Implementation of the secure database management system requires to achieve confidentiality from the triad of CIA (Confidentiality, Integrity, Availability). Confidentiality becomes more valuable when data is available on cloud. The main concern is forestalling cloud administrators, database administrators, and software developers to get plain data that resides in the database. Whereas encryption is the fundamental tool that may be used to ensure data confidentiality by picking the popular encryption methods such as AES-128, AES-192 or AES-256. In this study, we demonstrated our approach on Land Record Management Information System (LRMIS). After implementing the proposed methodology, technical persons are prohibited to get confidential data.

Data breach is a common phenomenon is these days. Entities holding the personal data are involved in providing data to marketing and other companies for their benefit. Consequently, the citizens suffer and pay the price of breaching. Various countries have adopted personal data protection laws in line with General Data Protection Regulations (GDPR). The California State has also made legislation to secure consumer rights in respect of personal data. This study made a comparison between General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). After study, it has been identified that GDPR is a comprehensive document which can be used for providing security of personal data around the world. It has all the relevant clauses/ Articles that can be used accordingly. Furthermore, being dynamic in nature it has the capability to become adoptable to new changes/ technologies. However, there is a need to expend the scope of the study and conduct a comparative analysis on the basis of geographical boundaries. The future directions may include the study of laws relating to personal data protection of various developing countries in the context of the GDPR.

The risk of malware has increased drastically in recent years due to advances in the IT industry but it also increased the need for malware analysis and prevention. Hackers inject malicious code using awful applications. In this research, a framework is proposed to identify malicious Android applications based on repacked malicious code. The sensitive features of android applications are extracted using source code. These extracted features are compared with existing malware signatures to identify repacked malicious android applications. Experiments are performed using 3490 android-based malware samples belonging to 21 different malware families. A threshold value for malware categorization is defined using fuzzy logic. If the fuzzy comparison match is greater than 40%, the application is malicious. Meanwhile, if the match is greater than 10% and less than 40%, the application is suspicious otherwise benign. Furthermore, the proposed framework presents around 74% of the repacked malware compared to other similar approaches.

The number of client-side attacks is increasing day-by-day. These attacks are launched by using various methods like phishing, drive-by downloads, click-frauds, social engineering, scareware, and ransomware. To get more advantage with less exertion and time, the attackers are focus on the clients, rather than servers which are more secured as compared to the clients. This makes clients as an easy target for the attackers on the Internet. A number of systems/tools have been created by the security community with various functions for detection of client-side attacks. The discovery of malicious servers that launch the client side attacks can be characterized in two types. First to detect malicious servers with passive detection which is often signature based. Second to detect the malicious servers with active detection often with dynamic malware analysis. Current systems or tools have more focus on identifying malicious servers rather than preventing the clients from those malicious servers. In this paper, we have proposed a solution for the detection and prevention of malicious servers that use the Bro Intrusion Detection System (IDS) and VirusTotal API 2.0. The detected malicious link is then blocked at the gateway.

The need of automated information extraction increases with the increase in biomedical text. Named entity recognition is one of the core tasks in automatic information extraction. Performing named entity recognition tasks are quite challenging in biomedical field. These challenges include limited availability of annotated datasets and misclassification of entities having multiple meanings. Many Neural Network and deep learning-based models are developed for overcoming these challenges and for increasing the performance of named entity recognition tasks. This paper compares different models based on neural network architecture. The performance of these models is compared on JNLPBA dataset. The results show that Long short-term memory - conditional random field model with Wiki PubMed-PMC embeddings has outperformed other models by achieving highest precision and F1-score. CollaboNet model achieves the highest recall. Further analysis is needed to explore and compare the tools for performing named entity tasks in biomedical field.

This book chapter discusses two of the agent-based modeling (ABM) levels, i. e. exploratory agent-based modeling (EABM) and validated agent-based modeling (VABM). In first part of this chapter, we shall briefly explain EABM with the help of a case study of 5G networks modeled in an agent-based simulator called NetLogo [1] of the use cases of 5G networks is Internet of Things (IoT). We designed, implemented and experimented this case study to explore the futuristic approaches to ease the implementation of this under-developing 5G networkwhich still needs to be explored. Next, we discuss another important level of modeling, i. e., VABM. Since ABM approach has turned into an attractive and efficient way for displaying large-scale complex systems, verification and validation (V&V) of these models have become questionable. Here, we shall briefly explain VABM with the help of the same case study of 5G networks modeled as in EABM. Using VABM, we shall validate the case study if it is a credible solution.

Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ​we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.

Networks are important storage data structures now used to store personal information of individuals around the globe. With the advent of personal genome sequencing, networks are going to be used to store personal genomic sequencing of people. In contrast to social media networks, the importance of relationships in this genomic network is extremely significant. Losing connections between individuals thus implies losing relationship information (E.g. father or son etc.). There currently exists a considerably serious problem in the current approach to storing network data. Simply stated, network data is not tamper-evident. In other words, if some links or nodes were changed/removed/added by a malicious attacker, it would be impossible for the administrator to detect such changes. While, in the current age of social media networks, change in node characteristics and links can be bad in terms of relationships, in the case of networks for storing personal genomes, the results could be truly devastating. Here we present a scheme for building tamper-evident networks using a combination of Cryptographic and Ego-based Network analytic methods. Using actual published data-sets, we also demonstrate the utility and validity of the scheme besides demonstrating its working in various possible scenarios of usage. Results from the extensive experiments demonstrate the validity of the proposed approach.

The Internet of Things vision has recently emerged as a result of a proliferation of a large number of networked consumer electronic devices. There is a growing need to autonomously monitor power consumption of these devices. We present a self-organizing distributed algorithm for the dynamic approximation of power consumption in networked consumer electronic devices.

Targeted cyber-threats are topmost concern of organizations and technologies of today. Malwares having similar objectives bear common artifacts. Thus defining a detection mechanism based on such peculiar artifacts will not only help in detecting existing risks but also gives a considerable defense against unknown malicious attacks. About 903 known malware samples related to espionage were analyzed statically and a data set comprising related artifacts is established and also checked against the benign software. Weightage is given to each artifact on the difference of its existence in malicious and benign code and artifact’s relation to the expected targeted organization or technology thus catering for targeted attacks. Designed algorithm for detection of espionage attack has given 99.16 % of authentication and 99.33 % of precision. Real time alarm generation is also incorporated by API hooking using Detour library for latter detailed analysis of suspicious program or application by proposed algorithm.

Background Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? Purpose The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. Method We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Results Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

Wireless Sensor Networks (WSN) are a fast-emerging area of interest in the domain of large-scale communication networks. However, design of novel WSN applications requires first developing models and performing extensive simulation. Modern large-scale WSNs are expected to be both dynamic as well as random and often spread over a large scale. However, this problem has not previously been addressed in literature. In our previous works, we have demonstrated how Agent-based modeling may be used to effectively model various types of Complex Adaptive Systems such as, but not limited to, Self-organizing Communication infrastructures. In this paper, we present first steps towards providing a comprehensive set of guidelines and tutorial for developing deployment models of WSNs besides modeling routing algorithms using agent-based modeling. Our simulation results demonstrate the effectiveness and ease of use coupled with a short learning curve involved in developing agent-based models of complex WSN applications.

The students and administrative staff members of Punjab University, Lahore (new campus) were screened for the presence of hepatitis B antigens (HbsAg) and HCV antibodies (anti- HCV). The prevalence rate of anti-HCV was found 1.48 % and HbsAg was 2.46 % respectively, no overlapping between the seropositiity of HBV and HCV. In order to prevent the transmission of HH H HBV and HCV through blood transfusion, it is essential that all donors should be screened for anti- HCV and HbsAg. There is need to create the awareness about it.