Distributed environments such as edge and vehicular networks demand agile real-time trust mechanisms due to their decentralised and high-risk dynamics. We propose a dynamic, multi-layered trust model that adapts to the context of these environments, including vehicular networks. Unlike static models, it considers trust a dynamic and subjective construct, using inputs such as interaction history, community reputation, anomaly detection, operational context, and policy compliance to compute real-time trust scores. Graph-based structures represent trust relationships, with context and roles influencing the weighting of factors. Trust is calculated as a weighted sum of direct trust, reputation, and anomaly scores guided by a contextual policy. We also introduce a mistrust metric to incorporate negative signals for timely threat responses. We present the formal model and validate it using real-world scenarios in vehicular networks. Metrics such as Direct Trust, Reputation Score, Anomaly Score, and Policy Modulation are explicitly defined and temporally updated to reflect evolving entity behavior. Our contributions are: 1) A two-layer model for trust and mistrust evaluation, 2) A real-time, context-sensitive trust computation mechanism, and 3) A dynamic weighting approach guided by policy and operational roles. Our dual-layer trust and mistrust framework delivers actionable and adaptable scores, enabling robust security posture evolving distributed environments.

Social media platforms provide effective mediums for expressing opinions and thoughts on several topics openly. This does protect our right to freedom of speech, however the enormous reach of social media makes it a potential tool for widespread radicalization among the youth, irrespective of the geographical and demographical boundaries. This necessitates the need to effectively identify the content which is a source of mass online radicalization. In order to curb the propagation, security agencies need an automatic radicalization detection mechanism for mining the huge volumes of social media content. In this article, we propose an approach for detecting online radicalized accounts and quantifying the degree to which these user accounts are propagating radical content. We propose to use three novel features, i.e., Similarity to domain, presence of radical content and sentiment to calculate the radicalness score for each online user. Our algorithm uses a CNN-LSTM-based technique to effectively differentiate between radical/non-radical content with an accuracy of 93%. Our empirical results show that radicalness scores for known radicalized websites are higher as compared to the non-radical users. We believe this is a first ever attempt at quantifying the level of radicalization of users using scientific methods which can be very helpful to national security agencies in tracking suspicious online users and stop the spread of anti-national content on social media.

The advent of internet has led to prodigious growth in popularity of social media platforms for people to communicate and opinionate on topics of their interests. And, terroristic events being a topic of national importance, receives enormous response from the citizens. Unfortunately, miscreants with anti-national agendas incite the large available audience on these platforms against the country by inducing anti-national content amid terrorist attacks. The social media platforms being of informal use are commonly observed to have users opinionating using multiple languages in same sentence called code-mixing. The over-arching goal of research done is to identify anti-national code-mix textual content on YouTube in the form of comments on terrorism-related videos. We collected YouTube comments on videos related to terroristic events in Kashmir region of India, which consisted of code-mix comments in Hindi (native language of India) and English languages. The paper presents a novel deep-learning-based transformer model HE-CM-BERT, i.e. Hindi-English code-mix BERT, where we extend the vocabulary of pre-trained multilingual BERT with code-mix vocabulary extracted from the collected data to automate the detection of anti-national code-mix text. The comparative analysis of the proposed model with the state-of-the-art machine learning and deep learning models depicts that it outperforms the existing ones.

This paper aims to showcase the sentiments of Twitter users on the Kashmir conflict, which is an unresolved territorial conflict between two countries, India and Pakistan. Sixty thousand (60k) tweets have been collected using popular keywords posted by users living in the two involved countries and conflicting state. We proposed a twofold adaptive approach to exploit and compare different sentiment analysis techniques, namely lexicons (VADER and NRC) and machine learning. The analysis brings out that positive sentiments are higher in both the involved countries. However, there is a belief that people of both countries are negative and vicious about this act. The observations may help to break this myth and to foster positive engagement between the two nations, which are at the edge of a full-fledged war. The study provides a list of top Twitter influencer handles in each country and the disputed state. Thus, the purpose of this research is to scientifically analyze the situation to arrive at a fair decision and thereby removing unjust opinions that cause a lot of disturbance. This research can be used as fundamental to visualize the extensive insights of a critical event and help in identifying the groups which try to incite users on Twitter.

In the present digital world, usage of the internet has increased many folds as users have become dependent on the cloud-based applications. The disclosure of personal information on such platforms becomes a prospective threat for an attack. Researchers have used randomised data distortion technique with addition of random noise to conceal the sensitive data from an unauthorised adversary. This perturbation technique has relevance for the numerical datasets only. In this paper, we propose a hybrid model of two phases encoding with additive random noise value for ensuring non-disclosure of private and sensitive information and maintaining an effective balance between data privacy and data utility. The proposed technique has been tested on different data sizes of the real estate industry in terms of efficiency and effectiveness in preserving privacy and data utility. The proposed algorithm has been evaluated in terms of privacy level and information loss. It has proved effective in comparison with other privacy-preserving techniques such as perturbation and encryption in terms of space complexity and efficiency.

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

Terrorist groups have reconnoitered smarter ways to use online discussion forums for their violent plans. They have been using their privately owned discussion forums for various illegal purposes. A comparative study of work done on various dark web forums of terroristic organizations is done in this paper. This paper proposes a novel approach to identify procurement of modern weapons over the social media forum by terrorist groups. We used data from four dark web forum websites named “Ansar Aljihad Network”, “IslamicAwakening”, “Gawaher”, and “IslamicNetwork”. Multiple experts independently annotated 313 randomly selected posts as procurement (YES) or non- procurement (NO) to label the forum threads. Mutual agreement between experts is computed to find the level of significance. Furthermore, we used machine learning classification techniques (MLCT) in order to classify labeled posts. To our knowledge, our procurement model presents a first of its kind model to automatically detect procurement of modern weapons over dark web. The work done presents application of social media analytics and text mining to counter terrorism.

The recent spike in malware dissemination rate in various striving organizations necessitate the stringent demand for the establishment of suitable counter-measures for safeguarding Information Technology assets against customized malware attacks. The Anti-virus (AV) communities perform a vital role in the threat management of such dissemination by detecting new malware in the wild and assigning labels to them. However during an extensive study conducted on malware, a huge heterogeneity in their categorization has been observed. It adversely affects research communities, business organizations, and AV companies as this creates confusion and difficulty to study malicious programs. Thus, there is an immediate need of a system where malware is determined not on the basis of their ‘confusing’ names given by AV vendors but by its activities so that its damage potential on the victim machine can be determined. This paper presents malware naming conventions being used by AV vendors and characterizes malware capabilities on the basis of their characteristic features obtained after performing static and dynamic malware analysis. It proposes a novel technique, an initiative to solve the issue of inconsistencies that will assess the level of different capabilities of a malware using Fuzzy logic paradigm. This approach would remove the misunderstandings and confusion that arises due to inconsistent naming convention and improve clarity towards their mature and new attributes. The proposed approach is tested on a set of well-known real malware samples and the results obtained are compared with that of fuzzy clustering.

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.

In the arena of internet of things, everyone has the ability to share every aspect of their lives with other people. Social media is the most popular and effective medium to provide communication. Social media has gripped our lives in a dramatic way. Privacy of users data lying with the service providers needs to be preserved when published for the purpose of research as the release of sensitive personal information of an individual may pose security threats. This has become an important research area nowadays. To some extent, the concepts of anonymization that were earlier used to preserve privacy of relational microdata have been applied to preserve privacy of social networks data. Anonymizing social networks data is challenging as it is a complex structure with users connected to one another graphically and the most important is to preserve the structural properties of the graph depicting the social network relationships while applying such concepts. Recent studies based upon K-anonymity and L-diversity help to preserve privacy of online social networks data and subsequently identify attacks that arise while applying these techniques in different scenarios. K-anonymity equalizes the degree of the nodes to prevent the data from identity disclosure but it cannot preserve sensitive information and also cannot handle attacks arising due to background knowledge and homogeneity. To cope up with the drawbacks of K anonymity, L-diversity was introduced that protects the sensitive labels of the users. In this paper, a novel technique has been proposed which implements the combined features of K-anonymity and L-diversity. Our proposed approach has been validated using the data of real time social network–Twitter (most popular microblogging network). The performance of the proposed technique has been measured by the metrics, such as average path length, average change in sensitive labels, and remaining ratio of top influential users. It thus becomes evident from the results that the values of these parameters attained with the proposed technique for the anonymized graph has minimal variation to that of original structural graph. So, it is possible to retain the utility without compromising privacy while publishing social networks data. Further, the performance of the proposed technique has been discussed by calculating the information loss that addresses the concern of preserving privacy with the least variation of actual content viz info loss.

Determining the location of a mobile user is central to several crowd-sensing applications. Using a Global Positioning System is not only power-hungry, but also unavailable in many locations. While there has been work on cellular-based localization, we consider an unexplored opportunity to improve location accuracy by combining cellular information across multiple mobile devices located near each other. For instance, this opportunity may arise in the context of public transport units having multiple travelers. Based on theoretical analysis and an extensive experimental study on several public transportation routes in two cities, we show that combining cellular information across nearby phones considerably improves location accuracy. Combining information across phones is especially useful when a phone has to use another phone’s fingerprint database, in a fingerprinting-based localization scheme. Both the median and 90 percentile errors reduce significantly. The location accuracy also improves irrespective of whether we combine information across phones connected to the same or different cellular operators. Sharing information across phones can raise privacy concerns. To address this, we have developed an id-free broadcast mechanism, using audio as a medium, to share information among mobile phones. We show that such communication can work effectively on smartphones, even in real-life, noisy-road conditions.

Academicians and industrialists working on malware use static and dynamic analysis in order to understand their functionality and the menace level posed by them. Industries providing anti-malware solutions calculate malware threat level using the approaches that involve human intervention and demand the skilled analysts along with a large number of resources. With the increasing volume, velocity, and complexity of malware, assigning such a large number of resources is not possible. Thus, there is a need to develop techniques that can automatically compute the threat or damage posed by a piece of malware (to a victim machine) as soon as it appears in the wild. This assessment of damage capability level to a zero-day malware can help in providing early warnings about a specific piece of malware so that immediate attention could be paid to it in terms of allocating resources for performing a closer analysis. This paper presents an automated technique based on fuzzy modeling for computing damage potential of malicious programs, which is calculated on the basis of features obtained after performing automated analysis of malware binaries in the sandboxed environment.

Online Social Networks act as a popular forum to promote and manage the reputation of aristocrats. Generally, big organizations, politicians, celebrities, and journalists require a huge number of followers/fans to promote and manage their reputation on Online Social Networks such as Twitter. This demand of more followers has originated Twitter Followers Market that deals with the sale and purchase of fake/compromised Twitter accounts. In this paper, an analysis of merchants of this marketing industry has been conducted using graph- and content-based features. The present study has been conducted by collecting 15,750 tweets related to the sale or purchase of Twitter followers posted by around 995 users. The analysis infers that the merchants of this marketing industry fulfill the characteristics of spammers as stated by the rules and policies of Twitter. Further, machine learning classification approach has been used to categorize these merchants from genuine users based on their behavioral features. To the best of our knowledge, this is the novel study to analyze and then categorize the behavior of such merchants involved in the sale of Twitter accounts as spammers.

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

Malware is one of the most terrible and major security threats facing the Internet today. It is evolving, becoming more sophisticated and using new ways to target computers and mobile devices. The traditional defences like antivirus softwares typically rely on signature based methods and are unable to detect previously unseen malwares. Machine learning approaches have been adopted to classify malwares based on the features extracted using static or dynamic analysis. Both type of malware analysis have their pros and cons. In this paper, we propose a classification framework which uses integration of both static and dynamic features for distinguishing malwares from clean files. A real world corpus of recent malwares is used to validate the proposed approach. The experimental results, based on a dataset of 998 malwares and 428 cleanware files provide an accuracy of 99.58% indicating that the hybrid approach enhances the accuracy rate of malware detection and classification over the results obtained when these features are considered separately.

Malware is one of the most terrible and major security threats facing the Internet today. Anti-malware vendors are challenged to identify, classify and counter new malwares due to the obfuscation techniques being used by malware authors. In this paper, we present a simple, fast and scalable method of differentiating malwares from cleanwares on the basis of features extracted from Windows PE files. The features used in this work are Suspicious Section Count and Function Call Frequency. After automatically extracting features of executables, we use machine learning algorithms available in WEKA library to classify them into malwares and cleanwares. Our experimental results provide an accuracy of over 98% for a data set of 3,087 executable files including 2,460 malwares and 627 cleanwares. Based on the results obtained, we conclude that the Function Call Frequency feature derived from the static analysis method plays a significant role in distinguishing malware files from benign ones.

With the increase in online publishing of social network data the requirement to protect confidential information related to users has become the main concern of publishers. To cater to this need many anonymization techniques like K-anonymity, L-diversity and T-closeness has been proposed by various researchers for micro-data as well as social network data. In this paper we aim to protect sensitive information of users of Twitter-second most popular social networking site. For the purpose of carrying out anonymization, a crawler has been developed to collect data of around 10K users from publicly available information. Data of around 30 users have been used to carry out the experimental work using ARX tool. All three anonymization methods: K-anonymity, L-diversity and T-closeness have been used. Performance of technique is evaluated using information gain as a metric.

The web has become a vital global platform that binds together almost all daily activities like communication, sharing, and collaboration. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are even harder to identify. People in the public eyes like politicians, celebrities, sports persons, media persons and other public figures with huge followings are particularly vulnerable to this type of attacks. The main objective in this work is to identify those forged users who harm genuine ones, jeopardize the identity and hence the security and privacy of users. In this paper a framework for the detection of malicious users, non-malicious users and celebrities has been developed by using an attribute set for user classification based on user characteristics. For the purpose of detecting malicious users, non-malicious users and celebrities, a crawler has been developed for Twitter and data of around 22K users have been collected from publicly available information. Data of around 7,500 users have been used for training and testing purpose in Weka for classification of users. 5 classifiers have been used and compared on the basis of performance metrics like precision, recall, F-measure and accuracy. RandomForest outperforms all the classifiers with 99.8% accuracy.