Biblio

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.

In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.

Inter-networked control systems make smart buildings increasingly efficient but can lead to severe operational disruptions and infrastructure damage. It is vital the security state of smart buildings is properly assessed so that thorough and cost effective risk management can be established. This paper uniquely reports on an actual risk assessment performed in 2018 on one of the world's most densely monitored, state-of-the-art, smart buildings. From our observations, we suggest that current practice may be inadequate due to a number of challenges and deficiencies, including the lack of a recognised smart building risk assessment methodology. As a result, the security posture of many smart buildings may not be as robust as their risk assessments suggest. Crucially, we highlight a number of key recommendations for a more comprehensive risk assessment process for smart buildings. As a whole, we believe this practical experience report will be of interest to a range of smart building stakeholders.

When using deep learning for DDoS attack detection, there is a general degradation in detection performance due to small sample size. This paper proposes a small-sample DDoS attack detection method based on deep transfer learning. First, deep learning techniques are used to train several neural networks that can be used for transfer in DDoS attacks with sufficient samples. Then we design a transferability metric to compare the transfer performance of different networks. With this metric, the network with the best transfer performance can be selected among the four networks. Then for a small sample of DDoS attacks, this paper demonstrates that the deep learning detection technique brings deterioration in performance, with the detection performance dropping from 99.28% to 67%. Finally, we end up with a 20.8% improvement in detection performance by deep transfer of the 8LANN network in the target domain. The experiment shows that the detection method based on deep transfer learning proposed in this paper can well improve the performance deterioration of deep learning techniques for small sample DDoS attack detection.

Due to the network topology high dynamic changes, the number of ground users and the impact of uneven traffic, the load difference between SDN-based satellite network controllers varies widely, which will cause network performance such as network delay and throughput to drop dramatically. Aiming at the above problems, a multi-controller optimized deployment strategy of satellite network based on SDN was proposed. First, the controller's load state is divided into four types: overload state, high load state, normal state, and idle state; second, when a controller in the network is idle, the switch under its jurisdiction is migrated to the adjacent low load controller and turn off the controller to reduce waste of resources. When the controller is in a high-load state and an overload state, consider both the controller and the switch, and migrate the high-load switch to the adjacent low-load controller. Balance the load between controllers, improve network performance, and improve network performance and network security. Simulation results show that the method has an average throughput improvement of 2.7% and a delay reduction of 3.1% compared with MCDALB and SDCLB methods.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

Data Transmission in network security is one of the most vital issues in today's communication world. The outcome of the suggested method is outlined over here. Enhanced security can be achieved by this method. The vigorous growth in the field of information communication has made information transmission much easier. But this type of advancement has opened up many possibilities of information being snooped. So, day-by-day maintaining of information security is becoming an inseparable part of computing and communication. In this paper, the authors have explored techniques that blend cryptography & steganography together. In steganography, information is kept hidden behind a cover image. In this paper, approaches for information hiding using both cryptography & steganography is proposed keeping in mind two considerations - size of the encrypted object and degree of security. Here, signature image information is kept hidden into cover image using private key of sender & receiver, which extracts the information from stego image using a public key. This approach can be used for message authentication, message integrity & non-repudiation purpose.

the purpose of this paper is investigation of existing approaches to formation of electronic digital signatures, as well as the possibility of software developing for electronic signature generation at electronic document circulation of institution. The article considers and analyzes the existing algorithms for generating and processing electronic signatures. Authors propose the model for documented information exchanging in institution, including cryptographic module and secure key storage, blockchain storage of electronic signatures, central web-server and web-interface. Examples of the developed software are demonstrated, and recommendations are given for its implementation, integration and using in different institutions.

Implementations of Cyber-Physical Systems (CPS), like the Internet of Things, Smart Factories or Smart Grid gain more and more impact in their fields of application, as they extend the functionality and quality of the offered services significantly. However, the coupling of safety-critical embedded systems and services of the cyber-space domain introduce many new challenges for system engineers. Especially, the goal to achieve a high level of security throughout CPS presents a major challenge. However, it is necessary to develop and deploy secure CPS, as vulnerabilities and threats may lead to a non- or maliciously modified functionality of the CPS. This could ultimately cause harm to life of involved actors, or at least sensitive information can be leaked or lost. Therefore, it is essential that system engineers are aware of the level of security of the deployed CPS. For this purpose, security metrics and security evaluation frameworks can be utilized, as they are able to quantitatively express security, based on different measurements and rules. However, existing security scoring solutions may not be able to generate accurate security scores for CPS, as they insufficiently consider the typical CPS characteristics, like the communication of heterogeneous systems of physical- and cyber-space domain in an unpredictable manner. Therefore, we propose a security analysis framework, called Security Qualification Matrix (SQM). The SQM is capable to analyses multiple attacks on a System-of-Systems level simultaneously. With this approach, dependencies, potential side effects and the impact of mitigation concepts can quickly be identified and evaluated.

The security problem of networked control systems (NCSs) suffering denial of service(DoS) attacks with incomplete information is investigated in this paper. Data transmission among different components in NCSs may be blocked due to DoS attacks. We use the concept of security level to describe the degree of security of different components in an NCS. Intrusion detection system (IDS) is used to monitor the invalid data generated by DoS attacks. At each time slot, the defender considers which component to monitor while the attacker considers which place for invasion. A one-shot game between attacker and defender is built and both the complete information case and the incomplete information case are considered. Furthermore, a repeated game model with updating beliefs is also established based on the Bayes' rule. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.

Trust among humans affects the way we interact with each other. In autonomous systems, this trust is often predefined and hard-coded before the systems are deployed. However, when systems encounter unfolding situations, requiring them to interact with others, a notion of trust will be inevitable. In this paper, we discuss trust as a fundamental measure to enable an autonomous system to decide whether or not to interact with another system, whether biological or artificial. These decisions become increasingly important when continuously integrating with others during runtime.

E- Health systems, specifically, Telecare Medical Information Systems (TMIS), are deployed in order to provide patients with specific diseases with healthcare services that are usually based on remote monitoring. Therefore, making an efficient, convenient and secure connection between users and medical servers over insecure channels within medical services is a rather major issue. In this context, because of the biometrics' characteristics, many biometrics-based three factor user authentication schemes have been proposed in the literature to secure user/server communication within medical services. In this paper, we make a brief study of the most interesting proposals. Then, we propose a new three-factor authentication and key agreement scheme for TMIS. Our scheme tends not only to fix the security drawbacks of some studied related work, but also, offers additional significant features while minimizing resource consumption. In addition, we perform a formal verification using the widely accepted formal security verification tool AVISPA to demonstrate that our proposed scheme is secure. Also, our comparative performance analysis reveals that our proposed scheme provides a lower resource consumption compared to other related work's proposals.

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.

Recognizing the need for proactive analysis of cyber adversary behavior, this paper presents a new event-driven simulation model and implementation to reveal the efforts needed by attackers who have various entry points into a network. Unlike previous models which focus on the impact of attackers' actions on the defender's infrastructure, this work focuses on the attackers' strategies and actions. By operating on a request-response session level, our model provides an abstraction of how the network infrastructure reacts to access credentials the adversary might have obtained through a variety of strategies. We present the current capabilities of the simulator by showing three variants of Bronze Butler APT on a network with different user access levels.

Public-key cryptography is a ubiquitous buildingblock of modern telecommunication technology. Among the most historically important, the knapsack-based encryption schemes, from the early years of public-key cryptography, performed particularly well in computational resources (time and memory), and mathematical and algorithmic simplicity. Although effective cryptanalyses readily curtailed their widespread adoption to several different attempts, the possibility of actual usage of knapsack-based asymmetric encryption schemes remains unsettled. This paper aims to present a novel construction that offers consistent security improvements on knapsack-based cryptography. We propose two improvements upon the original knapsack cryptosystem that address the most important types of attacks: the Diophantine approximationsbased attacks and the lattice problems oracle attacks. The proposed defences demonstrably preclude the types of attacks mentioned above, thus contributing to revive knapsack schemes or settle the matter negatively. Finally, we present the http://t3infosecurity.com/nepsecNep.Sec, a contest that is offering a prize for breaking our proposed cryptosystem.

One of the significant advancements in information technology is Cloud computing, but the security issue of data storage is a big problem in the cloud environment. That is why a system is proposed in this paper for improving the security of cloud data using encryption, information concealment, and hashing functions. In the data encryption phase, we implemented hybrid encryption using the algorithm of AES symmetric encryption and the algorithm of RSA asymmetric encryption. Next, the encrypted data will be hidden in an image using LSB algorithm. In the data validation phase, we use the SHA hashing algorithm. Also, in our suggestion, we compress the data using the LZW algorithm before hiding it in the image. Thus, it allows hiding as much data as possible. By using information concealment technology and mixed encryption, we can achieve strong data security. In this paper, PSNR and SSIM values were calculated in addition to the graph to evaluate the image masking performance before and after applying the compression process. The results showed that PSNR values of stego-image are better for compressed data compared to data before compression.

Mobile sensors are playing a vital role in various applications of a normal day life. Key size in securing data is an important issue to highlight in mobile sensor data transfer between a smart device and a data storage component. Such key size may affect memory storage and processing power of a mobile device. Therefore, we proposed a secure mobile sensor data transfer protocol called secure sensor protocol (SSP). SSP is based on Elliptic Curve Cryptography (ECC), which generates small size key in contrast to conventional asymmetric algorithms like RSA and Diffie Hellman. SSP receive values from light sensor and magnetic flux meter of a smart device. SSP encrypts mobile sensor data using ECC and afterwards it stores cipher information in MySQL database to receive remote data access. We compared the performance of the ECC with other existing asymmetric cryptography algorithms in terms of secure mobile sensor data transfer based on data encryption and decryption time, key size and encoded data size. In-addition, SSP shows better results than other cryptography algorithms in terms of secure mobile sensor data transfer.

Image Encryption is a technique where an algorithm along with a set of characters called key encrypts the data into cipher text. The cipher text can be converted back into plaintext by decryption. This technique is employed for the security of data such that confidentiality, integrity and authenticity of data is maintained. In today's era security of information has become a crucial task, unauthorized access and use of data has become a noticeable issue. To provide the security required, there are several algorithms to suit the purposes. While the use and transferring of images has become easy and faster due to technological advancements especially wireless sensor network, image destruction and illegitimate use has become a potential threat. Different transfer mediums and various uses of images require different and appropriately suiting encryption approaches. Hence, in this paper we discuss the types of image encryption techniques. We have also discussed several encryption algorithms, their advantages and suitability.

Traditionally Industrial Control System(ICS) used air-gap mechanism to protect Operational Technology (OT) networks from cyber-attacks. As internet is evolving and so are business models, customer supplier relationships and their needs are changing. Hence lot of ICS are now connected to internet by providing levels of defense strategies in between OT network and business network to overcome the traditional mechanism of air-gap. This upgrade made OT networks available and accessible through internet. OT networks involve number of physical objects and computer networks. Physical damages to system have become rare but the number of cyber-attacks occurring are evidently increasing. To tackle cyber-attacks, we have a number of measures in place like Firewalls, Intrusion Detection System (IDS) and Intrusion Prevention System (IPS). To ensure no attack on or suspicious behavior within network takes place, we can use visual aids like creating dashboards which are able to flag any such activity and create visual alert about same. This paper describes creation of parser object to convert Common Event Format(CEF) to Comma Separated Values(CSV) format and dashboard to extract maximum amount of data and analyze network behavior. And working of active querying by leveraging packet level data from network to analyze network inclusion in real-time. The mentioned methodology is verified on data collected from Waste Water Treatment Plant and results are presented.,} booktitle = {2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

With increasing monitoring and regulation by platforms, communities with criminal interests are moving to the dark web, which hosts content ranging from whistle-blowing and privacy, to drugs, terrorism, and hacking. Using post discussion data from six dark web forums we construct six interaction graphs and use social network analysis tools to study these underground communities. We observe the structure of each network to highlight structural patterns and identify nodes of importance through network centrality analysis. Our findings suggest that in the majority of the forums some members are highly connected and form hubs, while most members have a lower number of connections. When examining the posting activities of central nodes we found that most of the central nodes post in sub-forums with broader topics, such as general discussions and tutorials. These members play different roles in the different forums, and within each forum we identified diverse user profiles.

Nowadays, there is a flood of data such as naked body photos and child pornography, which is making people bloodless. In addition, people also distribute drugs through unknown dark channels. In particular, most transactions are being made through the Deep Web, the dark path. “Deep Web refers to an encrypted network that is not detected on search engine like Google etc. Users must use Tor to visit sites on the dark web” [4]. In other words, the Dark Web uses Tor's encryption client. Therefore, users can visit multiple sites on the dark Web, but not know the initiator of the site. In this paper, we propose the key idea based on the current status of such crimes and a crime information visual system for Deep Web has been developed. The status of deep web is analyzed and data is visualized using Java. It is expected that the program will help more efficient management and monitoring of crime in unknown web such as deep web, torrent etc.

A breakthrough in the emerging use of machine learning and deep learning is the concept of autoencoders and GAN (Generative Adversarial Networks), architectures that can generate believable synthetic content called deepfakes. The threat lies when these low-tech doctored images, videos, and audios blur the line between fake and genuine content and are used as weapons to cause damage to an unprecedented degree. This paper presents a survey of the underlying technology of deepfakes and methods proposed for their detection. Based on a detailed study of all the proposed models of detection, this paper presents SSTNet as the best model to date, that uses spatial, temporal, and steganalysis for detection. The threat posed by document and signature forgery, which is yet to be explored by researchers, has also been highlighted in this paper. This paper concludes with the discussion of research directions in this field and the development of more robust techniques to deal with the increasing threats surrounding deepfake technology.

Federated learning (FL), as a recent advance of distributed machine learning, is capable of learning a model over the network without directly accessing the client's raw data. Nevertheless, the clients' sensitive information can still be exposed to adversaries via differential attacks on messages exchanged between the parameter server and clients. In this paper, we consider the widely used federating averaging (FedAvg) algorithm and propose to enhance the data privacy by the differential privacy (DP) technique, which obfuscates the exchanged messages by properly adding Gaussian noise. We analytically show that the proposed secure FedAvg algorithm maintains an O(l/T) convergence rate, where T is the total number of stochastic gradient descent (SGD) updates for local model parameters. Moreover, we demonstrate how various algorithm parameters can impact on the algorithm communication efficiency. Experiment results are presented to justify the obtained analytical results on the performance of the proposed algorithm in terms of testing accuracy.