper reviews different methods of honeypot network and also gives an insight about the problems that those techniques can face along with their solution. Further it also gives the detail about the most preferred solution among all of the listed techniques in the paper.

Generally, the defense measures taken against new cyber-attack methods are insufficient for cybersecurity risk management. Contrary to classical attack methods, the existence of undiscovered attack types called’ zero-day attacks’ can invalidate the actions taken. It is possible with honeypot systems to implement new security measures by recording the attacker’s behavior. The purpose of the honeypot is to learn about the methods and tools used by the attacker or malicious activity. In particular, it allows us to discover zero-day attack types and develop new defense methods for them. Attackers have made protocols such as SSH (Secure Shell) and Telnet, which are widely used for remote access to devices, primary targets. In this study, SSHTelnet honeypot was established using Cowrie software. Attackers attempted to connect, and attackers record their activity after providing access. These collected attacker log records and files uploaded to the system are published on Github to other researchers1. We shared the observations and analysis results of attacks on SSH and Telnet protocols with honeypot.

What do we need to do to protect our personal information? IoT devices such as smartphones, smart watches, and home appliances are widespread. Encryption is required not only to prevent eavesdropping on communications but also to prevent information leakage from cloud services due to unauthorized access. Therefore, attention is being paid to fully homomorphic encryption (FHE) that allows addition and multiplication between ciphertexts. However, FHE with this convenient function has a drawback that the encryption requires huge volume of calculation and the ciphertext is large. Therefore, if FHE is used on a device with limited computational resources such as an IoT device, the load on the IoT device will be too heavy. In this research, we propose a system that can safely and effectively utilize data without imposing a load on IoT devices. In this system, somewhat homomorphic encryption (SHE), which is a lightweight cryptosystem compared with FHE, is combined with FHE. The results of the experiment confirmed that the load on the IoT device can be reduced to approximately 1/1400 compared to load of the system from previous research.

Hash constructions are used in cryptographic algorithms from very long. Features of Hashes that gives the applications the confidence to use them in security methodologies is “forward secrecy” Forward secrecy comes from one-way hash functions. Examples of earlier hash designs include SHA-3, MD-5, SHA-I, and MAME. Each of these is having their proven record to produce the security for the communication between unconstrained devices. However, this is the era of Internet of Things (IoT) and the requirement of lightweight hash designs are the need of hour. IoT mainly consists of constrained devices. The devices in IoT are having many constrained related to battery power, storage and transmission range. Enabling any security feature in the constrained devices is troublesome. Constrained devices under an IoT environment can work only with less complex and lightweight algorithms. Lightweight algorithms take less power to operate and save a lot of energy of the battery operated devices. SPONGENT, QUARK, HASH-ONE, PHOTON, are some of the well-known lightweight hash designs currently providing security to the IoT devices. In this paper, the authors will present an analysis of the functioning of different lightweight hash designs as well as their suitability to the IoT environment.

Cryptographic algorithms are playing an important role in the information security field. Strong and unbreakable algorithms provide high security and good throughput. The strength of any encryption algorithm is basically based on the degree of difficulty to obtain the encryption key by such cyber-attacks as brute. It is supposed that the bigger the key size, the more difficult it is to compute the key. But increasing the key size will increase both the computational complexity and the processing time of algorithms. In this paper, we proposed a reliable, effective, and more secure symmetric stream cipher algorithm for encryption and decryption called Symmetric Cipher based on Key Hashing Algorithm (SCKHA). The idea of this algorithm is based on hashing and splitting the encryption symmetric key. Hashing the key will hide the encrypted key to prevent any intruder from forging the hash code, and, thus, it satisfies the purpose of security, authentication, and integrity for a message on the network. In addition, the algorithm is secure against a brute-force attack by increasing the resources it takes for testing each possible key. Splitting the hashed value of the encryption key will divide the hashed key into two key chunks. The encryption process performed using such one chunk based on some calculations on the plaintext. This algorithm has three advantages that are represented in computational simplicity, security and efficiency. Our algorithm is characterized by its ability to search on the encrypted data where the plaintext character is represented by two ciphertext characters (symbols).

The rapid advancement of IoT technologies has led to its flexible adoption in battle field networks, known as Internet of Battlefield Things (IoBT) networks. One important application of IoBT networks is the weather sensory network characterized with a variety of weather, land and environmental sensors. This data contains hidden trends and correlations, needed to provide situational awareness to soldiers and commanders. To interpret the incoming data in real-time, machine learning algorithms are required to automate strategic decision-making. Existing solutions are not well-equipped to provide the fine-grained feedback to military personnel and cannot facilitate a scalable, end-to-end platform for fast unlabeled data collection, cleaning, querying, analysis and threats identification. In this work, we present a scalable end-to-end IoBT data driven platform for SVAD (Storage, Visualization, Anomaly Detection) analysis of heterogeneous weather sensor data. Our SVAD platform includes extensive data cleaning techniques to denoise efficiently data to differentiate data from anomalies and noise data instances. We perform comparative analysis of unsupervised machine learning algorithms for multi-variant data analysis and experimental evaluation of different data ingestion pipelines to show the ability of the SVAD platform for (near) real-time processing. Our results indicate impending turbulent weather conditions that can be detected by early anomaly identification and detection techniques.

Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect them to be fast, transparent, cheap, reliable and global. Recent technologies such as distributed ledgers create opportunities for near-real-time, cheaper and more transparent payments. However, in order to achieve a global payment system, payments should be possible not only within one ledger, but also across different ledgers and geographies.In this paper we propose Secure Payments with Overlay Networks (SPON), a service that enables global payments across multiple ledgers by combining the transaction exchange provided by the Interledger protocol with an intrusion-tolerant overlay of relay nodes to achieve (1) improved payment latency, (2) fault-tolerance to benign failures such as node failures and network partitions, and (3) resilience to BGP hijacking attacks. We discuss the design goals and present an implementation based on the Interledger protocol and Spines overlay network. We analyze the resilience of SPON and demonstrate through experimental evaluation that it is able to improve payment latency, recover from path outages, withstand network partition attacks, and disseminate payments fairly across multiple ledgers. We also show how SPON can be deployed to make the communication between different ledgers resilient to BGP hijacking attacks.

The Border Gateway Protocol (BGP) is in charge of the route exchange at the Internet scale. Anomalies in BGP can have several causes (mis-configuration, outage and attacks). These anomalies are classified into large or small scale anomalies. Machine learning models are used to analyze and detect anomalies from the complex data extracted from BGP behavior. Two types of data representation can be used inside the machine learning models: a graph representation of the network (graph features) or a statistical computation on the data (statistical features). In this paper, we evaluate and compare the accuracy of machine learning models using graph features and statistical features on both large and small scale BGP anomalies. We show that statistical features have better accuracy for large scale anomalies, and graph features increase the detection accuracy by 15% for small scale anomalies and are well suited for BGP small scale anomaly detection.

The Internet-of-Things (IoT) paradigm at large continues to be compromised, hindering the privacy, dependability, security, and safety of our nations. While the operational security communities (i.e., CERTS, SOCs, CSIRT, etc.) continue to develop capabilities for monitoring cyberspace, tools which are IoT-centric remain at its infancy. To this end, we address this gap by innovating an actionable Cyber Threat Intelligence (CTI) feed related to Internet-scale infected IoT devices. The feed analyzes, in near real-time, 3.6TB of daily streaming passive measurements ( ≈ 1M pps) by applying a custom-developed learning methodology to distinguish between compromised IoT devices and non-IoT nodes, in addition to labeling the type and vendor. The feed is augmented with third party information to provide contextual information. We report on the operation, analysis, and shortcomings of the feed executed during an initial deployment period. We make the CTI feed available for ingestion through a public, authenticated API and a front-end platform.