Biblio

The problem of information privacy has grown more significant in terms of data storage and communication in the 21st century due to the technological explosion during which information has become a highly important strategic resource. The idea of employing DNA cryptography has been highlighted as a potential technology that offers fresh hope for unbreakable algorithms since standard cryptosystems are becoming susceptible to assaults. Due to biological DNA's outstanding energy efficiency, enormous storage capacity, and extensive parallelism, a new branch of cryptography based on DNA computing is developing. There is still more study to be done since this discipline is still in its infancy. This work proposes a DNA encryption strategy based on cryptographic key generation techniques and chaotic diffusion operation.

Accurately constructing dynamic network topology is one of the core tasks to provide on-demand security services to the ubiquitous network. Existing schemes cannot accurately construct dynamic network topologies in time. In this paper, we propose a novel scheme to construct the ubiquitous network topology. Firstly, ubiquitous network nodes are divided into three categories: terminal node, sink node, and control node. On this basis, we propose two operation primitives (i.e., addition and subtraction) and three atomic operations (i.e., intersection, union, and fusion), and design a series of algorithms to describe the network change and construct the network topology. We further use our scheme to depict the specific time-varying network topologies, including Satellite Internet and Internet of things. It demonstrates that their communication and security protection modes can be efficiently and accurately constructed on our scheme. The simulation and theoretical analysis also prove that the efficiency of our scheme, and effectively support the orchestration of protection capabilities.

Dynamic memory allocators are critical components of modern systems, and developers strive to find a balance between their performance and their security. Unfortunately, vulnerable allocators are routinely abused as building blocks in complex exploitation chains. Most of the research regarding memory allocators focuses on popular and standardized heap libraries, generally used by high-end devices such as desktop systems and servers. However, dynamic memory allocators are also extensively used in embedded systems but they have not received much scrutiny from the security community.In embedded systems, a raw firmware image is often the only available piece of information, and finding heap vulnerabilities is a manual and tedious process. First of all, recognizing a memory allocator library among thousands of stripped firmware functions can quickly become a daunting task. Moreover, emulating firmware functions to test for heap vulnerabilities comes with its own set of challenges, related, but not limited, to the re-hosting problem.To fill this gap, in this paper we present HEAPSTER, a system that automatically identifies the heap library used by a monolithic firmware image, and tests its security with symbolic execution and bounded model checking. We evaluate HEAPSTER on a dataset of 20 synthetic monolithic firmware images — used as ground truth for our analyses — and also on a dataset of 799 monolithic firmware images collected in the wild and used in real-world devices. Across these datasets, our tool identified 11 different heap management library (HML) families containing a total of 48 different variations. The security testing performed by HEAPSTER found that all the identified variants are vulnerable to at least one critical heap vulnerability. The results presented in this paper show a clear pattern of poor security standards, and raise some concerns over the security of dynamic memory allocators employed by IoT devices.

To ensure dynamic and transient angle and load stability in order to maintain the power system security is a major task of the power Engineer. FACTS Controllers are most effective devices to ensure system security by enhancing the stability margins with reactive power support all over the power system network. The major shunt compensation devices of FACTS are SVC and STATCOM. This article dispenses the modelling and simulation of both the shunt devices viz. Oneis the Static Synchronous Compensator (STATCOM) and the other is Static Var Compensator (SVC). The small signal models of these devices have been derived from the first principles and obtained the transfer function models of weak and strong power systems. The weak power system has the Short Circuit Ratio (SCR) is about less than 3 and that of the strong power system has the SCR of more than 5. The performance of the both weak and strong power systems has been evaluated with time and frequency responses. The dynamic response is obtained with the exact models for both weak and strong systems, subsequently the root locus plots as well as bode plots have been obtained with MATLAB Programs and evaluated the performance of these devices and comparison is made. The Stability margins of both the systems with SVC and STATCOM have been obtained from the bode plots. The dynamic behaviour of the both kinds of power systems have been assessed with time responses of SVC and STATCOM models. All of these results viz. dynamic response, root locus and bode plots proves the superiority of the STATCOM over SVC with indices, viz. peak overshoot, settling time, gain margin and phase margins. The dynamic, steady state performance indices obtained from time response and bode plots proves the superior performance of STATCOM.

Train operation is highly influenced by the rail track state and the surrounding environment. An abnormal obstacle on the rail track will pose a severe threat to the safe operation of urban rail transit. The existing general obstacle detection approaches do not consider the specific urban rail environment and requirements. In this paper, we propose an edge intelligence (EI)-based obstacle intrusion detection system to detect accurate obstacle intrusion in real-time. A two-stage lightweight deep learning model is designed to detect obstacle intrusion and obtain the distance from the train to the obstacle. Edge computing (EC) and 5G are used to conduct the detection model and improve the real-time detection performance. A multi-agent reinforcement learning-based offloading and service migration model is formulated to optimize the edge computing resource. Experimental results show that the two-stage intrusion detection model with the reinforcement learning (RL)-based edge resource optimization model can achieve higher detection accuracy and real-time performance compared to traditional methods.

At present, pavement crack detection mainly depends on manual survey and semi-automatic detection. In the process of damage detection, it will inevitably be subject to the subjective influence of inspectors and require a lot of identification time. Therefore, this paper proposes the research and implementation of artificial intelligence real-time recognition method of crack edge based on zynq, which combines edge calculation technology with deep learning, The improved ipd-yolo target detection network is deployed on the zynq zu2cg edge computing development platform. The mobilenetv3 feature extraction network is used to replace the cspdarknet53 feature extraction network in yolov4, and the deep separable convolution is used to replace the conventional convolution. Combined with the advantages of the deep neural network in the cloud and edge computing, the rock fracture detection oriented to the edge computing scene is realized. The experimental results show that the accuracy of the network on the PID data set The recall rate and F1 score have been improved to better meet the requirements of real-time identification of rock fractures.

Edge detection is the key and difficult point of machine vision and image processing technology. The traditional edge detection algorithm is sensitive to noise and it is difficult to accurately extract the edge of the image, so the effect of image processing is not ideal. To solve this problem, people in the industry use the structural element features of morphological edge detection operator to extract the edge features of the image by carefully designing and combining the structural elements of different sizes and directions, so as to effectively ensure the integrity of edge information in all directions and eliminate large noise at the same time. This paper first introduces the traditional edge detection algorithms, then summarizes the edge detection algorithms based on mathematical morphology in recent years, finds that the selection of multi-scale and multi-directional structural elements is an important research direction, and finally discusses the development trend of mathematical morphology edge detection technology.

With the variety of cloud services, the cloud service provider delivers the machine learning service, which is used in many applications, including risk assessment, product recommen-dation, and image recognition. The cloud service provider initiates a protocol for the classification service to enable the data owners to request an evaluation of their data. The owners may not entirely rely on the cloud environment as the third parties manage it. However, protecting data privacy while sharing it is a significant challenge. A novel privacy-preserving model is proposed, which is based on differential privacy and machine learning approaches. The proposed model allows the various data owners for storage, sharing, and utilization in the cloud environment. The experiments are conducted on Blood transfusion service center, Phoneme, and Wilt datasets to lay down the proposed model's efficiency in accuracy, precision, recall, and Fl-score terms. The results exhibit that the proposed model specifies high accuracy, precision, recall, and Fl-score up to 97.72%, 98.04%, 97.72%, and 98.80%, respectively.

Fake news is a new phenomenon that promotes misleading information and fraud via internet social media or traditional news sources. Fake news is readily manufactured and transmitted across numerous social media platforms nowadays, and it has a significant influence on the real world. It is vital to create effective algorithms and tools for detecting misleading information on social media platforms. Most modern research approaches for identifying fraudulent information are based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, and newly built datasets and online services. There is a pressing need to develop a viable approach for readily detecting misleading information. The deep learning LSTM and Bi-LSTM model was proposed as a method for detecting fake news, In this work. First, the NLTK toolkit was used to remove stop words, punctuation, and special characters from the text. The same toolset is used to tokenize and preprocess the text. Since then, GLOVE word embeddings have incorporated higher-level characteristics of the input text extracted from long-term relationships between word sequences captured by the RNN-LSTM, Bi-LSTM model to the preprocessed text. The proposed model additionally employs dropout technology with Dense layers to improve the model's efficiency. The proposed RNN Bi-LSTM-based technique obtains the best accuracy of 94%, and 93% using the Adam optimizer and the Binary cross-entropy loss function with Dropout (0.1,0.2), Once the Dropout range increases it decreases the accuracy of the model as it goes 92% once Dropout (0.3).

The MANET architecture's future growth will make extensive use of encryption and encryption to keep network participants safe. Using a digital signature node id, we illustrate how we may stimulate the safe growth of subjective clusters while simultaneously addressing security and energy efficiency concerns. The dynamic topology of MANET allows nodes to join and exit at any time. A form of attack known as a black hole assault was used to accomplish this. To demonstrate that he had the shortest path with the least amount of energy consumption, an attacker in MATLAB R2012a used a digital signature ID to authenticate the node from which he wished to intercept messages (DSEP). “Digital Signature”, “MANET,” and “AODV” are all terms used to describe various types of digital signatures. Black Hole Attack, Single Black Hole Attack, Digital Signature, and DSEP are just a few of the many terms associated with MANET.

The Manufacturer Usage Description (MUD) standard aims to reduce the attack surface for IoT devices by locking down their behavior to a formally-specified set of network flows (access control entries). Formal network behaviors can also be systematically and rigorously verified in any operating environment. Enforcing MUD flows and monitoring their activity in real-time can be relatively effective in securing IoT devices; however, its scope is limited to endpoints (domain names and IP addresses) and transport-layer protocols and services. Therefore, misconfigured or compromised IoTs may conform to their MUD-specified behavior but exchange unintended (or even malicious) contents across those flows. This paper develops PicP-MUD with the aim to profile the information content of packet payloads (whether unencrypted, encoded, or encrypted) in each MUD flow of an IoT device. That way, certain tasks like cyber-risk analysis, change detection, or selective deep packet inspection can be performed in a more systematic manner. Our contributions are twofold: (1) We analyze over 123K network flows of 6 transparent (e.g., HTTP), 11 encrypted (e.g., TLS), and 7 encoded (e.g., RTP) protocols, collected in our lab and obtained from public datasets, to identify 17 statistical features of their application payload, helping us distinguish different content types; and (2) We develop and evaluate PicP-MUD using a machine learning model, and show how we achieve an average accuracy of 99% in predicting the content type of a flow.

Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations for training and validation. Differently from the vast related literature, in this paper we propose an early classification approach conducted on CSE-CIC-IDS2018 dataset, which contains both benign and malicious traffic, for the detection of malicious attacks before they could damage an organization. To this aim, we investigated a different set of features, and the sensitivity of performance of five classification algorithms to the number of observed packets. Results show that ML approaches relying on ten packets provide satisfactory results.

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems.

Undoubtedly, technology has not only transformed our world of work and lifestyle, but it also carries with it a lot of security challenges. The Distributed Denial-of-Service (DDoS) attack is one of the most prominent attacks witnessed by cyberspace of the current era. This paper outlines several DDoS attacks, their mitigation stages, propagation of attacks, malicious codes, and finally provides redemptions of exhibiting normal and DDoS attacked scenarios. A case study of a SYN flooding attack has been exploited by using Metasploit. The utilization of CPU frame length and rate have been observed in normal and attacked phases. Preliminary results clearly show that in a normal scenario, CPU usage is about 20%. However, in attacked phases with the same CPU load, CPU execution overhead is nearly 90% or 100%. Thus, through this research, the major difference was found in CPU usage, frame length, and degree of data flow. Wireshark tool has been used for network traffic analyzer.

With the advent of the 5G era, high-speed and secure network access services have become a common pursuit. The QUIC (Quick UDP Internet Connection) protocol proposed by Google has been studied by many scholars due to its high speed, robustness, and low latency. However, the research on the security of the QUIC protocol by domestic and foreign scholars is insufficient. Therefore, based on the self-similarity of QUIC network traffic, combined with traffic characteristics and signal processing methods, a QUIC-based network traffic anomaly detection model is proposed in this paper. The model decomposes and reconstructs the collected QUIC network traffic data through the Empirical Mode Decomposition (EMD) method. In order to judge the occurrence of abnormality, this paper also intercepts overlapping traffic segments through sliding windows to calculate Hurst parameters and analyzes the obtained parameters to check abnormal traffic. The simulation results show that in the network environment based on the QUIC protocol, the Hurst parameter after being attacked fluctuates violently and exceeds the normal range. It also shows that the anomaly detection of QUIC network traffic can use the EMD method.

Documents are a common method of storing infor-mation and one of the most conventional forms of expression of ideas. Cloud servers store a user's documents with thousands of other users in place of physical storage devices. Indexes corresponding to the documents are also stored at the cloud server to enable the users to retrieve documents of their interest. The index includes keywords, document identities in which the keywords appear, along with Term Frequency-Inverse Document Frequency (TF-IDF) values which reflect the keywords' relevance scores of the dataset. Currently, there are no efficient methods to delete keywords from millions of documents over cloud servers while avoiding any compromise to the user's privacy. Most of the existing approaches use algorithms that divide a bigger problem into sub-problems and then combine them like divide and conquer problems. These approaches don't focus entirely on fine-grained deletion. This work is focused on achieving fine-grained deletion of keywords by keeping the size of the TF-IDF matrix constant after processing the deletion query, which comprises of keywords to be deleted. The experimental results of the proposed approach confirm that the precision of ranked search still remains very high after deletion without recalculation of the TF-IDF matrix.

In recent years, with the occurrence of climate change and various extreme events, the research on the resistance of physical information systems to large-scale complex faults is of great significance. Propose a power information system to deal with complex faults in extreme weather, establish an anti-interference framework, construct a regional anti-interference strategy based on regional load output matching and topological connectivity, and propose branch active power adjustment methods to reduce disasters. In order to resist the risk of system instability caused by overrun of branch power and phase disconnection, the improved IEEE33 node test system simulation shows that this strategy can effectively reduce the harm of large-scale and complex faults.

Supply chain cyberattacks that exploit insecure third-party software are a growing concern for the security of the electric power grid. These attacks seek to deploy malicious software in grid control devices during the fabrication, shipment, installation, and maintenance stages, or as part of routine software updates. Malicious software on grid control devices may inject bad data or execute bad commands, which can cause blackouts and damage power equipment. This paper describes an experimental setup to simulate the software update process of a commercial power relay as part of a hardware-in-the-loop simulation for grid supply chain cyber-security assessment. The laboratory setup was successfully utilized to study three supply chain cyber-security use cases.

As cyber threats become highly damaging and complex, a new cybersecurity compliance certification model has been developed by the Department of Defense (DoD) to secure its Defense Industrial Base (DIB), and communication with its private partners. These partners or contractors are obligated by the Defense Federal Acquisition Regulations (DFARS) to be compliant with the latest standards in computer and data security. The Cybersecurity Maturity Model Certification (CMMC), and it is built upon existing DFARS 252.204-7012 and the NIST SP 800–171 controls. As of 2020, the DoD has incorporated DFARS and the National Institute of Standards and Technology (NIST) recommended security practices into what is now the CMMC. This paper presents the most commonly identified Security-Control-Deficiencies (SCD) faced, the attacks mitigated by addressing these SCD, and remediations applied to 127 DoD contractors in order to bring them into compliance with the CMMC guidelines. An analysis is done on what vulnerabilities are most prominent in the companies, and remediations applied to ensure these vulnerabilities are better avoided and the DoD supply-chain is more secure from attacks.

The healthcare industry is confronted with a slew of significant challenges, including stringent regulations, privacy concerns, and rapidly rising costs. Many leaders and healthcare professionals are looking to new technology and informatics to expand more intelligent forms of healthcare delivery. Numerous technologies have advanced during the last few decades. Over the past few decades, pharmacy has changed and grown, concentrating less on drugs and more on patients. Pharmaceutical services improve healthcare's affordability and security. The primary invention was a cyber-infrastructure made up of smart gadgets that are connected to and communicate with one another. These cyber infrastructures have a number of problems, including privacy, trust, and security. These gadgets create cyber-physical systems for pharmaceutical care services in p-health. In the present period, cyber-physical systems for pharmaceutical care services are dealing with a variety of important concerns and demanding conditions, i.e., problems and obstacles that need be overcome to create a trustworthy and effective medical system. This essay offers a thorough examination of CPS's architectural difficulties and emerging tendencies.

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

With the intelligent development of power system, due to the double-layer structure of smart grid and the characteristics of failure propagation across layers, the attack path also changes significantly: from single-layer to multi-layer and from static to dynamic. In response to the shortcomings of the single-layer attack path of traditional attack path identification methods, this paper proposes the idea of cross-layer attack, which integrates the threat propagation mechanism of the information layer and the failure propagation mechanism of the physical layer to establish a forward-backward bi-directional detection model. The model is mainly used to predict possible cross-layer attack paths and evaluate their path generation probabilities to provide theoretical guidance and technical support for defenders. The experimental results show that the method proposed in this paper can well identify the dynamic cross-layer attacks in the smart grid.