Search results for: cyber vetting

Authors: Tania Mitra

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Digital Activism or Cyber Activism uses digital media as a means to disseminate information and mobilize masses towards a specific goal. When digital activism was first born in the early 1990s, it was primarily used by groups of organized political activists. However, with the advent of social media, online activism has filtered down to the individual- one who does not necessarily belong to or identify with an agenda, group, or political party. A large part of digital activism today stems from the individual’s notion of what is right and wrong. This gives rise to a discourse around descriptive ethics and the implications of the independent digital activist. Although digital activism has paved the way for and bolstered support for causes like the MeToo Movement and Black Lives Matter, the lack of a unified, organized body has led to counterintuitive progressions and suspicions regarding the movements. The paper introduces the ideas of 'clout' culture, click baits, and clicktivism (the phenomenon where activism is reduced to a blind following of the online trends), to discuss the impacts of exclusive digital activism. By using Jeremy Bentham's utilitarian approach to ethics, that places emphasis on the best possible outcome for a society, the paper will show how individual online activism reaching for a larger, more common end can sometimes lead to an undermining of that end, not only in the online space but also how it manifests in the real world.

Keywords: digital activism, ethics, independent digital activist, utilitarianism

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Suglo Tohari Luri

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Data analysis is an important aspect of business performance. With the application of artificial intelligence within databases, selecting a suitable database engine for an application design is also very crucial for business data analysis. The application of business intelligence (BI) software into some relational databases such as Neo4j has proved highly effective in terms of customer data analysis. Yet what remains of great concern is the fact that not all business organizations have the neo4j business intelligence software applications to implement for customer data analysis. Further, those with the BI software lack personnel with the requisite expertise to use it effectively with the neo4j database. The purpose of this research is to demonstrate how the Neo4j program code alone can be applied for the analysis of e-commerce website customer visits. As the neo4j database engine is optimized for handling and managing data relationships with the capability of building high performance and scalable systems to handle connected data nodes, it will ensure that business owners who advertise their products at websites using neo4j as a database are able to determine the number of visitors so as to know which products are visited at routine intervals for the necessary decision making. It will also help in knowing the best customer segments in relation to specific goods so as to place more emphasis on their advertisement on the said websites.

Keywords: data, engine, intelligence, customer, neo4j, database

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Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

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Authors: Ashish Ashish

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In the last few decades, the discrete chaos of difference equations has gained a massive attention of academicians and scholars due to its tremendous applications in each and every branch of science, such as cryptography, traffic control models, secure communications, weather forecasting, and engineering. In this article, a generalized logistic discrete map is established and discrete chaos is reported through period doubling bifurcation, period three orbit and Lyapunov exponent. It is interesting to see that the generalized logistic map exhibits superior chaos due to the presence of an extra degree of freedom of an ordered parameter. The period doubling bifurcation and Lyapunov exponent are demonstrated for some particular values of parameter and the discrete chaos is determined in the sense of Devaney's definition of chaos theoretically as well as numerically. Moreover, the study discusses an extended chaos based image encryption and decryption scheme in cryptography using this novel system. Surprisingly, a larger key space for coding and more sensitive dependence on initial conditions are examined for encryption and decryption of text messages, images and videos which secure the system strongly from external cyber attacks, coding attacks, statistic attacks and differential attacks.

Keywords: chaos, period-doubling, logistic map, Lyapunov exponent, image encryption

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Authors: Yuya Tanigami, Naoaki Yamanaka, Satoru Okamoto

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Economic losses due to delays in traffic congestion regarding urban transportation networks have become a more serious social problem as traffic volume increases. Platooning has recently been attracting attention from many researchers to alleviate traffic jams, especially on the highway. On highways, platooning can have positive effects, such as reducing inter-vehicular distance and reducing air resistance. However, the impacts of platooning on urban roads have not been addressed in detail since traffic lights may break the platoons. In this study, we propose a platooning method using L2 norm and cosine similarity to form a platoon with highly similar routes. Also, we investigate the sorting method within a platoon according to each vehicle’s straightness. Our proposed sorting platoon method, which uses two lanes, eliminates Head of Line Blocking at the intersection and improves throughput at intersections. This paper proposes a cyber-physical system (CPS) approach to collaborative urban platoon control. We conduct simulations using the traffic simulator SUMO and the road network, which imitates Manhattan Island. Results from the SUMO confirmed that our method shortens the average travel time by 10-20%. This paper shows the validity of forming a platoon based on destination vectors and sorting vehicles within a platoon.

Keywords: CPS, platooning, connected car, vector correlation

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Authors: S. Bader, N. Essoukri Ben Amara

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With the technological development and rise of virtual worlds, these spaces are becoming more and more attractive for cybercriminals, hidden behind avatars and fictitious identities. Since access to these spaces is not restricted or controlled, some impostors take advantage of gaining unauthorized access and practicing cyber criminality. This paper proposes an identity management approach for securing access to virtual worlds. The major purpose of the suggested solution is to install a strong security mechanism to protect virtual identities represented by avatars. Thus, only legitimate users, through their corresponding avatars, are allowed to access the platform resources. Access is controlled by integrating an authentication process based on biometrics. In the request process for registration, a user fingerprint is enrolled and then encrypted into a watermark utilizing a cancelable and non-invertible algorithm for its protection. After a user personalizes their representative character, the biometric mark is embedded into the avatar through a watermarking procedure. The authenticity of the avatar identity is verified when it requests authorization for access. We have evaluated the proposed approach on a dataset of avatars from various virtual worlds, and we have registered promising performance results in terms of authentication accuracy, acceptation and rejection rates.

Keywords: identity management, security, biometrics authentication and authorization, avatar, virtual world

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Authors: C. C. Liang

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MSN used to be the most popular application for communicating among social networks, but Facebook chat is now the most popular. Facebook and MSN have similar characteristics, including usefulness, ease-of-use, and a similar function, which is the exchanging of information with friends. Facebook outperforms MSN in both of these areas. However, the adoption of Facebook and abandonment of MSN have occurred for other reasons. Functions can be improved, but users’ willingness to use does not just depend on functionality. Flow status has been established to be crucial to users’ adoption of cyber applications and to affects users’ adoption of software applications. If users experience flow in using software application, they will enjoy using it frequently, and even change their preferred application from an old to this new one. However, no investigation has examined choice behavior related to switching from Facebook to MSN based on a consideration of flow experiences and functions. This investigation discusses the flow experiences and functions of social-networking applications. Flow experience is found to affect perceived ease of use and perceived usefulness; perceived ease of use influences information ex-change with friends, and perceived usefulness; information exchange influences perceived usefulness, but information exchange has no effect on flow experience.

Keywords: consumer behavior, social media, technology acceptance model, flow experience

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Authors: Jordan Daud, Satriya Wibawa, Wahyu Wardhana

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The US Global War on Terror has had effect on Southeast Asia as Second Front of Global War on Terror. Since 2001, ASEAN had adopted legal framework to counter the terrorist threat through numerous approach which accommodate various counterterrorism policy of the ten member states. ASEAN have also enhanced multilateral cooperation with US and its allies in Asia Pacific region in addressing terrorist threat, terrorist funding, cyber terrorism and other forms of terrorism. This cooperation is essential to maintain international security and stability and also assure economic development. This work focuses on the US-ASEAN counterterrorism cooperation due to they identified terrorism as a mutual enemy that posed to human security, infrastructure security, and national security. Having in mind that international terrorism usually connected with Muslim community, this paper will also elaborate the concept of Jihad and Islam revivalism in politics to avoid negative image of Islam and Muslim. This paper argues that as region with large Muslim community, Southeast Asia still need to tighten counter terrorism cooperation and also lessening Muslim stereotypes with terrorism through educating public understanding and inter-faith and intra-faith dialogue to create a better world.

Keywords: ASEAN, U.S., counter terrorism, Muslim stereotypes

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Authors: Yvonne Dedzo

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Social media has become a powerful tool in bridging the distance between individuals regardless of their location. It has become a convenient platform for public discussion and, consequently, generated the phenomenon of citizen journalists who have become both proactive and reactive participants in the dissemination of news, information and other epochal and historical events. This phenomenon has fueled the growth of niche authorities who deliver exceptional democratically consequential information online. This study, therefore, investigates how some selected niche authorities maintain their status on social media. Using the selective processes theory, the study further interrogates the information shared by niche authorities and further analyses the extent to which a public interest-altruistic motive or personal interest-self-serving motive drives their agenda of new sharing and usage. Through cyber-ethnography and, qualitative content analysis and semi-structured interviews, data was gathered and analysed from the posts of two purposely selected niche authorities on Facebook and Twitter. The findings indicate that niche authorities maintain their status by being consistent, prompt, informative, resourceful and interactive in their postings on the social media platform. The study also discovered that even though niche authorities are motivated by both public interest-altruism and interest-self-serving, the latter had a higher of motivation than the former.

Keywords: social medida, citizen journalist, niche authorities, selective processes theory

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Authors: Matthew J. Flynn

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In early 2022, a thirty-mile column of Russian armor and assault vehicles sat poised to move south on the road to Kiev. That force has withdrawn as the Russians concentrate on attacking eastern Ukraine. Vladimir Putin’s armies appear content to destroy cities in an effort to attrit the Ukrainian will to continue fighting. That pivot signifies the acceptance of the ascendancy of the defense that now dictates any battlefield world-wide. To defeat what military theorist Carl von Clausewitz labeled “the stronger form of war” with a successful offensive requires an exercise in future war. In the past, the ascendancy of the defense has been overcome by a number of things including the application of superior leadership, better technology, organizational adaptation, and surpassing environmental limitations. A look at how each of these factors came to impact battle can tell us a great deal about what Ukraine means to tomorrow’s fight, and where the focus should lie to win the next war. Civilians presently secure the defensive ascendancy impacting warfare by dominating the shifts from domain to domain thanks to controlling access to cyberspace. That mandate will be tested and eventually falter. This paper tests the desirability of that proposition, as well as hoping for something more from humanity other than repeated and frequent wars making future war look much like past wars. As nations struggle to control cyberspace, a referendum on war as part of the human condition comes into focus.

Keywords: cyber, domains, future war, Putin, Ukraine

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Authors: Shila Khayambashi

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For the past few decades, Western researchers studied different youth issues, such as bullying, peer-aggression, depression, self-harm, and suicide, in a formulated and standardized manner. These researchers have grounded their studies upon a series of introduced characteristics and traits, which pragmatically defined the action of the individuals involved in these activities (Olweus, 1994). The phenomena of bullying and peer-aggression have touched the lives of many immigrant youths, as well. However, in the case of these immigrant young adults, the Police investigated, and later dismissed, the victims’ involvement in drugs and gangs’ activities, instead of questioning the possibility of the peer-aggression. This paper argues that neither government officials nor school personnel has ever investigated any cyber-documentation which would clarify these youth’s untimely deaths or search for any indication of peer-aggression at school. Through my ongoing research, I will problematize the Eurocentric definition of bullying and its limitations. I question the assumed universality of these definitions’ characteristics and their lack of minority representation. This research questions explicitly the positionality of the displaced youth within the promised multiculturality of Canada. I will ask: Does one size fit all, considering the bio-psycho-socio-economic differences between the Eastern and the Western worlds? More importantly, how does the epidemy of the communicative devices, like smartphones, and communicative apps, like Twitter and Snapchats, facilitate or hinder peer-aggression for the displaced youths?

Keywords: Bullying , Immigrant youths, Peer aggression, Minority population

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Authors: Larry Lai, Evans Leung

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Falun gong is a self-proclaimed religious group that has been under crackdown by Beijing for more than two decades. Diaosi, on the other hand, is an emerging community with members loosely connected on the internet through different online social platforms, centering around the sharing of different hobbies and interests. Diaosi community has been transformed from a potential threat to the Chinese authority for different causes to a pro-government force. This paper seeks to explain the different strategies adopted by the People's Republic of China (PRC) regime in handling these two potential threatening communities. Both communities share some obvious similarities: (1) both have massive nation-wide participation; (2) both have attempted to challenge the PRC's authority through contentious means; (3) both have high level of mobility, online or offline; and (4) both have at first been unnoticed until the threat against the PRC have taken form. But the strategies the PRC endorsed against the communities were, in many ways, different. The question is: if the strategy against Falun Gong has been an effective one, why used other strategies against Diaosi? The authors argue that the main reason for using different strategies lies in the differences between the two communities in terms of (i) the nature of the groups, and (ii) the group dynamics. Lastly, based on this analysis, the authors attempt to explore the possible strategies that the PRC would adopt against the Hong Kong cyber-world political community in light of the latest national security law in Hong Kong.

Keywords: contentious politics, Diaosi, Falun Gong, Hong Kong, People's Republic of China

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Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

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One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

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Authors: Ghodhbani Salah, Jemili Farah

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With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

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Authors: Johannes Atug, Andreas Radke, Mitchell Tseng, Gunther Reinhart

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In modern supply chains, large numbers of SKU (Stock-Keeping-Unit) need to be timely managed, and any delays in noticing disruptions of items often limit the ability to defer the impact on customer order fulfillment. However, in supply chains of IoT-connected enterprises, the ERP (Enterprise-Resource-Planning), the MES (Manufacturing-Execution-System) and the SCADA (Supervisory-Control-and-Data-Acquisition) systems generate large amounts of data, which generally glean much earlier notice of deviations in the business process steps. That is, analyzing these streams of data with process mining techniques allows the monitoring of the supply chain business processes and thus identification of items that deviate from the standard order fulfillment process. In this paper, a framework to enable event-based SCM (Supply-Chain-Management) processes including an overview of core enabling technologies are presented, which is based on the RAMI (Reference-Architecture-Model for Industrie 4.0) architecture. The application of this framework in the industry is presented, and implications for SCM in industry 4.0 and further research are outlined.

Keywords: cyber-physical production systems, event-based monitoring, supply chain management, RAMI (Reference-Architecture-Model for Industrie 4.0)

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Authors: Sri Lakshmi Kanniah, Mohd Naz’ri Mahrin

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More and more businesses and services are depending on software to run their daily operations and business services. At the same time, cyber-attacks are becoming more covert and sophisticated, posing threats to software. Vulnerabilities exist in the software due to the lack of security practices during the phases of software development. Implementation of secure software development practices can improve the resistance to attacks. Many methods, models and standards for secure software development have been developed. However, despite the efforts, they still come up against difficulties in their deployment and the processes are not institutionalized. There is a set of factors that influence the successful deployment of secure software development processes. In this study, the methodology and results from a systematic literature review of factors influencing the implementation of secure software development practices is described. A total of 44 primary studies were analysed as a result of the systematic review. As a result of the study, a list of twenty factors has been identified. Some of factors that affect implementation of secure software development practices are: Involvement of the security expert, integration between security and development team, developer’s skill and expertise, development time and communication between stakeholders. The factors were further classified into four categories which are institutional context, people and action, project content and system development process. The results obtained show that it is important to take into account organizational, technical and people issues in order to implement secure software development initiatives.

Keywords: secure software development, software development, software security, systematic literature review

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Authors: Desislav Andreev, Veselin Stanev

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Linux operating system continues to gain popularity with every passed year. This is due to its open-source license and a great number of distributions, covering users’ needs. At first glance it seems that Linux can be integrated in every type of systems – it is already present in personal computers, smartphones and even in some embedded systems like Raspberry Pi. However, Linux still does not meet the performance and security requirements to run effectively on a real-time system. Real-time systems are very time-restricted – their processes have to execute and finish at strict time intervals. The Completely Fair Scheduler present in Linux does not have such scheduling capabilities and it is not able to ensure that critical-time processes will execute on time. One of the ways to solve this problem is implementing an Adaptive Partition Scheduler solution similar to that present in QNX Neutrino operating system. This type of scheduling divides the CPU in multiple adaptive partitions where each partition holds a percentage of CPU usage called budget, which allows optimal usage of the CPU resources and also provides protection against cyber attacks such as Denial of Service. This approach will also benefit systems, where functional safety is highly demanded, such as the instrumental clusters in the Automotive industry. The purpose of this paper is to present a concept of Adaptive Partition Scheduler designed for Linux-based operating systems.

Keywords: adaptive partitions, Linux kernel modules, real-time systems, scheduling

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Christiana Ebobo

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Cybercrime consists of numerous crimes that are perpetrated on the internet on daily basis. The forms include but not limited to Identity theft, Pretentious dating, Desktop counterfeiting, Internet chat room, Cyber harassment, Fraudulent electronic mails, Automated Teller Machine Spoofing, Pornography, Piracy, Hacking, Credit card frauds, Phishing and Spamming. The general term used among the youths for this type of crime in Nigeria is ‘Yahoo Yahoo’. Cybercrime is on the increase among the youths at all levels as such this study aims at examining the impact of cybercrime on youth development in Nigeria. The study examines the impact of cybercrime on youths’ academic performance, integrity, employment and religious practices. The study is a survey which made use of questionnaire and focus group discussion among 150 randomly selected youths in Gwagwalada LCDA, Federal Capital Territory, Nigeria. The study adopts the systems theory as its theoretical framework. The study also adopts the simple frequency table and percentage for its data analysis. The study reveals that cybercrime has eaten deep into the minds of some youths and some of them are practicing diabolic means to succeed in it. It is also reveals that majority (68%) of the respondents believe that cybercrime impacts negatively on youths’ academic performance in Nigeria. The major recommendation of this study is that cybercrime offenders should be treated like armed robbers in order to discourage other youths from getting involved in it.

Keywords: armed robber, cybercrime, integrity, youth

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: P. Saee, Baiju Gopal

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YouTube has seen a surge in the recent years in the number of creators opening up about their mental illness on the video-sharing platform. In documenting their mental health, YouTubers perform an identity of their mental illness in the online world. Identity performance is a theory under identity research that has been readily applied to illness narratives and internet studies. Furthermore, in India, suffering from mental illnesses is regarded with stigma, making the act of taking mental health from a personal to a public space on YouTube a phenomenon worth exploring. Thus, the aim of this paper is to analyse the mental illness narratives of Indian YouTubers for understanding its performance in the virtual world. For this purpose, thematic narrative analysis on the interviews of four Indian YouTubers was conducted. This data was synthesized with analysis of the videos the YouTubers had uploaded on their channel sharing about their mental illness. The narratives of the participants shed light on two significant presentations that they engage in: (a) the identity of a survivor/fighter and (b) the identity of a silent sufferer. Further, the participants used metaphors to describe their illness, thereby co-constructing a corresponding identity based on their particular metaphors. Lastly, the process of bringing mental illness from back stage to front stage on YouTube involves a shift in the audience, from being rejecting and invalidating in real life to being supportive and encouraging in the virtual space. Limitations and implications for future research were outlined.

Keywords: cyber-psychology, internet, media, mental health, mental illness, technology

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Authors: Mohamamd Ayish

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This study addresses media diplomacy as an integral component of public diplomacy which emerged in the United States in the post-World War II era and found applications in other countries around the world. The study seeks to evolve a conceptual framework for understanding the practice of public diplomacy through social networks, often referred to as social engagement diplomacy. This form of diplomacy is considered far more ahead of the other two forms associated with both government controlled and independent media. The cases of the Voice of America Arabic Service and the 1977 CBS interviews with the late Egyptian President Anwar Sadat and Israeli Prime Minister Menachem Begin are cited in this study as reflecting the two traditional models. The new social engagement model sees public diplomacy as an act of communication that seeks to effect changes in target audiences through a process of persuasion shaped by discourse orientations and technological features. The proposed conceptual framework for social, diplomatic engagement draws on an open communication environment, an empowered audience, an interactive and symmetrical process of communication, multimedia-based flows of information, direct and credible feedback, distortion and high risk. The writer believes this study would be helpful in providing appropriate knowledge pertaining to our understanding of social diplomacy and furnishing concrete insights into how diplomats could harness virtual space to maximize their goals in the global environment.

Keywords: diplomacy, engagement, social, globalization

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Authors: Sai Rajeev Reddy, Ishita Roy, M. Anji Reddy

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The paper describes the importance of preventive measures and immediate Emergency logistics during accidents and unfortunate Disasters for the Hyderabad Metro Rails in their various stages of construction. This is the need of the modern generation where accidents, explosions, attacks and sudden crisis are frequent casualties which take huge tolls of life in the present world. The paper utilizes the workflow and application of Geographical information System (GIS) to provide information about problems and crisis structures for efficient Metro Transportation in the city. The study analyzes the difficulties and problems which cause accidents during operation and maintenance stages of the Metro Rail. The paper focuses upon the intermediate and firsthand information of Crisis with the help of GIS technology to share Disaster data for effective measures by the Cyber Police stations, Emergency Responders, Hospitals and First Aid Centre to act immediately and save lives. The results and conclusions have nevertheless proved very informative and useful for the safety board authorities of the Hyderabad Metro Rail. The operation and Maintenance are integral stages in the development of any Multipurpose transportation Projects and are usually prone to various Disasters and tragedies. Hence, the GIS technologies help in distribution of information among the masses with the web Technologies and advanced software developed to prevent and manage crisis widely and in a cost-benefits manner.

Keywords: Geographical Information System, emergency assessment, accident zones, surveillance

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Authors: Aulia Dwi Nastiti

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A number of studies and reports on terrorism have continuously addressed the role of internet and online activism to support terrorist and extremist groups. In particular, they stress on social media’s usage that generally serves for the terrorist’s propaganda as well as justification of their causes. While those analyses are important to understand how social media is a vital tool for global network terrorism, they are inadequate to explain the online communication patterns that enable terrorism acts. Beyond apparent online narratives, there is a deep cyber sphere where the very vein of terrorism movement lies. That is a hidden space in the internet called ‘darknet’. Recent investigations, particularly in Middle Eastern context, have shed some lights that this invisible space in the internet is fundamental to maintain the terrorist activities. Despite that, limited number of research examines darknet within the issue of terrorist movements in Indonesian context—where the country is considered quite a hotbed for extremist groups. Therefore, this paper attempts to provide an insight of darknet operation in Indonesian cases. By exploring how the darknet is used by the Indonesian-based extremist groups, this paper maps out communication patterns of terrorist groups on the internet which appear as an intermingled network. It shows the usage of internet is differentiated in different level of anonymity for distinctive purposes. This paper further argues that the emerging terrorist communication network calls for a more comprehensive counterterrorism strategy on the Internet.

Keywords: communication pattern, counterterrorism, darknet, extremist groups, terrorism

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: Iram Malik, Mahrukh Shaukat, Abeer Malik, Hafiz Mushtaq Ahmad

Abstract:

There has been a lot of research on cyberbullying but there is limited research on the topic of accidental cyberbullying on social media with a special focus on healthcare industry. This study emphasizes to uncover the factors that contribute to accidental cyberbullying on social media and how it affects individuals, professionals’ and organizations in health care sector. Nowadays social media is becoming a necessary part of our daily life; there is a need to look into how it is shaping our social life and behaviors displayed online. Instances of cyber bullying can have long-term repercussions due to over-sharing of information. The study used simple random sampling and the instrument of data collection was survey. A sample size of 250 healthcare professionals was chosen from the twin cities of Rawalpindi and Islamabad, Pakistan to examine the relationship between their attitude towards internet use, psychological distress, verbal aggression, envy, frustration, self-compassion, personality traits and accidental cyberbullying on social media. The results of the study have been encouraging. The findings show that psychological distress, aggression, envy, frustration and personality traits had direct effect on accidental cyberbullying whereas compassion, altruism lessened the effect of accidental cyberbullying behavior. It is our intent that the findings of this study could help raise awareness regarding fair use of social media, help policy makers in developing appropriate policies for avoiding cyberbullying in future.

Keywords: accidental cyberbullying, aggression, cyberbullying, frustration, social media

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Authors: Amanda Andrea Beatrix Brita, Dominic J. Buenaventura, Charmaine A. Seromines, Antonne Noel V. Chua, Ma. Luisa G. Badilles

Abstract:

Online ranting, or the act of forcefully expressing your opinion towards other people over the Internet, has been an ongoing issue ever since the creation of the Internet. Due to the COVID-19 pandemic, the rise of active users has been observed to have increased during this time, thus increasing the number of people being engaged in online activities, which consequently affects the number of individuals who participate in online ranting. However, with all that is observed, what is usually not a topic being discussed about this event are the motivations that lead people to do online ranting. The study was undertaken to discover the level of aggression, Satisfaction, and online engagement in their environment. We utilized 2 researcher-made questionnaires that were validated by three experts, namely for the Satisfaction and engagement levels. We adapted Cyber-Aggression Typology Questionnaire (CATQ) for the level of aggression. Quota sampling was used in determining the respondents. A quota of 250 students was recommended. The results showed that the students in every institution have a deficient level of aggression, and the majority of the institution has a shallow level of Satisfaction when it comes to online ranting. Similar findings are presented in the level of engagement; 2 out of 3 of the institution students have shallow engagement in the general settings that expound students' engagement. Although the result shows deficient levels, there are still instances and possibilities that students may still engage in an online rant regarding various circumstances when triggered. Researchers assumed that there are restrictions on movements likely to show opinions online because of consequences.

Keywords: aggression, satisfaction, online rant engagement, online rant

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

Abstract:

Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

Procedia PDF Downloads 48