Biblio

With the wide application of video information, the protection of video information from illegal access has been widely investigated recently. An efficient selective encryption scheme for high efficiency video coding (HEVC) based on hyperchaotic Lorenz system is proposed. Firstly, the hyperchaotic Lorenz system is discretized and the generated chaotic state values are converted into chaotic pseudorandom sequences for encryption. The important syntax elements in HEVC are then selectively encrypted with the generated stream cipher. The experimental results show that the encrypted video is highly disturbed and the video information cannot be recognized. Through the analysis of objective index results, it is shown that the scheme is both efficient and security.

To share the recorded ECG data with the cardiologist in Golden Hours in an efficient and secured manner via tele-cardiology may save the lives of the population residing in rural areas of a country. This paper proposes an encryption-authentication scheme for secure the ECG data. The main contribution of this work is to generate a one-time padding key and deploying an encryption algorithm in authentication mode to achieve encryption and authentication. This is achieved by a water cycle optimization algorithm that generates a completely random one-time padding key and Triple Data Encryption Standard (3DES) algorithm for encrypting the ECG data. To validate the accuracy of the proposed encryption authentication scheme, experimental results were performed on standard ECG data and various performance parameters were calculated for it. The results show that the proposed algorithm improves security and passes the statistical key generation test.

Emerging non-volatile memory technology enables non-volatile main memory (NVMM) that can provide larger capacity and better energy-saving opportunities than DRAMs. However, its non-volatility raises security concerns, where the data in NVMMs can be taken if the memory is stolen. Memory encryption protects the data by limiting it always stays encrypted outside the processor boundary. However, the decryption latency before the data being used by the processor brings new performance burdens. Unlike DRAM-based main memory, such performance overhead worsens on the NVMM due to the slow latency. In this paper, we will introduce optimizations that can be used to re-design the encryption scheme. In our tests, our two new designs, 3-level split counter mode encryption and 8-block split counter mode encryption, improved performance by 26% and 30% at maximum and by 8% and 9% on average from the original encryption scheme, split counter encryption.

Implementing the color images encryption is a challenging field of the research for IOT applications. An exponential growth in imaging cameras in IOT uses makes it critical to design the robust image security algorithms. It is also observed that performance of existing encryption methods degrades under the presence of noisy environments. This is the major concern of evaluating the encryption method in this paper. The prime concern of this paper is to design the fast efficient color images encryption algorithm by designing an efficient and robustness RSA encryption algorithm. Method takes the advantage of both preprocessing and the Gaussian pyramid (GP) approach for encryption. To improve the performance it is proposed to use the LAB color space and implement the RSA encryption on luminance (L) component using the GP domain. The median filter and image sharpening is used for preprocessing. The goal is to improve the performance under highly noisy imaging environment. The performance is compared based on the crypto weights and on the basis of visual artifacts and entropy analysis. The decrypted outputs are again converted to color image output. Using the LAB color space is expected to improve the entropy performance of the image. Result of proposed encryption method is evaluated under the different types of the noisy attacks over the color images and also performance is compared with state of art encryption methods. Significant improvement speed of the algorithm is compared in terms of the elapsed time

With the rapid development of Internet technologies, emerging network environments have been discussed, such as Internet of Things. In this manuscript, we proposed a novel subset predicate encryption for the access control in Internet of Things. Compared with the existing subset predicate encryption schemes, the proposed scheme enjoy the better efficiency due to the short private key and the efficient decryption procedure.

In recent times, security and privacy concerns associated with IoT devices have caught the attention of research community. The problem of securing IoT devices is immensely aggravating due to advancement in technology. These IoT devices are resource-constraint i.e. in terms of power, memory, computation, etc., so they are less capable to secure themselves. So we need a better approach to secure IoT devices within the limited resources. Several studies state that for these lightweight IoT devices Elliptic Curve Cryptography (ECC) suits perfectly. But there are several elliptic curve domain parameter standards, which may be used for different security levels. When any ECC based product is deployed then the selection of a suitable elliptic curve standard according to usability is become very important. So we have to choose one suitable standard domain parameter for the required security level. In this paper, two different elliptic curve standard domain parameters named secp256k1 and secp192k1 proposed by an industry consortium named Standards for Efficient Cryptography Group (SECG) [1] are implemented and then analyzed their performances metrics. The performance of each domain parameter is measured in computation time.

In this paper, we extend the existing classification of signature models by Cao. To do so, we present a new signature classification framework and migrate the original classification to build an easily extendable faceted signature classification. We propose 20 new properties, 7 property families, and 1 signature classification type. With our classification, theoretically, up to 11 541 420 signature classes can be built, which should cover almost all existing signature schemes.

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.

This paper describes a Cyber Threat, Vulnerability and Defense Modeling and Simulation tool kit used for evaluation of systems and networks to improve cyber resiliency. This capability is used to help increase the resiliency of networks at various stages of their lifecycle, from initial design and architecture through the operation of deployed systems and networks. Resiliency of computer systems and networks to cyber threats is facilitated by the modeling of agile and resilient defenses versus threats and running multiple simulations evaluated against resiliency metrics. This helps network designers, cyber analysts and Security Operations Center personnel to perform trades using what-if scenarios to select resiliency capabilities and optimally design and configure cyber resiliency capabilities for their systems and networks.

Recent cyber attacks on the power grid have been of increasing complexity and sophistication. In order to understand the impact of cyber-attacks on the power system resiliency, it is important to consider an holistic cyber-physical system specially with increasing industrial automation. In this work, device level resilience properties of the various controllers and their impact on the microgrid resiliency is studied. In addition, a cyber-physical resiliency metric considering vulnerabilities, system model, and device level properties is proposed. A use case is presented inspired by the recent Ukraine cyber-attack. A use case has been presented to demonstrate application of the developed cyber-physical resiliency metric to enhance situational awareness of the operator, and enable better control actions to improve resiliency.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

Highly distributed self-organizing CPS exhibit coordination schemata and communication requirements which are similar to structured overlay networks. To determine the resilience of such overlays, we analyze the connectivity of Kademlia, which has been successfully deployed in multiple applications with several thousands of nodes, e.g., BitTorrent. We measure the network connectivity within extensive simulations for different network configurations and present selected results.

As the public cloud becomes one of the leading ways in data sharing nowadays, data confidentiality and user privacy are increasingly critical. Partially policy-hidden ciphertext policy attribute-based encryption (CP-ABE) can effectively protect data confidentiality while reducing privacy leakage by hiding part of the access structure. However, it cannot satisfy the need of data sharing in the public cloud with complex users and large amounts of data, both in terms of less expressive access structures and limited granularity of policy hiding. Moreover, the verification of access right to shared data and correctness of decryption are ignored or conducted by an untrusted third party, and the prime-order groups are seldom considered in the expressive policy-hidden schemes. This paper proposes a fully policy-hidden CP-ABE scheme constructed on LSSS access structure and prime-order groups for public cloud data sharing. To help users decrypt, HVE with a ``convert step'' is applied, which is more compatible with CP-ABE. Meanwhile, decentralized credible verification of access right to shared data and correctness of decryption based on blockchain are also provided. We prove the security of our scheme rigorously and compare the scheme with others comprehensively. The results show that our scheme performs better.

IP Identification (IP ID) is an IP header field that identifies a data packet in the network to distinguish its fragments from others during the reassembly process. Random generated IP ID field could be used as a covert channel by embedding hidden bits within it. This paper uses the support vector machine (SVM) while enabling a features reduction procedure for investigating to what extend could the entropy feature of the IP ID covert channel affect the detection. Then, an entropy-based SVM is employed to evaluate the roles of the IP ID covert channel hidden bits on detection. Results show that, entropy is a distinct discrimination feature in classifying and detecting the IP ID covert channel with high accuracy. Additionally, it is found that each of the type, the number and the position of the hidden bits within the IP ID field has a specified influence on the IP ID covert channel detection accuracy.

The Digital Signature Algorithm (DSA) is a representative of a family of digital signature algorithms that are known to have a number of subliminal channels for covert data transmission. The capacity of these channels stretches from several bits (narrowband channels) to about 256 or so bits (a broadband channel). There are a couple of methods described in the literature to prevent the usage of the broadband channel with the help of a warden. In the present paper, we discuss some weaknesses of the known methods and suggest a solution that is free of the weaknesses and eliminates the broadband covert channel. Our solution also requires a warden who does not participate in signature generation and is able to check any signed message for the absence of the covert communication.

Antivirus software is considered to be the primary line of defense against malicious software in modern computing systems. The purpose of this paper is to expose exploitation that can evade Antivirus software that uses signature-based detection algorithms. In this paper, a novel approach was proposed to change the source code of a common Metasploit-Framework used to compile the reverse shell payload without altering its functionality but changing its signature. The proposed method introduced an additional stage to the shellcode program. Instead of the shellcode being generated and stored within the program, it was generated separately and stored on a remote server and then only accessed when the program is executed. This approach was able to reduce its detectability by the Antivirus software by 97% compared to a typical reverse shell program.

Malicious hackers breach security perimeters, cause infrastructure disruptions as well as steal proprietary information, financial data, and violate consumers' privacy. Protection of the whole organization by using the firm's security officers can be besieged with faulty warnings. Engineers must shift from console to console to put together investigative clues as a result of today's fragmented security technologies that cause frustratingly sluggish investigations. Endpoint Detection and Response (EDR) solutions adds an extra layer of protection to prevent an endpoint action into a breach. EDR is the region's foremost detection and response tool that combines endpoint and network data to recognize and respond to sophisticated threats. Offering unrivaled security and operational effectiveness, it integrates prevention, investigation, detection, and responding in a single platform. EDR provides enterprise coverage and uninterrupted defense with its continuous monitoring and response to threats. We have presented a comprehensive review of existing EDRs through various security layers that includes detection, response and management capabilities which enables security teams to have unified end-to-end corporate accessibility, powerful analytics along with additional features such as web threat scan, external device scan and automatic reaction across the whole technological tower.

Web application vulnerability is one of the major causes of cyber attacks. Cyber criminals exploit these vulnerabilities to inject malicious commands to the unsanitized user input in order to bypass authentication of the database through some cyber-attack techniques like cross site scripting (XSS), phishing, Structured Query Language Injection Attack (SQLIA), malware etc., Although many research works have been conducted to resolve the above mentioned attacks, only few challenges with respect to SQLIA could be resolved. Ensuring security against complete set of malicious payloads are extremely complicated and demanding. It requires appropriate classification of legitimate and injected SQL commands. The existing approaches dealt with limited set of signatures, keywords and symbols of SQL queries to identify the injected queries. This work focuses on extracting SQL injection patterns with the help of existing parsing and tagging techniques. Pattern-based tags are trained and modeled using Multi-layer Perceptron which significantly performs well in classification of queries with accuracy of 94.4% which is better than the existing approaches.

Radio-frequency (RF) sensing is a key technology for designing intelligent and secure wireless networks with high spectral efficiency and environment-aware adaptation capabilities. However, existing sensing techniques can extract only limited information from RF signals or assume that the RF signals are generated by certain known protocols. As a result, their applications are limited if proprietary protocols or encryption methods are adopted, or in environments subject to errors such as unintended interference. To address this challenge, we study protocol-agnostic cross-layer sensing to extract high-layer protocol information from raw RF samples without any a priori knowledge of the protocols. First, we present a framework for protocol-agnostic sensing for over-the-air (OTA) RF signals, by taking packet boundary recognition (PBR) as an example. The framework consists of three major components: OTA Signal Generator, Agnostic RF Sink, and Ground Truth Generator. Then, we develop a software-defined testbed using USRP SDRs, with eleven benchmark statistical algorithms implemented in the Agnostic RF Sink, including Kullback-Leibler divergence and cross-power spectral density, among others. Finally, we test the effectiveness of these statistical algorithms in PBR on OTA RF samples, considering a wide variety of transmission parameters, including modulation type, transmission distance, and packet length. It is found that none of these benchmark statistical algorithms can achieve consistently high PBR rate, and new algorithms are required particularly in next-generation low-latency wireless systems.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.

Wireless Sensor Networks (WSN) are gaining popularity as being the backbone of Cyber physical systems, IOT and various data acquisition from sensors deployed in remote, inaccessible terrains have remote deployment. However due to remote deployment, WSN is an adhoc network of large number of sensors either heli-dropped in inaccessible terrain like volcanoes, Forests, border areas are highly energy deficient and available in large numbers. This makes it the right soup to become vulnerable to various kinds of Security attacks. The lack of energy and resources makes it deprived of developing a robust security code for mitigation of various kinds of attacks. Many attempts have been made to suggest a robust security Protocol. But these consume so much energy, bandwidth, processing power, memory and other resources that the sole purpose of data gathering from inaccessible terrain from energy deprived sensors gets defeated. This paper makes an attempt to study the types of attacks on different layers of WSN and the examine the recent trends in development of various security protocols to mitigate the attacks. Further, we have proposed a simple, lightweight but powerful security protocol known as Simple Sensor Security Protocol (SSSP), which captures the uniqueness of WSN and its isolation from internet to develop an energy efficient security solution.

Security of data and information in servers connected to networks that provide services to user computers, is the most important thing to maintain data privacy and security in network security management mechanisms. Weaknesses in the server security system can be exploited by intruders to disrupt the security of the server. One way to maintain server security is to implement an intrusion detection system using the Intrusion Detection System. This research is experimenting to create a security system prototype, monitoring, and evaluating server security systems using Snort and alert notifications that can improve security monitoring for server security. The system can detect intrusion attacks and provide warning messages and attack information through the Intrusion Detection System monitoring system. The results show that snort and alert notifications on the security server can work well, efficiently, and can be handled quickly. Testing attacks with Secure Shell Protocol and File Transfer Protocol Brute Force, Ping of Death and scanning port attacks requires a detection time of no more than one second, and all detection test results are detected and send real-time notification alerts to the Administrator.

This work investigates entropy-based prevention of Distributed Denial-of-Service (DDoS) attacks for Software Defined Networks (SDN). The experiments are conducted on a virtual SDN testbed setup within Mininet, a Linux-based network emulator. An arms race iterates on the SDN testbed between offense, launching botnet-based DDoS attacks with progressive sophistications, and defense who is deploying SDN controls with emerging technologies from other faucets of cyber engineering. The investigation focuses on the transmission control protocol’s synchronize flood attack that exploits vulnerabilities in the three-way TCP handshake protocol, to lock up a host from serving new users.The defensive strategy starts with a common packet filtering-based design from the literature to mitigate attacks. Utilizing machine learning algorithms, SDNs actively monitor all possible traffic as a collective dataset to detect DDoS attacks in real time. A constant upgrade to a stronger defense is necessary, as cyber/network security is an ongoing front where attackers always have the element of surprise. The defense further invests on entropy methods to improve early detection of DDoS attacks within the testbed environment. Entropy allows SDNs to learn the expected normal traffic patterns for a network as a whole using real time mathematical calculations, so that the SDN controllers can sense the distributed attack vectors building up before they overwhelm the network.This work reveals the vulnerabilities of SDNs to stealthy DDoS attacks and demonstrates the effectiveness of deploying entropy in SDN controllers for detection and mitigation purposes. Future work includes provisions to use these entropy detection methods, as part of a larger system, to redirect traffic and protect networks dynamically in real time. Other types of DoS, such as ransomware, will also be considered.

Technological progress has changed our world beyond recognition. However, along with the incredible benefits and conveniences we have received new dangers and risks. Mankind is increasingly becoming hostage to information technology and cyber world. Recently, cybercrime is one of the top 10 risks to sustainable development in the world. It poses serious new challenges to global security and economy. The aim of the article is to obtain an assessment of some of the financial effects of modern IT crimes based on an analysis of the main aspects of monetary costs and the hidden economic impact of cybercrime. A multifactor regression model has been proposed to determine the contribution of the cost of the main consequences of IT incidents: business disruption, information loss, revenue loss and equipment damage caused by different types of cyberattacks worldwide in 2019 to total cost of cyberattacks. Information loss has been found to have a major impact on the total cost of cyberattacks, reducing profits and incurring additional costs for businesses. It was built a canonical model for identifying the dependence of total submission to ID ransomware, total cost of cybercrime and the main indicators of economic development for the TOP-10 countries. There is a significant correlation between two sets of indicators, in particular, it is confirmed that most cyberattacks target countries - countries with a high level of development, and the consequences of IT crimes are more significant for low-income countries.

As it is easy to ensure the confidentiality of users on the Dark Web, malware and exploit kits are sold on the market, and attack methods are discussed in forums. Some services provide IoT Botnet to perform distributed denial-of-service (DDoS as a Service: DaaS), and it is speculated that the purchase of these services is made on the Dark Web. By crawling such information and storing it in a database, threat intelligence can be obtained that cannot otherwise be obtained from information on the Surface Web. However, crawling sites on the Dark Web present technical challenges. For this paper, we implemented a crawler that can solve these challenges. We also collected information on markets and forums on the Dark Web by operating the implemented crawler. Results confirmed that the dataset collected by crawling contains threat intelligence that is useful for analyzing cyber attacks, particularly those related to IoT Botnet and DaaS. Moreover, by uncovering the relationship with security reports, we demonstrated that the use of data collected from the Dark Web can provide more extensive threat intelligence than using information collected only on the Surface Web.