The March 2021 issue in your hands carries a series of interesting articles about education and institution. The institution in focus is the famous Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya (SCSVMV in short), a Deemed University located in Enathur, Kanchipuram. The greatness of such institutions stay in the background most often in the din that surrounds us; mainly due to the postcolonial conditioning that has remained deep rooted in our education system. The New Education Policy 2020 brings in fresh air and encourages experiments. Around the same time, Covid-19 pushed the world to embrace ICT in full measure. In a recent conclave I participated to talk about the role of SCSVMV in the context of NEP 2020, the idea of focusing an issue highlighting the methods, practices, and successes, was born. One gets news about institutions that are in the urban areas that are funded by the Government in good measure; but one often does not get to see India in its entirety. In this March 2021 issue, readers will get a comprehensive and coherent picture of experiences of a 25 year old institution founded for social upliftment of society in a rural area. Surprisingly, it fits well into our track on Education and Management of ICT. I hope the readers find the articles interesting and thought provoking. We are moving the journal in to era of open exploration of complex usage of the Information and Communication Technology; the reliability and resilience of the ICT based systems have become important in the changed scenario, i.e. post Covid. When intense usage comes from unexpected quarters such as Ayurveda, the ICT designer faces the problem of complexity of the system coupled with the simplicity expected by the user, which one can call Designers’ Dilemma. Well, what we have is just the beginning and perhaps the tip of the iceberg. In my usual style, it is my pleasure to introduce briefly the ten articles of this issue.

We have spent five decades and four decades respectively, in the field of higher education. The information chronicled in this paper represents our collective understanding and wisdom acquired therefrom. We describe ‘the desirable way’ for generations to come, after witnessing changes for the better and for the worse. Our emphasis is on holistic view, be it learning, research, technology, commerce, or environment; when we say holistic, we mean in space and time. There is a bit of philosophic underpinning when we make observations of consequence, but we do so with serious scientific basis. In our discourse, we are agnostic to belief (hypothesis) based dialectic logic and reasoning system and axiomatic (and hypothesis) based proving system that follows methodology assiduously.

Being a Teacher is a spiritual experience. One not only experiences happiness and satisfaction, but also imparts them to students who are listening. In this article, the author tries to capture the splendor of the classroom experience that practices the ideal form of teaching–learning process. The basis is the prescriptive methods given to this world by Adi Shankaracharya.

Palm Leaf Manuscripts hold the knowledge of India dating back to several centuries. Therefore, Palm Leaf manuscripts are valued as Saaswathaiswaryam, implying eternal (Saaswath [ Sanskrit word meaning Eternal, Forever ]) wealth (Aiswaryam [ Sanskrit word meaning wealth ]). But the Palm Leaf manuscripts are in different conditions- brittle, moth-eaten, edge-damaged, portions broken, etc. Besides, the leaves themselves are in bundles, that too out of order. The challenge for our generation is to design a methodology to capture the contents, recreate the missing portion, and figure out a way to right-order the manuscripts. Alongside, use modern technology for storage and retrieval, with emphasis on relevance to the requirement posed in the search query.

The number of actions at a user’s disposal in the digital arena is on the rise; the number of technologies available to track users’ activities in an organisation are on the rise; the volume of information logged using such technologies are on the increase. And yet, the number of security incidents recorded per unit time is also on the rise. While there is greater awareness in the community and a plethora of passionate analysts to triage and analyse incidents, there seems to develop an ever-widening gap between the number of such analysts to the growth of incident volumes, particularly so in the last decade. In fact, operational response remains largely in the realms of manual remediation. If one were to take an objective view of the sequence of actions that transpire between when a detection is observed to the time that it is remediated, there is a broad spectrum between completely objective tasks (that can be automated) to purely subjective evaluation tasks (largely manual). This can be regarded as the automation scale. During a response scenario, it is conceivable that an analyst would conduct a series of tasks, some of which are common across all incidents, some of it constrained to the technology pertaining to the alert that instigated the response, and the rest involving manual evaluation and context establishment. If one were to magnify that period of observation for a granular view of the tasks conducted over that time, we hypothesise that one can generate a calibration scale to uniquely identify tasks that warrant automation. In this work, we develop a cognitive model called SAI reinforced with a machine learning framework to organically escalate tasks into the operational automation. While the cognitive model discerns tasks from the sample space of {objective, technology-dependent, subjective}, the organic escalation is achieved through a reward-penalty model from amongst the possible response spectrum evaluated in a finite n-tuple context. Finding: In the absence of external business factors, if the number of parameters influencing an alert is limited to C and the total number of alerts in a given period is limited to K, then it is sufficient if, for any task involved in that alert remediation, the task receives at least 10.C.K endorsements during the same period, in order to escalate it into automation. We demonstrate how the framework can seamlessly accommodate false positives that are opportunistic to any operational environment while providing savings in expenditure by transforming as a measuring scale for alarms in an organisation.

Rapid advancements in technology coupled with drastic reduction in cost of storage have resulted in tremendous increase in the volumes of stored data. As a consequence, analysts find it hard to cope with the rates of data arrival and the volume of data, despite the availability of many automated tools. In a digital investigation context where it is necessary to obtain information that led to a security breach and corroborate them is the contemporary challenge. Traditional techniques that rely on keyword based search fall short of interpreting data relationships and causality that is inherent to the artifacts, present across one or more sources of information. The problem of handling very large volumes of data, and discovering the associations among the data, emerges as an important contemporary challenge. The work reported in this paper is based on the use of metadata associations and eliciting the inherent relationships. We study the metadata associations methodology and introduce the algorithms to group artifacts. We establish that grouping artifacts based on metadata can provide a volume reduction of at least \( {\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {2M}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${2M}$}} \), even on a single source, where M is the largest number of metadata associated with an artifact in that source. The value of M is independent of inherently available metadata on any given source. As one understands the underlying data better, one can further refine the value of M iteratively thereby enhancing the volume reduction capabilities. We also establish that such reduction in volume is independent of the distribution of metadata associations across artifacts in any given source. We systematically develop the algorithms necessary to group artifacts on an arbitrary collection of sources and study the complexity.

Analysis of contemporary web browser sessions for forensic purposes has one major challenge - that of distinguishing Internet events and sessions across multiple tabs. While some information is contained inside the log files, identifying the coherency and concurrency is necessary to generate \"specificity of attribution\". In this work, we focus on isolating multiple simultaneous browser sessions using network and browser-related metadata on browsers that deploy a separate process for each browser session, which we term process-separated browser implementation. In a previous work, it was shown that network artifacts can be associated with browser artifacts to relate network stream with browser sessions on multi-threaded browser implementations. However, in process-separated browser implementations, where each session has its own network stream, the ability to track all network streams via a single process is not available. Therefore, there is a need to associate each network stream with its corresponding process for reconstruction. In this paper, we propose an algorithm to reconstruct multiple simultaneous browser sessions on browser applications that use a separate process for each browser session. We achieve this by developing a representation for the information associated with a browser session. Further, we define two relationships, viz., 'stream coherency' and 'session concurrency' based on the associations discovered among the network and browser artifacts. Finally, we develop an algorithm called \"Samhita\" to identify number of simultaneous browser sessions that are deployed and associate them with the respective processes and network streams. We take the reader through specially designed experiments to elicit browser-session intelligence and the process to separate out the tabbed sessions using the timing information present in the browser context and session context.

Internet browsers support multiple browser tabs, each browser tab capable of initiating and maintaining a separate web session, accessing multiple uniform resource identifiers (URIs) simultaneously. As a consequence, network traffic generated as part of a web request becomes indistinguishable across tabbed sessions. However, it is possible to find the specificity of attribution in the session-related context information recorded as metadata in log files (in servers and clients) and as network traffic related logs in routers and firewalls, along with their metadata. The forensic questions of "who," "what" and "how" are easily answered using the metadata-based approach presented in this chapter. The same questions can help systems administrators decide on monitoring and prevention strategies. Metadata, by definition, records context information related to a session; such metadata recordings transcend sources. This chapter presents an algorithm for reconstructing multiple simultaneous browser sessions on browser applications with multi-threaded implementations. Two relationships, coherency and concurrency, are identified based on metadata associations across artifacts from browser history logs and network packets recorded during active browser sessions. These relationships are used to develop the algorithm that identifies the number of simultaneous browser sessions that are deployed and then reconstructs the sessions. Specially-designed experiments that leverage timing information alongside the browser and session contexts are used to demonstrate the processes for eliciting intelligence and separating and reconstructing tabbed browser sessions.

Mobile Agent coordination is important in applications, which are both time and space bound, such as disaster recovery or demining. In such environments, each agent is required to be mobile to cover a region around it. In order to cover the entire area, all agents should collectively ensure full coverage of the area of interest, for which infrastructure for coordination across mobile agents may not be readily available. Knowing when to communicate and what to communicate becomes therefore becomes a key learning exercise. In such a scenario, it is interesting to study the evolution of a communication hierarchy in order to accomplish a multi-agent coordination task such as area coverage. In this paper, we propose a coordination hierarchy for a survivor rescue task and overlay a learning framework to teach a team of agents what to communicate and when to communicate while recovering the survivors efficiently. We then study the evolution of a communication hierarchy derived from the agent learning.

At present Internet has emerged as a country's predominant and viable data communication infrastructure. The Autonomous System (AS) resources which are building blocks of the Internet are AS numbers, IPv4 and IPv6 Prefixes. AS number growth is one of Internet infrastructure development indicators. Hence understanding on long term trend and stochastic variation behaviour are essential to detect significant events during the growth. In this work, time series based approximation is considered for mathematical modelling and forecast the yearly AS growth. The AS data of five countries namely India, China, Japan, South Korea and Taiwan are extracted from APNIC archive. ARIMA models with different Auto Regressive and Moving Average parameters are identified for forecasting. Model validation, parameter estimation, point forecast and prediction intervals with 95 % confidence levels for the five countries are reported in the paper. The significant level change in variations, positive growth percentage in Inter Annual Absolute Variations (IAAV) and higher percentage of advertised ASes when compared to other countries indicate India's fast growth and wider global reachability of Internet infrastructure from 2007 onwards. The correlation between IAAV change point and GDP growth period indicates that service sector industry growth is the driving force behind significant yearly changes.

When a security incident is reported, identifying the point-of-breach is critical to figure out what an attacker might do next. Besides, the information related to the resources and levels of privilege acquired as a result of the breach, is essential to re-create the process. Such information is also crucial to planning a defense in real-time. To this end, we need to determine the following parameters associated with the system under attack, viz., a breach probability determined from the success of past cyber-attacks, the attack matrix which is dependent on the system design that decides the types of attacks and the associated modalities that the system may be susceptible to under standard conditions, and the access matrix which is determined by the access privileges that get granted when an attack succeeds. In this paper, we introduce the breach-point detection problem and present a model based on Bayes' conditional probability to identify the point-of-breach on a system impacted by a cyber-attack. We evaluate the probability associated with likely points-of-breach in the rest of the system based on the knowledge of its design and estimate the posterior probabilities associated with the points so identified based on the system parameters outlined earlier. We also determine the most probable point-of-breach and its posterior probability using our model. We demonstrate the use of our model by way of a numerical example. Finally, we illustrate the generalization of this approach to an arbitrary system and outline a method to compute its system parameters, viz., the breach probability, the attack matrix and the access matrix.