The increasing demand for smart city applications and Internet of Things (IoT) solutions has led to a growing need for simulation tools that can accurately model and analyze complex systems. iFogSim and Simulator of Urban Mobility (SUMO) are two popular simulation tools that cater to different aspects of smart city and IoT applications. iFogSim focuses on fog computing and IoT simulations, while SUMO specializes in traffic and mobility simulations. The integra-tion of iFogSim and SUMO simulators can enable researchers and developers to design and optimize smart city applications more effectively by providing a robust testbed. This paper proposes integrating iFogSim and SUMO to create a comprehensive simulation framework, iFogSUMO, that accurately models and analyzes adaptive traffic control systems. The frame-work was evaluated for ambulance routing by simulating traffic with various vehicle types and executing the adaptive traffic control algorithm to assess its performance in terms of trip time, waiting time, and time loss during ambulance routing, while maintaining scalability across heterogeneous vehicle fleets.

This review examines how advances in fog computing help mitigate physiological stress in urban residents by addressing one of its key contributing factors, traffic congestion. Commuters wait for hours on congested roads, with noise pollution and the feeling of helplessness adding to the frustration, the main cause being a steep increase in automobiles and a lack of traffic management. Traffic congestion can be associated with elevated cortisol levels, symptoms of anxiety and depression, and general decline in well-being. We have identified and analyzed research dealing with the implementation of adaptive traffic signals to enhance traffic flow and prioritize emergency vehicles. Various technologies, including machine learning, AI, and CNN, have been investigated. The majority of the present methods treat traffic data at the cloud level. This approach is expensive and uses significant bandwidth. Fog computing is an alternative for processing this traffic data close to the edge. The fog computing approach has reduced latency, making traffic systems smarter and cheaper. As a result, it can contribute to improving overall traffic management, with the potential to alleviate stress and enhance the mental well-being of commuters.

Federated learning (FL) is a technique that allows decentralized model training on local datasets, removing the necessity to deliver the data to a coordinator. In contrast to conventional methods where data is collected and centralized, federated learning enables credit bureaus to construct credit ratings using a distributed approach. In this approach, a global model is trained by aggregating localized models from edge devices (smartphones or IoT devices located closer to the data or the runner). Despite the advantages of federated learning over centralized methods, the open training environment and exposed model parameters make it vulnerable to malicious poisoning attacks. Various poisoning attacks encompassing label-flipping, clean-label, model and data poisoning, have the potential to compromise the integrity and efficacy of federated learning systems. This research study sheds light on the utilization of federated learning for credit score prediction, highlighting the effects of poisoning attacks on learning metrics. By better understanding these attacks and their impact, enhanced strategies to detect and mitigate such threats, safeguarding the integrity of federated learning systems in various domains can be devised. A credit scoring system is implemented based on the decentralized approach of federated learning using a non-IID dataset. The impact of changing parameters, such as the quantity of runners and attack percentage on learning metrics for three types of attacks, viz., label-flipping, Byzantine, and data poisoning are observed.

The proliferation of network-enabled devices and the increasing reliance on digital services have underscored the importance of effective network monitoring and security measures. DDoS attacks continue to pose significant threats to network availability and performance, targeting critical infrastructure and disrupting business operations. This research paper presents a detailed methodology for constructing an experimental setup using Mininet to simulate and collect traffic flow data during Distributed Denial of Service (DDoS) attacks. The paper outlines the steps involved in setting up the network environment, configuring traffic generation, launching DDoS attacks, and collecting traffic flow data for analysis. The proposed setup provides researchers with a controlled environment to study DDoS attack behaviors and evaluate mitigation strategies.

Traffic congestion is a growing problem in India, largely caused by the increasing number of vehicles. To address this, creating an adaptive traffic control system has become essential. To present a solution, this paper aims to create a resilient, adaptive traffic control system that dynamically adjusts signal timings based on the current traffic density. The paper proposes introducing a Fog layer at each traffic junction to capture real-time video feeds and process them locally, with the final decision about dynamic signal control being taken in the Cloud. The validity of the solution is tested through simulations in SUMO. A Machine Learning model is trained in the Cloud to predict expected vehicle numbers to set adaptive signal times for contingency situations. The paper aims to demonstrate the resilience and performance of the system with the adaptive traffic signal control algorithm, potentially offering a solution to traffic congestion in India.

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,

Differential Privacy (DP) conceptualizes a system for sharing information about a cluster of individuals in a dataset and protecting their privacy at the same time. DP, characterized by parameters (ϵ, δ), provides mathematical assurances on privacy and hence is a recognized approach for privacy-preserving data analysis and aggregation. DP data transformation mechanisms add randomness to the data to achieve differential privacy, therefore adversely impacting the utility of the data resulting in a trade-off between utility and privacy. While researchers have developed numerous mechanisms for differentially private data aggregation and sharing, a desirable mechanism should be fault-tolerant (ability to compute aggregate of partially shared data) and should obviate the need for interactive communication amongst data owners or the need for a trusted aggregator. Various researchers have endeavored to address some aspects of desirability. However, none have addressed all the desirable features simultaneously. Developing a distributed, differentially private aggregation mechanism called Privacy-Preserving Endpoint Aggregation (PPEA), satisfying all the desirable characteristics of DP data aggregation and sharing is a key contribution of this thesis. Additionally, by the experimentation, it is demonstrated that PPEA provides better utility at par with an existing ‘Central, Trusted’ method called PrivEx. An improvement of 11% is observed in PPEA w.r.t. PrivEx on UCI ML Smart Meter Dataset [3]. This is further verified for Synthetic Dataset and gives 4 times better utility as compared to PrivEx [5]. PPEA meets the standards of current state of art technique ‘Central, Trusted’ techniques. DPBench benchmark[1] principles have been followed to design the set of experiments while validating the technique. In addition to experimental validation, PPEA has been validated on real-time IoT devices for feasibility [8]. Issues like the implementation of Random number generators and data processing have been resolved and then to verify the correctness of the implementation DPBench trends were verified. Based on the memory, time and power consumption pattern, the implementation is feasible for IoT devices Xiaomi MiBand2 and XD58C Pulse Sensor. Any DP data aggregation mechanism must ensure data utility for further data analysis in the trade-off between utility and privacy. This trade-off must be addressed by the appropriate choice of key DP parameters (ϵ, δ). While certain guidelines or ranges for the selection of ϵ have been proposed by many researchers, none of them have proposed a systematic method to select the value of DP parameters. We have defined a cost-based profit maximization model [7], where the profit is the difference between the gains obtained by utilizing the data and the loss incurred due to disclosure of data. For this, the utility has been modeled mathematically, thereby reducing the requirement of conducting multiple experiments to calculate the utility, which may not be feasible in many cases due to privacy concerns. The correctness of the mathematical model is verified on multiple real-world and synthetic datasets. It is shown that the model does not depend on the type of input data and is applicable for multiple data distributions. The utility calculated mathematically is within 1.6% of the actual values obtained experimentally for all synthetic datasets, while the mathematical utility for the London Smart Meter dataset[4] is within 4.25% of the actual value. The cost of privacy loss used in the method of recommendation is based on the disclosure risk [2] which is the direct probability of disclosure, which is more practical and explainable in contrast to the constant multiple of ϵ that is used in the literature. This cost can also be explained in terms of the cost of providing insurance. These privacy recommendations are also shown to be conveniently extensible to other standard statistical aggregates such as mean and standard deviation and also as a group of multiple statistics simultaneously. This has been validated for a synthetic dataset and the recommended ϵ provides the best profit [7]. This work on the recommendation of privacy parameters facilitates the analyst to select the best DP technique from a given set of techniques. This can be used to design a generic method that can help select the best technique for the type of analysis by using weights of each type and data parameters. The financial model and other methods of selection in the literature depend on the availability of financial information and data parameters, that may not be available in most cases. So another crucial part of this work is the fallback methods[6] for selecting the optimal value when financial information is not available. Three methods viz Linear Scalarization, Least Square Optimization, and Upper-bound recommendation have been proposed to address this. The first two methods enable precise recommendation of the value of ϵ, while the elasticity-based method provides a safe upper bound. This is done by using normalized deviation as a shared unit to allow the comparison of disclosure risk and utility for the trade-off and selection of the best value. In future the recommendation methods can be extended to Machine Learning. For ML-based algorithms the utility can be measured by metrics such as accuracy, precision, recall, and F1-Score which are difficult to estimate mathematically. So data-driven estimates can be used instead. This allows the application of our recommendation methods to ML algorithms and is an interesting extension to this work. Application of recommendation methods to privacy preservation methods other than differential privacy can also be explored.

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

In today’s time, virtualization is used by Cloud providers to provide multiple virtual machines to the customers. Even though there are various benefits of virtualization in terms of cost or maintenance, still there is a high possibility of attacks on cloud servers and hypervisors. In order to provide system-level protection to all the servers running under a hypervisor within the control of a hypervisor, implementing an intrusion detection system at the hypervisor level is highly recommended. While devising such a detection model, it is observed that in addition to the network layer and transport layer features of a network packet, the frame layer features are also having a high impact on the detection performance. This paper focuses on designing a hypervisor-based intrusion detection system that considers the frame layer features and analyzes their impact on intrusion detection performance. The detection model also uses three types of machine learning classifiers and the best classifier is identified out of our experimentation.

In the era where online businesses are booming ranging from online shopping to ordering food, the use of location-based services has increased dramatically. To get the benefits of location-based services, users have to share some sensitive information like real-time locations. The leakage of such data could have serious consequences like identity theft and personal attacks. Certain models have been developed but also lack in achieving the right trade-off between privacy and utility. The proposed solution achieves a reasonable tradeoff between utility and privacy. To protect the user’s privacy without degrading the Quality of Service, the proposed idea uses multiple privacy preservation techniques together such as Geo-Indistinguishability, Differential Privacy, and Hashing.

The source code of any software is confidential and its intellectual property has to be protected from the malicious intent of the attackers. The present work offers the architecture of a cloud model for secure intellectual property protection of arithmetic expressions in a source code. The data obfuscation techniques are meant to be applied on different types of arithmetic operators in an arithmetic expression, “The required algorithms proposed in this paper are named as i) ‘Adaptive Number Generation’ obfuscation to be applied on all operators ii) Obfuscation by ‘Carry Padding’ to be applied on ‘+’ operator iii) Three Obfuscations by ‘Mixed Boolean Arithmetic’ to be applied on ‘+’, ‘-’ and ‘*’ operators and iv) ‘A-RSA+′ obfuscation to be applied on ‘+’ operator.” Experimentations are conducted to validate their functionality and performance metrics measurements in terms of Cyclomatic complexity, Time complexity, Space complexity and Potency analysis are carried out on a set of benchmark source codes and it has been found that there is only a very slight increase of 6.57% number of lines of code in the obfuscated codes compared to the corresponding original, the average increase in the obfuscated code's execution time is 29% and the estimated average potency given by the proposed obfuscation techniques is 1.06% which is also greater than one. Also, a mathematical model is developed to estimate the Mean Obfuscated Source Code Cloud Delay (MOSCD), which is the time duration in which the obfuscated source code is available physically at the cloud server to face any possible Man-At-The-End (MATE) attacks. An attacker may build a control flow graph (CFG) out of the source code to perform attacks based on static and dynamic analysis-based reverse engineering. The time required to generate a CFG is also estimated and in comparison, with the MOSCD, it is observed that the minimum CFG build time is almost three multiples of the maximum MOSCD value we received in our experimentations it is proved that our model is strong to thwart away any possible MATE attacks.

With the ever-increasing road traffic density, controlling the traffic becomes an enormous task. Since the number of people in the traffic police department is limited, it becomes difficult to manage the traffic in all city areas manually. The current traffic control signal timing is fixed and does not vary depending on the traffic density. The proposed Real-Time Auto-mated Traffic Signal (RTATS) system detects, tracks, and counts the vehicles on the road using You Only Look Once (YOLO), Euclidean-distance tracker, Line of Interest (LOI) algorithms, and Computer Vision techniques to calculate to fine-tune the Green Signal Time (GST). On the basis of the video (CCTV) surveillance camera data, the traffic flow, density, and average speed of vehicles can be calculated. The proposed RTATS system develops an algorithm for dynamically adjusting the green signal time (DGST). It varies the green signal timing based on the road traffic density at the junction and the average traffic volume contained by each type of vehicle on the road. The system will enable the fine-tuning of Green Signal Time (GST) and maximize the traffic flow with minimum human intervention.

Several applications collect users’ trajectory data to provide better location-based services. Since the data reveals the mobility of the users as a group and as an individual, publishing this trajectory data may pose a threat to one’s privacy. The current trajectory data mining procedures take all the raw information from the trajectory data without considering the users’ privacy. This type of data mining can be prone to linkage attacks. This research proposes a novel mechanism that uses OpenStreetMap and its geo-entities recognition feature to extract and cluster the traffic signals to form vertices of a graph. From the trajectory data, vehicle type and the edge in the graph is determined. The vehicle count for each vehicle type on the edge of a graph is iteratively updated and it is visually represented on the respective road edge between two junction vertices. The visualization of the weighted road network graph enables analysts to infer useful information as the road graph itself preserves the statistical information of the road density but hides the individual users’ trajectory.

Acquiring and aggregating data from a group of individuals is crucial for studying their general behavior. Differentially Private (DP) techniques, characterized by the parameter ϵ, help to protect Individually Identifiable Data (IID) of individuals participating in such data collection. However, such techniques affect the usefulness of the data leading to a trade-off between usefulness and privacy, thereby making the selection of ϵ an important problem before data acquisition. In this work, we use a mathematical formalism to estimate usefulness and privacy for sum query as aggregate analysis for the local model of privacy. The mathematical relation enables the application of a variety of optimization techniques, discussed in the work, to select an optimal value of ϵ. Existing methods for selecting ϵ are based on financial parameters, but they heavily rely on past data and domain knowledge which may not be available in many cases. To address this, we have provided Knee-point based recommendations along with a selection criterion to choose the method of recommendation depending on the availability of information. This allows analysts to take enlightened decisions while negotiating the value of ϵ. Our experiments on synthetic and real-world datasets unambiguously demonstrate the strength of the mathematical model and the recommended values

Differential privacy (DP) techniques provide important mathematical guarantees of privacy and in particular local DP mechanisms used to protect individual privacy without needing to trust any external entity. However, validation of these techniques is usually carried out using static datasets since IoT devices generating real-time streaming data pose additional difficulties. Hence, current work aims to validate the effectiveness of one such scheme, Privacy-Preserving Endpoint Aggregation (PPEA), on real-time private data obtained from resource-constrained edge devices by measuring utility metrics for the average operation aggregate function. This paper aims to study the feasibility of implementing PPEA for periodic real-time heart rate collection from fitness trackers, which are pervasive IoT devices within the personal healthcare domain capable of recording individual's private data, by considering factors like memory consumption, execution time, and power consumption. We address challenges concerning resource limitations on edge devices regarding lacking out-of-the-box provisions for implementing randomization techniques to achieve DP on streaming data.

Obfuscation is the technique of making code difficult to read or comprehend. Nowadays, attackers often hide their malicious code using obfuscation and use it as a tool to exploit users. In order to grasp the internal logic of the code and formulate countermeasures against malicious code (hidden by obfuscation), obfuscated code must be detected or eliminated (“deobfuscated”). The current technology is evolving in terms of securing software from obfuscated malware. This paper proposes a comprehensive solution to detect obfuscation using techniques like Taint Analysis, Symbolic Execution of Code, Construction of Control Flow Graph. Further, this study also includes a comprehensive comparative analysis of existing deobfuscation tools and gives a complete solution to tackle some of the most prevalent obfuscation techniques like Javascript, Packer and XOR Obfuscation.

Manufacturers offer Trade Promotions to retailers with the objective of making their products more accessible to end consumers or reducing the inventory level. There are two broad types of trade promotions - off-invoice and scan-back. Retailers prefer off-invoice promotions, but this often leads to lesser profit for manufacturers because of the forward buying from retailers in the promotion period. Manufacturers prefer scan-back-based promotions. But retailers are not attracted to the simple Scan-back deals due to the risk involved in selling the product to the end consumer in the stipulated promotion period. The proposed model designs a promotional strategy that is acceptable to both parties - manufacturer and retailer. The promotion strategy is designed in such a way that it maximizes the manufacturer's profit while ensuring that the retailer's profit is greater than or equal to the retailer's profit in an off-invoice case. Two approaches are tried out to check the overall profitability of the supply chain: without Inventory cost and with Inventory cost. The system uses the log-log regression model to assess the impact of the product's price and its corresponding demand, thereby providing the price elasticity value, which is one of the essential input parameters. The optimization model is a constrained nonlinear optimization model.

The ability to analyze personal data for a group of individuals without compromising their respective privacy has been a focus of significant research in recent years. For such analyses, data analysts need to acquire data from individuals without revealing their Individually Identifiable Data (IID). Well established Differentially Private techniques, characterized by privacy parameters (ϵ,δ), transform the data to protect the IID. However, such transformations adversely affect the usefulness of data leading to a trade-off between usefulness and privacy. Therefore, negotiating appropriate values of privacy parameters before data acquisition is a challenging task for data analysts. Most of the work, in selecting values of privacy parameters, is either based on constraining all other parameters or they provide a set of acceptable values. Here also the problem of selecting the best value from the set of acceptable values is left to the analyst. A major contribution of this paper is the method of identifying the best value of privacy parameters in a trade-off between usefulness and privacy by introducing a cost-based model, thereby addressing the issue. To enable estimation of usefulness and its cost before data acquisition, we have mathematically modeled utility in terms of data and privacy parameters. We have considered standard statistical aggregates such as Sum, Mean and Standard Deviation as compared to most of the existing works that consider only Count query as aggregate analysis. The correctness of our mathematical estimation has been validated on a diverse set of synthetic and real-world datasets spanning popular data distributions.