Biblio

Topic modeling algorithms from the natural language processing (NLP) discipline have been used for various applications. For instance, topic modeling for the product recommendation systems in the e-commerce systems. In this paper, we briefly reviewed topic modeling applications and then described our proposed idea of utilizing topic modeling approaches for cyber threat intelligence (CTI) applications. We improved the previous work by implementing BERTopic and Top2Vec approaches, enabling users to select their preferred pre-trained text/sentence embedding model, and supporting various languages. We implemented our proposed idea as the new topic modeling module for the Open Web Application Security Project (OWASP) Maryam: Open-Source Intelligence (OSINT) framework. We also described our experiment results using a leaked hacker forum dataset (nulled.io) to attract more researchers and open-source communities to participate in the Maryam project of OWASP Foundation.

The compressed sensing (CS) method can reconstruct images with a small amount of under-sampling data, which is an effective method for fast magnetic resonance imaging (MRI). As the traditional optimization-based models for MRI suffered from non-adaptive sampling and shallow” representation ability, they were unable to characterize the rich patterns in MRI data. In this paper, we propose a CS MRI method based on iterative shrinkage threshold algorithm (ISTA) and adaptive sparse sampling, called DSLS-ISTA-Net. Corresponding to the sampling and reconstruction of the CS method, the network framework includes two folders: the sampling sub-network and the improved ISTA reconstruction sub-network which are coordinated with each other through end-to-end training in an unsupervised way. The sampling sub-network and ISTA reconstruction sub-network are responsible for the implementation of adaptive sparse sampling and deep sparse representation respectively. In the testing phase, we investigate different modules and parameters in the network structure, and perform extensive experiments on MR images at different sampling rates to obtain the optimal network. Due to the combination of the advantages of the model-based method and the deep learning-based method in this method, and taking both adaptive sampling and deep sparse representation into account, the proposed networks significantly improve the reconstruction performance compared to the art-of-state CS-MRI approaches.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.

Vulnerability detection has always been an essential part of maintaining information security, and the existing work can significantly improve the performance of vulnerability detection. However, due to the differences in representation forms and deep learning models, various methods still have some limitations. In order to overcome this defect, We propose a vulnerability detection method VDBWGDL, based on weight graphs and deep learning. Firstly, it accurately locates vulnerability-sensitive keywords and generates variant codes that satisfy vulnerability trigger logic and programmer programming style through code variant methods. Then, the control flow graph is sliced for vulnerable code keywords and program critical statements. The code block is converted into a vector containing rich semantic information and input into the weight map through the deep learning model. According to specific rules, different weights are set for each node. Finally, the similarity is obtained through the similarity comparison algorithm, and the suspected vulnerability is output according to different thresholds. VDBWGDL improves the accuracy and F1 value by 3.98% and 4.85% compared with four state-of-the-art models. The experimental results prove the effectiveness of VDBWGDL.

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.

Cyber security is everybody's responsibility. It is the capability of the person to protect or secure the use of cyberspace from cyber-attacks. Cyber security awareness is the combination of both knowing and doing to safeguard one's personal information or assets. Online threats continue to rise in the Philippines which is the focus of this study, to identify the level of cyber security awareness among the students and teachers of Occidental Mindoro State College (OMSC) Philippines. Results shows that the level of cyber security awareness in terms of Knowledge, majority of the students and teachers got the passing score and above however there are almost fifty percent got below the passing score. In terms of Practices, both the teachers and the students need to strengthen the awareness of system and browser updates to boost the security level of the devices used. More than half of the IT students are aware of the basic cyber security protocol but there is a big percentage in the Non-IT students which is to be considered. Majority of the teachers are aware of the basic cyber security protocols however the remaining number must be looked into. There is a need to intensity the awareness of the students in the proper etiquette in using the social media. Boost the basic cyber security awareness training to all students and teachers to avoid cybercrime victims.

Virtual power plants are among the promising ways that variable generation and flexible demand may be optimally balanced in the future. The virtual power plant is an important branch of the energy internet, and it plays an important role in the aggregation of distributed power generation resources and the establishment of virtual power resource transactions. However, in the existing virtual power plant model, the following problems are becoming increasingly prominent, such as safeguard, credit rating system, privacy protection, benefit distribution. Firstly, the operation and transaction mechanism of the virtual power plant was introduced. Then, the blockchain technology is introduced into the virtual power plant transaction to make it more conducive to the information transparent, stable dispatch system, data security, and storage security. Finally, the operation and transaction system based on blockchain technology for the virtual power plant was design.

In this work, we discuss data breaches based on the “2012 SocialArks data breach” case study. Data leakage refers to the security violations of unauthorized individuals copying, transmitting, viewing, stealing, or using sensitive, protected, or confidential data. Data leakage is becoming more and more serious, for those traditional information security protection methods like anti-virus software, intrusion detection, and firewalls have been becoming more and more challenging to deal with independently. Nevertheless, fortunately, new IT technologies are rapidly changing and challenging traditional security laws and provide new opportunities to develop the information security market. The SocialArks data breach was caused by a misconfiguration of ElasticSearch Database owned by SocialArks, owned by “Tencent.” The attack methodology is classic, and five common Elasticsearch mistakes discussed the possibilities of those leakages. The defense solution focuses on how to optimize the Elasticsearch server. Furthermore, the ElasticSearch database’s open-source identity also causes many ethical problems, which means that anyone can download and install it for free, and they can install it almost anywhere. Some companies download it and install it on their internal servers, while others download and install it in the cloud (on any provider they want). There are also cloud service companies that provide hosted versions of Elasticsearch, which means they host and manage Elasticsearch clusters for their customers, such as Company Tencent.

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.

Critical infrastructures such as the electricity grid can be severely impacted by cyber-attacks on its supply chain. Hence, having a robust cybersecurity infrastructure and management system for the electricity grid is a high priority. This paper proposes a cyber-security protocol for defense against man-in-the-middle (MiTM) attacks to the supply chain, which uses encryption and cryptographic multi-party authentication. A cyber-physical simulator is utilized to simulate the power system, control system, and security layers. The correctness of the attack modeling and the cryptographic security protocol against this MiTM attack is demonstrated in four different attack scenarios.

The botnet-based network assault are one of the most serious security threats overlay the Internet this day. Although significant progress has been made in this region of research in recent years, it is still an ongoing and challenging topic to virtually direction the threat of botnets due to their continuous evolution, increasing complexity and stealth, and the difficulties in detection and defense caused by the limitations of network and system architectures. In this paper, we propose a novel and efficient botnet detection method, and the results of the detection method are validated with the CTU-13 dataset.

A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant.

In this decade, digital transactions have risen exponentially demanding more reliable and secure authentication systems. CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) system plays a major role in these systems. These CAPTCHAs are available in character sequence, picture-based, and audio-based formats. It is very essential that these CAPTCHAs should be able to differentiate a computer program from a human precisely. This work tests the strength of text-based CAPTCHAs by breaking them using an algorithm built on CNN (Convolution Neural Network) and RNN (Recurrent Neural Network). The algorithm is designed in such a way as an attempt to break the security features designers have included in the CAPTCHAs to make them hard to be cracked by machines. This algorithm is tested against the synthetic dataset generated in accordance with the schemes used in popular websites. The experiment results exhibit that the model has shown a considerable performance against both the synthetic and real-world CAPTCHAs.

The internet has grown increasingly important in everyone's everyday lives due to the availability of numerous web services such as email, cloud storage, video streaming, music streaming, and search engines. On the other hand, attacks by computer programmes such as bots are a common hazard to these internet services. Captcha is a computer program that helps a server-side company determine whether or not a real user is requesting access. Captcha is a security feature that prevents unauthorised access to a user's account by protecting restricted areas from automated programmes, bots, or hackers. Many websites utilise Captcha to prevent spam and other hazardous assaults when visitors log in. However, in recent years, the complexity of Captcha solving has become difficult for humans too, making it less user friendly. To solve this, we propose creating a Captcha that is both simple and engaging for people while also robust enough to protect sensitive data from bots and hackers on the internet. The suggested captcha scheme employs animated artifacts, rotation, and variable fonts as resistance techniques. The proposed captcha technique proves successful against OCR bots with less than 15% accuracy while being easier to solve for human users with more than 98% accuracy.

The term cryptocurrency refers to a digital currency based on cryptographic concepts that have become popular in recent years. Bitcoin is a decentralized cryptocurrency that uses the distributed append-only public database known as blockchain to record every transaction. The incentive-compatible Proof-of-Work (PoW)-centered decentralized consensus procedure, which is upheld by the network's nodes known as miners, is essential to the safety of bitcoin. Interest in Bitcoin appears to be growing as the market continues to rise. Bitcoins and Blockchains have identical fundamental ideas, which are briefly discussed in this paper. Various studies discuss blockchain as a revolutionary innovation that has various applications, spanning from bitcoins to smart contracts, and also about it being a solution to many issues. Furthermore, many papers are reviewed here that not only look at Bitcoin’s fundamental underpinning technologies, such as Mixing and the Bitcoin Wallets but also at the flaws in it.

A recent spam wave of IP addresses in the Bitcoin P2P network allowed us to estimate the degree distribution of reachable peers. The resulting distribution indicates that about half of the reachable peers run with Bitcoin Core’s default setting of a maximum of 125 concurrent connections and nearly all connection slots are taken. We validate this result empirically. We use our observations of the spam wave to group IP addresses that belong to the same peer. By doing this grouping, we improve on previous measurements of the number of reachable peers and show that simply counting IP addresses overestimates the number of reachable peers by 15 %. We revalidate previous work by using our observations to estimate the number of unreachable peers.

Advanced Encryption Standard (AES) algorithm plays an important role in a data security application. In general S-box module in AES will give maximum confusion and diffusion measures during AES encryption and cause significant path delay overhead. In most cases, either L UTs or embedded memories are used for S- box computations which are vulnerable to attacks that pose a serious risk to real-world applications. In this paper, implementation of the composite field arithmetic-based Sub-bytes and inverse Sub-bytes operations in AES is done. The proposed work includes an efficient multiple round AES cryptosystem with higher-order transformation and composite field s-box formulation with some possible inner stage pipelining schemes which can be used for throughput rate enhancement along with path delay optimization. Finally, input biometric-driven key generation schemes are used for formulating the cipher key dynamically, which provides a higher degree of security for the computing devices.

This paper analyzes the problems existing in the existing emergency management technology system in China from various perspectives, and designs the construction of intelligent emergency system in combination with the development of new generation of Internet of Things, big data, cloud computing and artificial intelligence technology. The overall design is based on scientific and technological innovation to lead the reform of emergency management mechanism and process reengineering to build an intelligent emergency technology system characterized by "holographic monitoring, early warning, intelligent research and accurate disposal". To build an intelligent emergency management system that integrates intelligent monitoring and early warning, intelligent emergency disposal, efficient rehabilitation, improvement of emergency standards, safety and operation and maintenance construction.

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack. Specially, given an image selected from a group of images containing some common and salient objects, we aim to generate an adversarial version that can mislead CoSOD methods to predict incorrect co-salient regions. Note that, compared with general white-box adversarial attacks for classification, this new task faces two additional challenges: (1) low success rate due to the diverse appearance of images in the group; (2) low transferability across CoSOD methods due to the considerable difference between CoSOD pipelines. To address these challenges, we propose the very first blackbox joint adversarial exposure and noise attack (Jadena), where we jointly and locally tune the exposure and additive perturbations of the image according to a newly designed high-feature-level contrast-sensitive loss function. Our method, without any information on the state-of-the-art CoSOD methods, leads to significant performance degradation on various co-saliency detection datasets and makes the co-salient objects undetectable. This can have strong practical benefits in properly securing the large number of personal photos currently shared on the Internet. Moreover, our method is potential to be utilized as a metric for evaluating the robustness of CoSOD methods.

Most existing deep neural networks (DNNs) are inexplicable and fragile, which can be easily deceived by carefully designed adversarial example with tiny undetectable noise. This allows attackers to cause serious consequences in many DNN-assisted scenarios without human perception. In the field of speaker recognition, the attack for speaker recognition system has been relatively mature. Most works focus on white-box attacks that assume the information of the DNN is obtainable, and only a few works study gray-box attacks. In this paper, we study blackbox attacks on the speaker recognition system, which can be applied in the real world since we do not need to know the system information. By combining the idea of transferable attack and query attack, our proposed method NMI-FGSM-Tri can achieve the targeted goal by misleading the system to recognize any audio as a registered person. Specifically, our method combines the Nesterov accelerated gradient (NAG), the ensemble attack and the restart trigger to design an attack method that generates the adversarial audios with good performance to attack blackbox DNNs. The experimental results show that the effect of the proposed method is superior to the extant methods, and the attack success rate can reach as high as 94.8% even if only one query is allowed.

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

A single RFID (Radio Frequency Identification) is a technology for the remote identification of objects or people. It integrates a reader that receives the information contained in an RFID tag through an RFID authentication protocol. RFID provides several security services to protect the data transmitted between the tag and the reader. However, these advantages do not prevent an attacker to access this communication and remaining various security and privacy issues in these systems. Furthermore, with the rapid growth of IoT, there is an urgent need of security authentication and confidential data protection. Authentication protocols based on elliptic curve cryptographic (ECC) were widely investigated and implemented to guarantee protection against the various attacks that can suffer an RFID system. In this paper, we are going to focus on a comparative study between the most efficient ECC-based RFID authentication protocols that are already published, and study their security against the different wireless attacks.

Study on the effect of nanosecond laser anneal (NLA) induced crystallization of ferroelectric (FE) Si-doped hafnium oxide (HSO) material is reported. The laser energy density (0.3 J/cm2 to 1.3 J/cm2) and pulse count (1.0 to 30) variations are explored as pathways for the HSO based metal-ferroelectric-metal (MFM) capacitors. The increase in energy density shows transition toward ferroelectric film crystallization monitored by the remanent polarization (2Pr) and coercive field (2Ec). The NLA conditions show maximum 2Pr (\$\textbackslashsim 24\textbackslash \textbackslashmu\textbackslashmathrmC/\textbackslashtextcmˆ2\$) comparable to the values obtained from reference rapid thermal processing (RTP). Reliability dependence in terms of fatigue (107 cycles) of MFMs on NLA versus RTP crystallization anneal is highlighted. The NLA based MFMs shows improved fatigue cycling at high fields for the low energy densities compared to an RTP anneal. The maximum fatigue cycles to breakdown shows a characteristic dependence on the laser energy density and pulse count. Leakage current and dielectric breakdown of NLA based MFMs at the transition of amorphous to crystalline film state is reported. The role of NLA based anneal on ferroelectric film crystallization and MFM stack reliability is reported in reference with conventional RTP based anneal.

Contrary to previous opinion, ‘frost shattering’ is not the only major contributor to rock weathering at mid latitudes and high elevations, more specifically along edges of bedrock escarpments. Lightning is also a significant contributor to land surface denudation. We can show this as lightning strikes on outcrops can dramatically alter the magnetic signature of rocks and is one of the main sources of noise in paleomagnetic studies. Igneous rocks in the highlands of Lesotho, southern Africa (\textgreater 3000 m elevation) provide an ideal study location, as flow lavas remain as prominent ridges that are relatively resistant to weathering. It is well known that lightning strikes can cause large remanent magnetization in rocks with little resultant variation in susceptibility. At two adjoining peaks in the Lesotho highlands, mapped freshly fractured rock correlates with areas of high magnetic intensity (remanent component), but little variation in susceptibility (related to the induced field), and is therefore a clear indicator of lightning damage. The majority of these mapped strike sites occur at the edges of topographic highs. Variations in magnetic intensity are correlated with the much lower resolution national lightning strikes dataset. These data confirm that high elevation edges of peak scarps are the focus of previous lightning strikes. This method of magnetic surveying compared with lightning strike data is a new method of confirming the locations of lightning strikes, and reduces the need for intensive paleomagnetic studies of the area to confirm remanence.