Biblio

In a Leading field of Information Technology moreover make information Security a unified piece of it. To manage security, Authentication assumes a significant part. Biometric is the physical unique identification as well as Authentication for third party. We are proposed the Security model for preventing many attacks so we are used Inner most layer as a 3DES (Triple Encryption standard) Cryptography algorithm that is providing 3-key protection as 64-bit And the outer most layer used the MD5 (Message Digest) Algorithm. i. e. Providing 128 – bit protection. As well as we are using Fingerprint Identification as a physical Security that used in third party remote integrity auditing, and remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depends on identity-based cryptography, which works on the convoluted testament the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.

Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.

Increasingly, the transportation industry has moved towards automation to improve safety, fuel efficiency, and system productivity. However, the increased scrutiny that automated vehicles (AV) face over functional safety has hindered the industry's unbridled confidence in self-driving technologies. As AVs are cyber-physical systems, they utilize distributed control to accomplish a range of safety-critical driving tasks. The Operation Systems (OS) serve as the core of these control systems. Therefore, their designs and implementation must incorporate ways to protect AVs against what must be assumed to be inevitable cyberattacks to meet the overall AV functional safety requirements. This paper investigates the connection between functional safety and cybersecurity in the context of OS. This study finds that risks due to delays can worsen by potential cybersecurity vulnerabilities through a case example of an automated vehicle following. Furthermore, attack surfaces and cybersecurity countermeasures for protecting OSs from security breaches are addressed.

Modern Industrial Automation & Control Systems (IACS) are essential part of the critical infrastructures and services. They are used in health, power, water, and transportation systems, and the impact of cyberattacks on IACS could be severe, resulting, for example, in damage to the environment, public or employee safety or health. Thus, building IACS safe and secure against cyberattacks is extremely important. The attacker model is one of the key elements in risk assessment and other security related information system management tasks. The aim of the study is to specify the attacker's profile based on the analysis of network and system events. The paper presents an approach to the selection of attacker's profile attributes from raw network and system events of the Linux OS. To evaluate the approach the experiments were performed on data collected within the Global CPTC 2019 competition.

Nowadays, source location privacy in underwater acoustic sensor networks (UASNs) has gained a lot of attention. The aim of source location privacy is to use specific technologies to protect the location of the source from being compromised. Among the many technologies available are fake packet technology, multi-path routing technology and so on. The fake packet technology uses a certain amount of fake packets to mask the transmission of the source packet, affecting the adversary's efficiency of hop-by-hop backtracking to the source. However, during the operation of the fake packet technology, the fake packet, and the source packet may interfere with each other. Focus on this, a fake packet time slot assignment-based source location privacy protection (FPTSA-SLP) scheme. The time slot assignment is adopted to avoid interference with the source packet. Also, a relay node selection method based on the handshake is further proposed to increase the diversity of the routing path to confuse the adversary. Compared with the comparison algorithm, the simulation results demonstrate that the proposed scheme has a better performance in safety time.

Multicore processors are currently the focus of new and future critical-system architectures. However, they introduce new problems in regards to safety and security requirements. Real-time control flow monitoring techniques were proposed as solutions to detect the most common types of program errors and security attacks. We propose a new way to use the latest debug and trace architectures to achieve full and isolated real-time control flow monitoring. We present an online trace decoder FPGA component as a solution in the search for scalable and portable monitoring architectures. Our FPGA accelerator achieves real-time CPU monitoring with only 8% of used resources in a Zynq-7000 FPGA.

The Internet of Vehicles (IoV) is a network that considers vehicles as intelligent machines. They interact and communicate with each other to improve the performance and safety of traffic. IoV solves certain problems, but it has some issues such as response time, which prompted researchers to propose the integration of Fog Computing into vehicular networks. In Vehicular Fog Computing (VFC), the services are provided at the edge of the network to increase data rate and reduce response time. However, in order to satisfy network users, the security and privacy of sensitive data should be guaranteed. Using pseudonyms instead of real identities is one of the techniques considered to preserve the privacy of users, however, this can push malicious vehicles to exploit such a process and launch the Sybil attack by creating several pseudonyms in order to perform various malicious activities. In this paper, we describe the Sybil attack effects on VFC networks and compare them to those in conventional networks, as well as identify the various existing methods for detecting this attack and determine if they are applicable to VFC networks.

The digitalization of safety-critical railroad infrastructure enables new types of attacks. This increases the need to integrate Information Technology (IT) security measures into railroad systems. For that purpose, we rely on a security architecture for a railway object controller which controls field elements that we developed in previous work. Our architecture enables the integration of security mechanisms into a safety-certified railway system. In this paper, we demonstrate the practical feasibility of our architecture by using a Trusted Platform Module (TPM) 2.0 and a Multiple Independent Levels of Safety and Security (MILS) Separation Kernel (SK) for our implementation. Our evaluation includes a test bed and shows how certification and homologation can be achieved.

Deep reinforcement learning has recently been adopted for robot behavior learning, where robot skills are acquired and adapted from data generated by the robot while interacting with its environment through a trial-and-error process. Despite this success, most model-free deep reinforcement learning algorithms learn a task-specific policy from a clean slate and thus suffer from high sample complexity (i.e., they require a significant amount of interaction with the environment to learn reasonable policies and even more to reach convergence). They also suffer from poor initial performance due to executing a randomly initialized policy in the early stages of learning to obtain experience used to train a policy or value function. Model based deep reinforcement learning mitigates these shortcomings. However, it suffers from poor asymptotic performance in contrast to a model-free approach. In this work, we investigate knowledge transfer from a model-based teacher to a task-specific model-free learner to alleviate executing a randomly initialized policy in the early stages of learning. Our experiments show that this approach results in better asymptotic performance, enhanced initial performance, improved safety, better action effectiveness, and reduced sample complexity.

Risk management is an essential part of software security. Assemblyline is a software security tool developed by the Canadian Centre for Cyber Security (CCCS) for malware detection and analysis. In this paper, we examined the performance of Assemblyline for assessing the risk of executable files. We developed and examined use-cases where Assemblyline is included as part of a security safety net assessing vulnerabilities that would lead to risk. Finally, we considered Assemblyline’s utility in a continuous integration / delivery context using our test results.

A program is said to be isochronous if its running time does not depend on classified information. The programming languages literature contains much work that transforms programs to ensure isochronicity. The current state-of-the-art approach is a code transformation technique due to Wu et al., published in 2018. That technique has an important virtue: it ensures that the transformed program runs exactly the same set of operations, regardless of inputs. However, in this paper we demonstrate that it has also a shortcoming: it might add out-of-bounds memory accesses into programs that were originally memory sound. From this observation, we show how to deliver the same runtime guarantees that Wu et al. provide, in a memory-safe way. In addition to being safer, our LLVM-based implementation is more efficient than its original inspiration, achieving shorter repairing times, and producing code that is smaller and faster.

Industry 4.0 is now much more than just a buzzword. However, with the advancement of automation through digitization and softwarization of dedicated hardware, applications are also becoming more susceptible to random hardware errors in the calculation. This cyber-physical demonstrator uses a robotic application to show the effects that even single bit flips can have in the real world due to hardware errors. Using the graphical user interface including the human machine interface, the audience can generate hardware errors in the form of bit flips and see their effects live on the robot. In this paper we will be showing a new technology, the SIListra Safety Transformer (SST), that makes it possible to detect those kind of random hardware errors, which can subsequently make safety-critical applications more reliable.

This article analyzes the basic concepts of supply chain finance, participating institutions, business methods, and exposure to risks. The author combined the basic content of the Internet of Things and block chain technology to carry out research. This paper studies the specific applications of the Internet of Things and block chain technology in supply chain financial risk identification, supply chain financial risk assessment, full-process logistics supervision, smart contract transaction management, corporate financial statement sorting, and risk prevention measures. The author's purpose is to improve the financial risk management level of the enterprise supply chain and promote the stable development of the enterprise economy.

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.

The npm ecosystem has the largest number of third-party packages for making node.js-based web apps. Due to its free and open nature, it can raise diversity of security concerns. Adversaries can take advantage of existing software APIs included in node.js web apps for achieving their own malicious targets. More specifically, attackers may inject malicious data into its client requests and then submit them to a victim node.js server. It then may manipulate program states to reuse sensitive APIs as gadgets required in the node.js web app executed on the victim server. Once such sensitive APIs can be successfully accessed, it may indirectly raise security threats such as code injection attacks, software-layer DoS attacks, private data leaks, etc. For example, when the sensitive APIs are implemented as pattern matching operations and are called with hard-to-match input string submitted by clients, it may launch application-level DoS attacks.In this paper, we would like to introduce software reuse exploits through reusing packages available in node.js web apps for posing security threats to servers. In addition, we propose an approach based on data flow analysis to detect vulnerable npm packages that can be exposed to such exploits. To evaluate its effectiveness, we collected a dataset of 15,000 modules from the ecosystem to conduct the experiments. As a result, it discovered out 192 vulnerable packages. By manual analysis, we identified 156 true positives of 192 that can be exposed to code reuse exploits for remotely causing software-layer DoS attacks with 128 modules of 156, for code injection with 18 modules, and for private data leaks including 10 vulnerable ones.

Program verification techniques have gained increasing popularity in academic and industrial circles during the last years. Predicate abstraction is a traditional and practical verification technique, which can solve the problem of state space explosion pretty well. Many software verification tools have implemented it. But these implementations are not user-friendly, or scalable. Aimed at these problems, we describe and implement a new automatic predicate abstraction framework, CChecker, for proving the safety of procedural programs with integer assignments. CChecker is a whole system composed of two parts: front and back end. The front end preprocesses and parses the source programs into logic models based on Clang. And the back end resolves the models based on Z3 to get software safety property. At last, the experiments show the potential of CChecker.

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.

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.

As a large amount of renewable energy is injected into the power grid, the source side of the power grid becomes extremely uncertain. Traditional static safety analysis methods based on pure physical models can no longer quickly and reliably give analysis results. Therefore, this paper proposes a deep learning-based static security analytical method. First, the static security assessment index of the power grid under the N-1 principle is proposed. Secondly, a neural network model and its input and output data for static safety analysis problems are designed. Finally, the validity of the proposed method was verified by IEEE grid data. Experiments show that the proposed method can quickly and accurately give the static security analysis results of the source-side high uncertainty grid.

With the vigorous development of the Internet and the widespread popularity of smart devices, the amount of data it generates has also increased exponentially, which has also promoted the generation and development of cloud computing and big data. Given cloud computing and big data technology, cloud storage has become a good solution for people to store and manage data at this stage. However, when cloud storage manages and regulates massive amounts of data, its security issues have become increasingly prominent. Aiming at a series of security problems caused by a malicious user's illegal operation of cloud storage and the loss of all data, this paper proposes a threshold signature scheme that is signed by a private key composed of multiple users. When this method performs key operations of cloud storage, multiple people are required to sign, which effectively prevents a small number of malicious users from violating data operations. At the same time, the threshold signature method in this paper uses a double update factor algorithm. Even if the attacker obtains the key information at this stage, he can not calculate the complete key information before and after the time period, thus having the two-way security and greatly improving the security of the data in the cloud storage.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.

To ensure traffic safety and proper operation of vehicular networks, safety messages or beacons are periodically broadcasted in Vehicular Adhoc Networks (VANETs) to neighboring nodes and road side units (RSU). Thus, authenticity and integrity of received messages along with the trust in source nodes is crucial and highly required in applications where a failure can result in life-threatening situations. Several digital signature based approaches have been described in literature to achieve the authenticity of these messages. In these schemes, scenarios having high level of vehicle density are handled by RSU where aggregated signature verification is done. However, most of these schemes are centralized and PKI based where our goal is to develop a decentralized dynamic system. Along with authenticity and integrity, trust management plays an important role in VANETs which enables ways for secure and verified communication. A number of trust management models have been proposed but it is still an ongoing matter of interest, similarly authentication which is a vital security service to have during communication is not mostly present in the literature work related to trust management systems. This paper proposes a secure and publicly verifiable communication scheme for VANET which achieves source authentication, message authentication, non repudiation, integrity and public verifiability. All of these are achieved through digital signatures, Hash Message Authentication Code (HMAC) technique and logging mechanism which is aided by blockchain technology.

In Industry 4.0, Information Technology (IT) and Operational Technology (OT) tend to converge further with an increasing interdependence of safety and security issues to be considered. On one hand, cyber attacks are possible which can alter implemented safety functionality leading to situations where people are harmed, serious injuries may occur or the environment gets damaged. On the other side, safety can also impact security. For instance, the misuse of a Safety Instrumented System (SIS) may force a machine or a production line to shut down resulting in a denial of service. To prevent or mitigate risks from such scenarios, this paper proposes a threat modeling technique which addresses an integrated view on safety and security. The approach is tailored to the industrial automation domain considering plausible attacks and evaluating risks based on three different metrics. The metrics selected consist of Common Vulnerability Scoring System (CVSS) used as an international standard for rating cyber security vulnerabilities, Security Level (SL) from IEC 62443 to rate cyber security risks in OT environments w.r.t. the underlying architecture, and Safety Integrity Level (SIL) from IEC 61508 to rate safety risks. Due to the variety of use cases involving the chosen metrics, the approach is also feasible for followup analyses, such as integrated safety and security assessments or audits.

In this paper we propose to combine the safe C dialect Checked C with concolic testing to obtain an effective methodology for attaining safer C code. Checked C is a modern and backward compatible extension to the C programming language which provides facilities for writing memory-safe C code. We utilize incremental conversions of unsafe C software to Checked C. After each increment, we leverage concolic testing, an effective test generation technique, to support the conversion process by searching for newly introduced and existing bugs.Our RISC-V experiments using the RIOT Operating System (OS) demonstrate the effectiveness of our approach. We uncovered 4 previously unknown bugs and 3 bugs accidentally introduced through our conversion process.

The secure functioning of automotive systems is vital to the safety of their passengers and other roadway users. One of the critical functions for safety is the controller area network (CAN), which interconnects the safety-critical electronic control units (ECUs) in the majority of ground vehicles. Unfortunately CAN is known to be vulnerable to several attacks. One such attack is the bus-off attack, which can be used to cause a victim ECU to disconnect itself from the CAN bus and, subsequently, for an attacker to masquerade as that ECU. A limitation of the bus-off attack is that it requires the attacker to achieve tight synchronization between the transmission of the victim and the attacker's injected message. In this paper, we introduce a schedule-based attack framework for the CAN bus-off attack that uses the real-time schedule of the CAN bus to predict more attack opportunities than previously known. We describe a ranking method for an attacker to select and optimize its attack injections with respect to criteria such as attack success rate, bus perturbation, or attack latency. The results show that vulnerabilities of the CAN bus can be enhanced by schedule-based attacks.