Biblio

Reliable group communication is an important cornerstone for various applications in the domain of Industrial Internet of Things (IIoT). Yet, despite various proposals, state-of- the-art (open) protocol stacks for IPv6-enabled Low Power and Lossy Networks (LLNs) have little to offer, regarding standardized or agreed-upon protocols for correct multicast routing, not to mention reliable multicast. We present an informal computational model, which allows us to analyze the respective candidates for multicast routing. Further, we focus on the IEEE 802.15.4/6LoWPAN stack and discuss prominent multicast routing protocols and how they fit into this model.

In MANET's (MOBILE Adhoc Network), the origin node will communicate the target node with the help of in-between nodes by Multi-hop communication to save power [1]. Thus, the main objective in MANETs is to identify the feasible route such that the parcels of the data can be done in an organized manner. So, the nodes in the selected route are honest and reliable. However, bad behavior nodes may affect routing performance. This work aims to discover the route does not have egotistic nodes, i.e., nodes which having honest & energy are less not considered for the route between origin and target. The proposed procedure holds the input from the end-user and results in the boundary values i.e. avg. throughput, appropriateness and drop fraction of egotistic nodes were stored in a result location. After the simulation, the discovered route by using the proposed protocol improves the overall network performance for output parameters.

In this paper, we propose two new attacks: the Adversarial Universal False Positive (UFP) Attack and the Adversarial Universal False Negative (UFN) Attack. The objective of this research is to introduce a new class of attack using only feature vector information. The results show the potential weaknesses of five machine learning (ML) classifiers. These classifiers include k-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forrest (RF), a Support Vector Machine (SVM) with a Radial Basis Function (RBF) Kernel, and XGBoost (XGB).

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.

Insider threat is one of the most pernicious threat vectors to information and communication technologies (ICT) across the world due to the elevated level of trust and access that an insider is afforded. This type of threat can stem from both malicious users with a motive as well as negligent users who inadvertently reveal details about trade secrets, company information, or even access information to malignant players. In this paper, we propose a novel approach that uses system logs to detect insider behavior using a special recurrent neural network (RNN) model. Ground truth is established using DANTE and used as baseline for identifying anomalous behavior. For this, system logs are modeled as a natural language sequence and patterns are extracted from these sequences. We create workflows of sequences of actions that follow a natural language logic and control flow. These flows are assigned various categories of behaviors - malignant or benign. Any deviation from these sequences indicates the presence of a threat. We further classify threats into one of the five categories provided in the CERT insider threat dataset. Through experimental evaluation, we show that the proposed model can achieve 93% prediction accuracy.

Most organizations use web applications for sharing resources and communication via the internet and information security is one of the biggest concerns in most organizations. Web applications are becoming vulnerable to threats and malicious attacks every day, which lead to violation of confidentiality, integrity, and availability of information assets.We have proposed and implemented a new automated tool for the identification and mitigation of Cross-Site Request Forgery (CSRF) vulnerability. A secret token pattern based has been used in the automated tool, which applies effective security mechanism on PHP based web applications, without damaging the content and its functionalities, where the authenticated users can perform web activities securely.

Quality Critical Decentralised Applications (QC-DApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers to deliver their business value. The development of the QCDApp has to tackle the low-performance challenge of the current blockchain technologies due to the low collaboration efficiency among distributed peers for consensus. On the other hand, the resilience of the Cloud has enabled significant advances in software-defined storage, networking, infrastructure, and every technology; however, those rich programmabilities of infrastructure (in particular, the advances of new hardware accelerators in the infrastructures) can still not be effectively utilised for QCDApp due to lack of suitable architecture and programming model.

The article is devoted to the choice of personal means of the 5G defense in dependence of hard- and software available to the user. The universal module MS 127.04 and its software compatible unit can be universally configured for use. An intelligent hardware and software platform is proposed for multi-core setting of policies for the automatic encryption of confidential data and selective blocking related to the implementation of computing security and confidentiality of data transfer, using such additional specially. A platform that resists the external influences is described. The platform is based on a universal module MS 127.05 (produced in Russia), that is a heterogeneous multiprocessor system on a chip), the system features 16 processor cores (NeuroMatrix Core 4) and five ARM Cortex-A5 units (ULSI 1879VM8Ya.

This article describes the process assembling of Encryption & Decryption In Trusted Platform Module of encoding information in NM640X \textregistered. Encoding of information, carried out in assembly language according to Gost 28147-89. It is a realisation of standard GOST 28147-89- Russian state symmetric key block cipher. GOST 28147-89 has 64-bit to access the kernel, trust, and allocated memory in the BlockSize and 256-bit KeySize.

Nowadays, Security is the biggest challenge in communication networks. Well defined security protocols not only solve the privacy and security issues but also help to reduce the implementation cost and simplify network's operation. Network society demands more reliable and secure network services as well as infrastructure. In communication networks, data theft, hacking, fraud, cyber warfare are serious security threats. Security as defined by experts is confirming protected communication amongst communication/computing systems and consumer applications in private and public networks, it is important for promising privacy, confidentiality, and protection of information. This paper highlights the security related issues and challenges in communication networks. We also present the holistic view for the underlaying physical layer including physical infrastructure attacks, jamming, interception, and eavesdropping. This research focused on improving the security measures and protocols in different communication networks.

Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.

We present our journey in constructing the first integrated data warehouse for Philippine transportation research in the hopes of developing a Transportation Decision Support System for impact studies and policy making. We share how we collected data from diverse sources, processed them into a homogeneous format and applied them to our multimodal platform. We also list the challenges we encountered, including bureaucratic delays, data privacy concerns, lack of software, and overlapping datasets. The data warehouse shall serve as a public resource for researchers and professionals, and for government officials to make better-informed policies. The warehouse will also function within our multi-modal platform for measurement, modelling, and visualization of road transportation. This work is our contribution to improve the transportation situation in the Philippines, both in the local and national levels, to boost our economy and overall quality of life.

In order to develop safety-reliable standards for IoT (Internet of Things) networks, appropriate tools for their verification are needed. Among them there is a group of tools based on automated symbolic analysis. Such a tool is Tamarin software. Its usage for creating formal proofs of security protocols correctness has been presented in this paper using the simple example of an exchange of messages with asynchronous encryption between two agents. This model can be used in sensor networks or IoT e.g. in TLS protocol to provide a mechanism for secure cryptographic key exchange.

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.

Online privacy policies are lengthy and hard to comprehend. To address this problem, researchers have utilized machine learning (ML) to devise tools that automatically summarize online privacy policies for web users. One such tool is our free and publicly available browser extension, PrivacyCheck. In this paper, we enhance PrivacyCheck by adding a competitor analysis component-a part of PrivacyCheck that recommends other organizations in the same market sector with better privacy policies. We also monitored the usage patterns of about a thousand actual PrivacyCheck users, the first work to track the usage and traffic of an ML-based privacy analysis tool. Results show: (1) there is a good number of privacy policy URLs checked repeatedly by the user base; (2) the users are particularly interested in privacy policies of software services; and (3) PrivacyCheck increased the number of times a user consults privacy policies by 80%. Our work demonstrates the potential of ML-based privacy analysis tools and also sheds light on how these tools are used in practice to give users actionable knowledge they can use to pro-actively protect their privacy.

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

Power systems automation and communication standards are crucial for the transition of the conventional power system towards a smart grid. The IEC 61850 standard is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation devices. IEC 61850 serves as the foundation for open communication and data exchange for digital substations of the smart grid. However, IEC 61850 has cyber security vulnerabilities that can be exploited with a man-in-the-middle attack. Such coordinated cyber attacks against the protection system in digital substations can disconnect generation and transmission lines, causing cascading failures. In this paper, we demonstrate a cyber attack involving the Generic Object-Oriented Substation Event (GOOSE) protocol of IEC 61850. This is achieved by exploiting the cyber security vulnerabilities in the protocol and injecting spoofed GOOSE data frames into the substation communication network at the bay level. The cyber attack leads to tripping of multiple protective relays in the power grid, eventually resulting in a blackout. The attack model and impact on system dynamics are verified experimentally through hardware-in-the-loop simulations using commercial relays and Real-Time Digital Simulator (RTDS).

Power system automation and communication standards are spearheading the power system transition towards a smart grid. IEC 61850 is one such standard, which is widely used for substation automation and protection. It enables real-time communication and data exchange between critical substation automation and protection devices within digital substations. However, IEC 61850 is not cyber secure. In this paper, we demonstrate the dangerous implications of not securing IEC 61850 standard. Cyber attacks may exploit the vulnerabilities of the Sampled Values (SV) and Generic Object-Oriented Substation Event (GOOSE) protocols of IEC 61850. The cyber attacks may be realised by injecting spoofed SV and GOOSE data frames into the substation communication network at the bay level. We demonstrate that such cyber attacks may lead to obstruction or tripping of multiple protective relays. Coordinated cyber attacks against the protection system in digital substations may cause generation and line disconnections, triggering cascading failures in the power grid. This may eventually result in a partial or complete blackout. The attack model, impact on system dynamics and cascading failures are veri ed experimentally through a proposed cyber-physical experimental framework that closely resembles real-world conditions within a digital substation, including Intelligent Electronic Devices (IEDs) and protection schemes. It is implemented through Hardware-in-the-Loop (HIL) simulations of commercial relays with a Real-Time Digital Simulator (RTDS).

Inter-substation Generic Object Oriented Substation Events (GOOSE) communications that are used for critical protection functions have several cyber-security vulnerabilities. GOOSE messages are directly mapped to the Layer 2 Ethernet without network and transport layer headers that provide data encapsulation. The high-status-number attack is a malicious attack on GOOSE messages that allows hackers to completely take over intelligent electronic devices (IEDs) subscribing to GOOSE communications. The status-number parameter of GOOSE messages, stNum is tampered with in these attacks. Given the strict delivery time requirement of 3 ms for GOOSE messaging, it is infeasible to encrypt the GOOSE payload. This work proposes to secure the sensitive stNum parameter of the GOOSE payload using systematically encoded polynomial codes. Exploiting linear codes allows for the security features to be encoded in linear time, in contrast to complex hashing algorithms. At the subscribing IED, the security feature is used to verify that the stNum parameter has not been tampered with during transmission in the insecure medium. The decoding and verification using syndrome computation at the subscriber IED is also accomplished in linear time.

ARP-attack often occurs in LAN network [1], which directly affects the user's online experience. The common type of ARP-attack is MITM-Attack (Man-in-the-Middle Attack) with two-types, disguising a host or a gateway. Common means of ARP-attack prevention is by deploying network-security equipment or binding IP-MAC in LAN manually[10]. This paper studies an automatic ARP-attack prevention technology for multi-object, based on the domain-control technology and batch-processing technology. Compared with the common ARP-attack-prevention measure, this study has advantages of low-cost, wide-application, and maintenance-free. By experimentally researching, this paper demonstrates the research correctness and technical feasibility. This research result, multi-object-oriented automatic defense technology for ARP-attacking, can apply to enterprise network.

Logic Locking is a well-accepted protection technique to enable trust in the outsourced design and fabrication processes of integrated circuits (ICs) where the original design is modified by incorporating additional key gates in the netlist, resulting in a key-dependent functional circuit. The original functionality of the chip is recovered once it is programmed with the secret key, otherwise, it produces incorrect results for some input patterns. Over the past decade, different attacks have been proposed to break logic locking, simultaneously motivating researchers to develop more secure countermeasures. In this paper, we propose a novel stuck-at fault-based differential fault analysis (DFA) attack, which can be used to break logic locking that relies on a stored secret key. This proposed attack is based on self-referencing, where the secret key is determined by injecting faults in the key lines and comparing the response with its fault-free counterpart. A commercial ATPG tool can be used to generate test patterns that detect these faults, which will be used in DFA to determine the secret key. One test pattern is sufficient to determine one key bit, which results in at most \textbackslashtextbarK\textbackslashtextbar test patterns to determine the entire secret key of size \textbackslashtextbarK\textbackslashtextbar. The proposed attack is generic and can be extended to break any logic locked circuits.

With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.

Wireless Network-on-Chip (WNoC) broadly adopts single channel for low overhead data transmission. Sharing of the channel among multiple wireless interfaces (WIs) is controlled by a channel access mechanism (CAM). Such CAM can be malfunctioned by a Hardware Trojan (HT) in a malicious WI or a rogue third party intellectual property (IP) core present on the same System-on-Chip (SoC). This may result in denial-of-service (DoS) or spoofing in WNoC leading to starvation of healthy WIs and under-utilization of wireless channel. Our work demonstrates possible threat model on CAM and proposes low overhead decentralized countermeasures for both DoS and spoofing attacks in WNoC.

Multi-factor user authentication (MFUA) becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing MFUAs require multiple interactions between users and different authentication components when sensing the multiple factors, leading to extra overhead and bad use experiences. In this paper, we propose a secure and user-friendly MFUA system, namely BioDraw, which utilizes four categories of biometrics (impedance, geometry, composition, and behavior) of human hand plus the pattern-based password to identify and authenticate users. A user only needs to draw a pattern on a RFID tag array, while four biometrics can be simultaneously collected. Particularly, we design a gradient-based pattern recognition algorithm for pattern recognition and then a CNN-LSTM-based classifier for user recognition. Furthermore, to guarantee the systemic security, we propose a novel anti-spoofing scheme, called Binary ALOHA, which utilizes the inhabit randomness of RFID systems. We perform extensive experiments over 21 volunteers. The experiment result demonstrates that BioDraw can achieve a high authentication accuracy (with a false reject rate less than 2%) and is effective in defending against various attacks.

E-learning enables the transfer of skills, knowledge, and education to a large number of recipients. The E-Learning platform has the tendency to provide face-to-face learning through a learning management system (LMS) and facilitated an improvement in traditional educational methods. The LMS saves organization time, money and easy administration. LMS also saves user time to move across the learning place by providing a web-based environment. However, a few students could be willing to exploit such a system's weakness in a bid to cheat if the conventional authentication methods are employed. In this scenario user authentication and surveillance of end user is more challenging. A system with the simultaneous authentication is put forth through multifactor adaptive authentication methods. The proposed system provides an efficient, low cost and human intervention adaptive for e-learning environment authentication and monitoring system.