Biblio

Underwater Wireless Sensor Networks (UWSN) has vast application areas. Due to the unprotected nature, underwater security is a prime concern. UWSN becomes vulnerable to different attacks due to malicious nodes. Sybil attack is one of the major attacks in UWSN. Most of the proposed security methods are based on encryption and decryption which consumes resources of the sensor nodes. In this paper, a simple authentication mechanism is proposed for securing the UWSN from the Sybil attack. As the nodes have very less computation power and energy resources so this work is not followed any kind of encryption and decryption technique. An authentication process is designed in such a way that node engaged in communication authenticate neighboring nodes by node ID and the data stored in the cluster head. This work is also addressed sensor node compromisation issue through Hierarchical Fuzzy System (HFS) based trust management model. The trust management model has been simulated in Xfuzzy-3.5. After the simulation conducted, the proposed trust management mechanism depicts significant performance on detecting compromised nodes.

Fog Computing was envisioned to solve problems like high latency, mobility, bandwidth, etc. that were introduced by Cloud Computing. Fog Computing has enabled remotely connected IoT devices and sensors to be managed efficiently. Nonetheless, the Fog-Cloud paradigm suffers from various security and privacy related problems. Blockchain ensures security in a trustless way and therefore its applications in various fields are increasing rapidly. In this work, we propose a Fog-Cloud architecture that enables Blockchain to ensure security, scalability, and privacy of remotely connected IoT devices. Furthermore, our proposed architecture also efficiently manages common problems like ever-increasing latency and energy consumption that comes with the integration of Blockchain in Fog-Cloud architecture.

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units — Application-specific Integrated Circuits (ASICs) specialized for Machine Learning — may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating ℤ2 quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

With the rapid growth of Internet of Things (IoT) network intrusion attacks, there is a critical need for sophisticated and comprehensive intrusion detection systems (IDSs). Classifying infrequent intrusion types such as root-to-local (R2L) and user-to-root (U2R) attacks is a reoccurring problem for IDSs. In this study, various data sampling and class balancing techniques-Generative Adversarial Network (GAN)-based oversampling, k-nearest-neighbor (kNN) oversampling, NearMiss-1 undersampling, and class weights-were used to resolve the severe class imbalance affecting U2R and R2L attacks in the NSL-KDD intrusion detection dataset. Artificial Neural Networks (ANNs) were trained on the adjusted datasets, and their performances were evaluated with a multitude of classification metrics. Here, we show that using no data sampling technique (baseline), GAN-based oversampling, and NearMiss-l undersampling, all with class weights, displayed high performances in identifying R2L and U2R attacks. Of these, the baseline with class weights had the highest overall performance with an F1-score of 0.11 and 0.22 for the identification of U2R and R2L attacks, respectively.

This paper describes the architecture and the fundamental methodology of an anomaly detector, which by continuously monitoring Simple Network Management Protocol data and by processing it as complex-events, is able to timely recognize patterns of faults and relevant cyber-attacks. This solution has been applied in the context of smart grids, and in particular as part of a security and resilience component of the Information and Communication Technologies (ICT) Gateway, a middleware-based architecture that correlates and fuses measurement data from different sources (e.g., Inverters, Smart Meters) to provide control coordination and to enable grid observability applications. The detector has been evaluated through experiments, where we selected some representative anomalies that can occur on the ICT side of the energy distribution infrastructure: non-malicious faults (indicated by patterns in the system resources usage), as well as effects of typical cyber-attacks directed to the smart grid infrastructure. The results show that the detection is promisingly fast and efficient.

The traditional Proof of Existence blockchain service on the Bitcoin network can be used to verify the existence of any research data at a specific point of time, and to validate the data integrity, without revealing its content. Several variants of the blockchain service exist to certify the existence of data relying on cryptographic fingerprinting, thus enabling an efficient verification of the authenticity of such certifications. However, nowadays research data is continuously changing and being modified through different processing steps in most scientific research workflows such that certifications of individual data objects seem to be constantly outdated in this setting. This paper describes how the blockchain and distributed ledger technology can be used to form a new certification model, that captures the research process as a whole in a more meaningful way, including the description of the used data through its different stages and the associated computational pipeline, code for analysis and the experimental design. The scientific blockchain infrastructure bloxberg, together with a deep learning based analysis from the behavioral science field are used to show the applicability of the approach.

As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety- and security-critical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models' robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes a simple and novel approach aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.

Ad Hoc Network is wireless networks that get more attention from past to present. Mobile ad hoc network (MANET) is one of the types of ad hoc networks, it deployed rapidly because it infrastructure-less. A node in a mobile ad hoc network communicates through wireless links without wired channels. When source nodes want to communicate with the destination outside its transmission range it uses multi-hop mechanisms. The intermediate node forwards the data packet to the next node until the data packet reaches its destination. Due wireless links and lack of centralized administration device, mobile ad hoc network is more vulnerable for security attacks. The rushing attack is one of the most dangerous attacks in the on-demand routing protocol of mobile ad hoc networks. Rushing attack highly transmits route request with higher transmission power than the genuine nodes and become participate between source and destination nodes, after that, it delays or drop actual data pass through it. In this study, the researcher incorporates rushing attack in one of the most commonly used mobile ad hoc network routing protocols namely Ad hoc on-demand multipath distance vector and provides a rushing attack prevention method based on the time threshold value and random route selection. Based on the time RREQ arrives a node takes a decision, if the RREQ packet arrives before threshold value, the RREQ packet consider as came from an attacker and discarded else RREQ packet received then randomly select RREQ to forward. In this study performance metrics like packet delivery ratio, end-to-end delay and throughput have been evaluated using Network simulation (NS-2.35). As a result of simulation shows newly proposed prevention mechanism improves network performance in all cases than the network under attacker. For example, the average packet delivery ratio enhanced from 54.37% to 97.69%, throughput increased from 20.84bps to 33.06bpsand the average delay decreased from 1147.22ms to 908.04ms. It is concluded that the new proposed techniques show improvement in all evaluated performance metrics.

We study communication systems based on chaotic time-delayed feedback generator. The aim of the study is a comparative assessment of the noise immunity for the four different communication systems at the same levels of the external noise. It is shown that the principle of correlation receiver, which is used in classical communication systems, can be also used in the case where chaotic signals generated by self-oscillating systems with complex behavior are used as reference signals. Systems based on the correlation receiver principles have very high immunity to the external noise.

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.

Mobile Ad Hoc Network (MANET) is one of the types of Ad-hoc Network which is comprised of wireless in a network. The main problem in this research is the vulnerability of the protocol routing Dymo against jellyfish attack, so it needs detection from a jellyfish attack. This research implements the DELPHI method to detect jellyfish attacks on a DYMO protocol which has better performance because the Delay Per-Hop Indicator (DELPHI) gathers the amount of hop and information delay from the disjoint path and calculates the delays per-hop as an indicator of a jellyfish attack. The evaluation results indicate an increase in the end-to-end delay average, start from 112.59s in 10 nodes increased to 143.732s in 30 nodes but reduced to 84,2142s in 50 nodes. But when the DYMO routing did not experience any jellyfish attacks both the delivery ratio and throughput are decreased. The delivery ratio, where decreased from 10.09% to 8.19% in 10 nodes, decreased from 20.35% to 16.85%, and decreased from 93.5644% to 82.825% in 50 nodes. As for the throughput, for 10 nodes decreased from 76.7677kbps to 68.689kbps, for 30 nodes decreased from 100kbps to 83.5821kbps and for 50 nodes decreased from 18.94kbps to 15.94kbps.

Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Symmetric key foundation is the most appropriate model for secure exchanges in WSNs among the ideal models. The core objective is to enhance and evaluate certain issues like attack on the nodes, to provide better key strength, better connectivity, security in interaction among the nodes. The keys distributed by the base station to cluster head are generated using Symmetric Balanced Incomplete Block Design (SBIBD). The keys distributed by cluster head to its member nodes are generated using Symmetric Balanced Incomplete Block Design (SBIBD) and Keys are refreshed periodically to avoid stale entries. Compromised sensor nodes can be used to insert false reports (spurious reports) in wireless sensor networks. The idea of interaction between the sensor nodes utilizing keys and building up a protected association helps in making sure the network is secure. Compared with similar existing schemes, our approach can provide better security.

This study focuses on the behavior of magnetic properties and electromagnetic absorption of MgFe2O4 prepared by powder metallurgy. Magnesium ferrite was synthesized using oxide precursors (MgO and Fe2 O3). The samples were calcined at 700 °C for 3 hours and sintered at 1100 °C for 24 hours with varying compaction pressure (80 kg/cm2, 90 kg/cm2, 100 kg/cm2). Magnesium ferrites were characterized using an X-Ray Diffraction (XRD) for their crystal structure analysis, a Scanning Electron Microscope equipped with an Energy Dispersive Spectroscopy (SEM-EDS) for their microstructure and elemental composition studies, a Permagraph for their magnetic properties, and a Vector Network Analysis (VNA) for their microwave absorption characteristics. XRD patterns shows primary phase of MgFe2O4 and secondary phase of Fe2 O3 present in all three samples. The SEM characterization reveal the microstructure of magnesium ferrite and the EDS spectra confirm the presence of Fe, Mg, and O. The hysteresis curves show that the values of remanence magnetic induction (Br) are 17.5 emu/g, 16.5 emu/g, and 14.5 emu/g, respective to the increasing compaction pressure. Saturation magnetization values are increasing whereas the coercivity values are found to have inconsistent change with increasing compaction pressure. According to VNA results, the values of reflection loss are -16.15 dB, -22.45 dB, and -27.55 dB, respectively.

These days, almost everyone has been entirely relying on mobile devices and mobile related applications running on Android Operating Systems, the most used Mobile Operating System in the world with the largest market share. These Mobile devices and applications can become an information goldmine for hackers and are considered one of the significant concerns mobile users face who stand a chance of being victimized during data breach from hackers due to lapse in information security and controls. Such challenge can be put to bare through systematic digital forensic analysis through penetration testing for a humanoid robot like Zenbo, which run Android OS and related application, to help identify associated security vulnerabilities and develop controls required to improve security using popular penetration testing tools such as Drozer, Mobile Application Security framework (mobSF), and AndroBugs with the help of Santoku Linux distribution.

In recent years we can observe that digital forensics is being applied to a variety of domains as nearly any data can become valuable forensic evidence. The sheer scope of web-based investigations provides a vast amount of information. Due to a rapid increase in the number of cybercrimes the importance of application-specific forensics is greater than ever. Criminals use the application not only to communicate but also to facilitate crimes. It came to our attention that the gaming chat application Discord is one of them. Discord allows its users to send text messages as well as exchange image, video, and audio files. While Discord's community is not as large as that of the most popular messaging apps the stable growth of its userbase and recent incidents indicate that it is used by criminals. This paper presents our research into the digital forensic analysis of Discord client-side artefacts and presents experimental development of a tool for extraction, analysis, and presentation of the data from Discord application. The work then proposes a solution in form of a tool, `DiscFor', that can retrieve information from the application's local files and cache storage.

Context: The technical debt (TD) metaphor helps to conceptualize the pending issues and trade-offs made during software development. Knowing TD causes can support in defining preventive actions and having information about effects aids in the prioritization of TD payment. Goal: To investigate the impact of the experience level on how practitioners perceive the most likely causes that lead to TD and the effects of TD that have the highest impacts on software projects. Method: We approach this topic by surveying 227 practitioners. Results: While experienced software developers focus on human factors as TD causes and external quality attributes as TD effects, low experienced developers seem to concentrate on technical issues as causes and internal quality issues and increased project effort as effects. Missing any of these types of causes could lead a team to miss the identification of important TD, or miss opportunities to preempt TD. On the other hand, missing important effects could hamper effective planning or erode the effectiveness of decisions about prioritizing TD items. Conclusion: Having software development teams composed of practitioners with a homogeneous experience level can erode the team's ability to effectively manage TD.

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

An important demand of a wearable health observance system is to soundly exchange the Employees' health data and preventing improper use of black devices. In this project we tend to measure planning wearable sensors device sight abnormal and/or unforeseen things by observance physiological parameters alongside different symptoms. Therefore, necessary facilitate is provided in times of urgent would like. To minimize the health hazards and improving the well-being of employees is to be a major critical role in an organization. As per the report by the Indian Labour Organization, the organization spends an average of 3.94% for GDP on employee treatment. The same study revealed that almost 2.78% million deaths occurs every year and 3.74% million occur non-fatal injuries every year at work. So, the organizations are making towards mitigating the facilities to decimating various IoT technologies and the IoT technology are embedded with modern smart systems, it is easy to monitor every employee in an organization, and also it collects and gather the data and send any critical information by the employees.

Mathematical morphology has been explored in deep learning architectures, as a substitute to convolution, for problems like pattern recognition and object detection. One major advantage of using morphology in deep learning is the utility of morphological erosion and dilation. Specifically, these operations naturally embody interpretability due to their underlying connections to the analysis of geometric structures. While the use of these operations results in explainable learned filters, morphological deep learning lacks attribution modeling, i.e., a paradigm to specify what areas of the original observed image are important. Furthermore, convolution-based deep learning has achieved attribution modeling through a variety of neural eXplainable Artificial Intelligence (XAI) paradigms (e.g., saliency maps, integrated gradients, guided backpropagation, and gradient class activation mapping). Thus, a problem for morphology-based deep learning is that these XAI methods do not have a morphological interpretation due to the differences in the underlying mathematics. Herein, we extend the neural XAI paradigm of saliency maps to morphological deep learning, and by doing, so provide an example of morphological attribution modeling. Furthermore, our qualitative results highlight some advantages of using morphological attribution modeling.

Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work.

Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.

In this work, security analysis of a Smart Home System (SHS) is inspected. The paper focuses on describing common and likely cyber security threats against SHS. This includes both their influence on human privacy and safety. The SHS is properly presented and formed applying Architecture Analysis and Design Language (AADL), exhibiting the system layout, weaknesses, attack practices, besides their requirements and post settings. The obtained model is later inspected along with a security requirement with JKind model tester software for security endangerment. The overall attack graph causing system compromise is graphically given using Graphviz.

In some cases, the confidentiality of cryptographic algorithms used in digital communication is related to computational complexity mathematical problems, such as calculating the discrete logarithm, the knapsack problem, decomposing a composite number into prime divisors etc. This article describes the application of insolvability of factorization of a large composite number, and reviews previous work integer factorization using either the deterministic or the bio-inspired algorithms. This article focuses on the possibility of using bio-inspired methods to solve the problem of cryptanalysis of asymmetric encryption algorithms, which ones based on factorization of composite numbers. The purpose of this one is to reviewing previous work in integer factorization algorithms, developing a prototype of either the deterministic and the bio-inspired algorithm and the effectiveness of the developed algorithms and recommendations are made for future research paths.

Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).

When location-based services (LBS) are delivered, location data should be protected against honest-but-curious LBS providers, them being quasi-identifiers. One of the existing approaches to achieving this goal is location k-anonymity, which leverages the presence of a trusted party, called location trusted service (LTS), playing the role of anonymizer. A drawback of this approach is that the location trusted service is a single point of failure and traces all the users. Moreover, the protection is completely nullified if a global passive adversary is allowed, able to monitor the flow of messages, as the source of the query can be identified despite location k-anonymity. In this paper, we propose a distributed and hierarchical LTS model, overcoming both the above drawbacks. Moreover, position notification is used as cover traffic to hide queries and multicast is minimally adopted to hide responses, to keep k-anonymity also against the global adversary, thus enabling the possibility that LBS are delivered within social networks.