Biblio

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.

The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach detects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.

As nuclear power plant Instrumentation and Control(I&C) systems have turned into digital systems, the possibility of cyber-attacks has increased. To protect the nuclear power plant from cyber-attacks, digital assets are classified and managed as critical digital assets which have safety, security and emergency preparedness functions. However, critical digital assets represent 70-80% of total digital assets, and applying and managing the same security control is inefficient. Therefore, this paper presents the criteria for identifying digital assets that are classified as vital digital assets that can directly affect the serious accidents of nuclear power plants.

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

Researchers are trying to find new ways of finding and pointing out Cybersecurity vulnerabilities by using innovative metrics. New theoretical proposals need to be tested in a real environment, using Cybersecurity tools applications that can validate the applicability of those in real life. This paper presents an experimental flexible environment, which can be used for the validation of several theoretical claims based on an “easy to use” architecture implemented in a cloud environment. The framework provides a much shorter time setup in the real world as well as a much better understanding based on log analysis provided by MS Azure. As a proof of concept, we have tested three different claims and provided proves of results such as screenshots and log samples.

The paper discusses the use of virtual (VR) and augmented (AR) reality for visual analytics in information security. Paper answers two questions: “In which areas of information security visualization VR/AR can be useful?” and “What is the difference of the VR/AR from similar methods of visualization at the level of perception of information?”. The first answer is based on the investigation of information security areas and visualization models that can be used in VR/AR security visualization. The second answer is based on experiments that evaluate perception of visual components in VR.

Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of user's eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a student's ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.

What does it mean to trust, or not trust, an augmented reality system? Froma computer security point of view, trust in augmented reality represents a real threat to real people. The fact that augmented reality allows the programmer to tinker with the user's senses creates many opportunities for malfeasance. It might be natural to think that if we warn users to be careful it will lower their trust in the system, greatly reducing risk.

Today's software is full of security vulnerabilities that invite attack. Attackers are especially drawn to software systems containing sensitive data. For such systems, this paper presents a modeling approach especially suited for Serum or other forms of agile development to identify and reduce the attack surface. The latter arises due to the locations containing sensitive data within the software system that are reachable by attackers. The approach reduces the attack surface by changing the design so that the number of such locations is reduced. The approach performs these changes on a visual model of the software system. The changes are then considered for application to the actual system to improve its security.

To improve production efficiency, more industrial control systems are connected to IT networks, and more IT technologies are applied to industrial control networks, network security has become an important problem. Industrial control network security analysis and decision-making is a effective method to solve the problem, which can predict risks and support to make decisions before the actual fault of the industrial control network system has not occurred. This paper proposes a security analysis and decision-making method with forward reasoning based on strong relevant logic for industrial control networks. The paper presents a case study in security analysis and decision-making for industrial control networks. The result of the case study shows that the proposed method is effective.

Organizations perform security analysis for assessing network health and safe-guarding their growing networks through Vulnerability Assessments (AKA VA Scans). The output of VA scans is reports on individual hosts and its vulnerabilities, which, are of little use as the origin of the attack can't be located from these. Attack Graphs, generated without an in-depth analysis of the VA reports, are used to fill in these gaps, but only provide cursory information. This study presents an effective model of depicting the devices and the data flow that efficiently identifies the weakest nodes along with the concerned vulnerability's origin.The complexity of the attach graph using MulVal has been greatly reduced using the proposed approach of using the risk and CVSS base score as evaluation criteria. This makes it easier for the user to interpret the attack graphs and thus reduce the time taken needed to identify the attack paths and where the attack originates from.

Cyber threats have increased extensively during the last decade, especially in smart grids. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and Advanced Persistent Threat (APT) is almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques can mine data to detect different attack stages of APT. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits.

Machine learning, specifically deep learning is becoming a key technology component in application domains such as identity management, finance, automotive, and healthcare, to name a few. Proprietary machine learning models - Machine Learning IP - are developed and deployed at the network edge, end devices and in the cloud, to maximize user experience. With the proliferation of applications embedding Machine Learning IPs, machine learning models and hyper-parameters become attractive to attackers, and require protection. Major players in the semiconductor industry provide mechanisms on device to protect the IP at rest and during execution from being copied, altered, reverse engineered, and abused by attackers. In this work we explore system security architecture mechanisms and their applications to Machine Learning IP protection.

With the growing number of electronic features in cars and their connections to the cloud, smartphones, road-side equipment, and neighboring cars the need for effective cybersecurity is paramount. Beyond the concern of brand degradation, warranty fraud, and recalls, what keeps manufacturers up at night is the threat of malicious attacks which can affect the safety of vehicles on the road. Would any single protection technique provide the security needed over the long lifetime of a vehicle? We present a new methodology for automotive cybersecurity where the designs are made to withstand attacks in the future based on the concepts of high availability and malicious fault-tolerance through self-defending techniques. When a system has an intrusion, self-defending technologies work to contain the breach using integrity verification, self-healing, and fail-over techniques to keep the system running.

In this paper, we consider a stochastic model to evaluate the system availability of an intrusion tolerant system (ITS), where the system undergoes the patch management with a periodic vulnerability checking strategy, i.e., a pull-type patch management. Based on the model, this paper discusses the appropriate timing for patch applying. In particular, the paper models the attack behavior of adversary and the system behaviors under reactive defense strategies by a composite stochastic reward net (SRN). Furthermore, we formulate the interval availability by applying the phase-type (PH) approximation to solve the Markov regenerative process (MRGP) models derived from the SRNs. Numerical experiments are conducted to study the sensitivity of the system availability with respect to the number of checking.

Information security is a top priority in government and industry because high consequence cyber incidents continue with regularity. The blue teamers that protect cyber systems cannot stop or even know about all these incidents, so they must take measures to tolerate these incursions in addition to preventing and detecting them. We propose dynamically compartmentalizing subject networks into collaboration zones and limiting the communication between these zones. In this article, we demonstrate this technique's effect on the attacker and the defender for various parameter settings using discrete-time simulation. Based on our results, we conclude that dynamic cyber zone defense is a viable intrusion tolerance technique and should be considered for technology transfer.

Critical infrastructures of all countries unites common cyberspace. In this space, there are many threats that can disrupt the security of critical infrastructure in one country, but also cause damage in other countries. This is a reality that makes it necessary to agree on intergovernmental national views on the composition of critical infrastructures, an assessment of their security and protection. The article presents an overview of views on critical infrastructures of the United States, the European Union, the United Kingdom, and the Russian Federation, the purpose of which is to develop common positions.

Network security is a general idea to ensure information transmission over PC and portable systems. Elliptic curve cryptosystems are nowadays widely used in public communication channels for network security. Their security relies upon the complexity of clarifying the elliptic curve discrete alogarithm issue. But, there are several general attacks in them. Elliptic bend number juggling is actualized over complex fields to enhance the security of elliptic curve cryptosystems. This paper starts with the qualities of elliptic curve cryptosystems and their security administrations. At that point we talk about limited field number-crunching and its properties, prime field number-crunching, twofold field math and complex number-crunching, and elliptic bend number-crunching over prime field and parallel field. This paper proposes how to execute the unpredictable number of math under prime field and double field utilizing java BigInteger class. also, we actualize elliptic bend math and elliptic bend cryptosystems utilizing complex numbers over prime field and double field and talk about our trials that got from the usage.

For multiple embedded computers working together, the functional failure resulting from the underlying hardware or system crash will cause a sudden abort of applications. Different types of applications may have security requirements for data isolation and access control. Therefore, we propose a scheme to implement multi-level security domain dynamic management oriented embedded computers based on MILS architecture. Firstly, the scheme builds local security policy items and access control lists according to type, function and security level. After that, security domain of all applications is constructed to achieve the safety purpose that applications can perform migration cross partitions and cross platforms. Our experiments and analysis show that the proposed scheme is feasible and correct.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Today's IP and content distribution networks are unable to fulfill all data distribution and security requirements. The named data network (NDN) has emerged as a promising candidate to cope with the Internet usage of the 21st century. Although the NDN has many built-in security features, this survey reviews several pressing security issues and open research areas.

Computer and network accountability is to make every action in computers and networks accountable. In order to achieve accountability, we need to answer the following questions: what did it happen? When did it happen? Who did it? In order to achieve accountability, the first step is to record what exactly happened. Therefore, an accountable logging is needed and implemented in computers and networks. Our previous work proposed a novel accountable logging methodology called Flow-Net. However, how to storage the huge amount of Flow-net logs into databases is not clear. In this paper, we try to answer this question.

The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).

Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-`99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.