Concealing Cyber-Decoys using Two-Sided Feature Deception Games

An increasingly important tool for securing computer networks is the use of deceptive decoy objects (e.g., fake hosts, accounts, or files) to detect, confuse, and distract attackers. One of the well-known challenges in using decoys is that it can be difficult to design effective decoys that are hard to distinguish from real objects, especially against sophisticated attackers who may be aware of the use of decoys. A key issue is that both real and decoy objects may have observable features that may give the attacker the ability to distinguish one from the other. However, a defender deploying decoys may be able to modify some features of either the real or decoy objects (at some cost) making the decoys more effective. We present a game-theoretic model of two-sided deception that models this scenario. We present an empirical analysis of this model to show strategies for effectively concealing decoys, as well as some limitations of decoys for cyber security

Consumption-Based CO2 Emissions on Sustainable Development Goals of SAARC Region

Consumption-based CO2 emission (CBE) accounting shows the possibility of global carbon leakage. Very little attention has been paid to the amount of emissions related to the consumption of products and services and their impact on sustainable development goals (SDGs), especially in the SAARC region. This study used a CBE accounting method to measure the CO2 emissions of five major SAARC member countries. Additionally, a Fully Modified Ordinary Least Square (FMOLS) and a causality model were used to investigate the long-term effects of the CBE and SDG variables between 1972 and 2015. The results showed that household consumption contributed more than 62.39% of CO2 emissions overall in the SAARC region. India had the highest household emissions, up to 37.27%, and Nepal contributed the lowest, up to 0.61%. The total imported emissions were the greatest in India (16.88 Gt CO2) and Bangladesh (15.90 Gt CO2). At the same time, the results for the long-term relationships between the CBEs and SDGs of the SAARC region showed that only the combustible renewables and waste (CRW) variable is significant for most of these countries. The sharing of the responsibility for emissions between suppliers and customers could encourage governments and policymakers to make global climate policy and sustainable development decisions,which are currently stalled by questions over geographical and past emission inequities

Game-Theoretic Deception Modeling for Distracting Network Adversarie

In this day and age, adversaries in the cybersecurity space have become alarmingly capable of identifying network vulnerabilities and work out various targets to attack where deception is becoming an increasingly crucial technique for the defenders to delay these attacks. For securing computer networks, the defenders use various deceptive decoy objects to detect, confuse, and distract attackers. By trapping the attackers, these decoys gather information, waste their time and resources, and potentially prevent future attacks. However, we have to consider that an attacker with the help of smart techniques may detect the decoys and avoid them. One of the well-known challenges in using decoys is that it can be difficult to design effective decoys that are hard to distinguish from real objects, especially against sophisticated attackers who may be aware of the use of decoys. Both real and decoy objects have observable features that may give the attacker the ability to distinguish one from the other. One way for a defender to enhance a decoyâ??s effectiveness is to modify a few features of either the real or fake objects. But such information manipulation or system modification for the defender needs to be cost-effective. Game-theoretical models are often useful to analyze strategic interactions between agents to find the best decision-making solutions. In this thesis, I study some game-theoretic and adversarial machine learning models to determine optimal strategies for the defender and focus on employing decoys to prevent security threats. The first game model I work to design practical decoy objects that can fool a sophisticated attacker. This model allows us to investigate many aspects of how a defender should optimize efforts to conceal deceptive objects, which can be applied to honeypots, disguising network traffic, and other domains. Furthermore, its theoretical foundation provides the benefits and limitations of adversarial learning methods for generating deceptive objects. In our model, we allow the defender to modify either the real or fake object that renders objects indistinguishable for the attacker thus, improve deception noticeably. By using this model, we seek to capture some key aspects of cyber deception that are missing from other game-theoretic models. In particular, we focus on whether the defender can design convincing decoy objects and the limitations of deception if some discriminating features of real and fake objects are not easily maskable. To my knowledge, this the first model that introduces a two-sided deception technique to mislead the attackers. However, an important element to take into account for the use of two-sided deception is cost. Here we show, in some cases, deception is either unnecessary or too costly to be effective. The deception level mainly relates to attackersâ?? sophistication, wherein naïve attackers are easy to deceive even with a low-cost strategy. This game model provides a new and more nuanced way to consider the quality of various deception strategies but strives to solve large and complex two-sided feature deception problems. To further develop and scale the model, we use the Adversarial Machine Learning (AML) approach that can generate fake samples that look like real samples and real samples that look like fake samples when the feature space is complex and large. The technique can also be used as a robust classifier for the binary classification problem in a dynamic learning environment. We also present the empirical analysis of the AML algorithm and discuss some possible use cases of our model. The next game-theoretic model I design to use deceptively crafted honey traffic to confound the knowledge gained by an adversary through passive network reconnaissance. This model characterizes how a defender should deploy honey traffic in the presence of a sophisticated attacker and finds the optimal strategy for deploying honey flows that are fast enough to be used for realistic networks. These optimal defender strategies deter an attacker from acting on the existence of real vulnerabilities found in network traffic. Our proposed model balances cost and benefit trade-offs, but can still be solved quickly enough to be used in a complex network environment. We show that the strategic optimization benefit is the highest when the cost of producing honey flow is reasonable, which is the most likely real application scenario, and the network defender should generate more honey flows to cover the highly valued vulnerabilities. We extend the current game model by addressing that the attack distributes beliefs over various types of honey traffic, reflecting the quality of the honey flows, indicating how hard it is for the attacker to distinguish. In addition, the attacker needs to pay a cost to attack. This model further captures how the honey traffic quality impacts attacks decision-making strategies. We show that high quality honey traffic makes it harder for the attacker to distinguish between real and honey traffic

Game-Theoretic Deception Modeling for Distracting Network Adversaries

In this day and age, adversaries in the cybersecurity space have become alarmingly capable of identifying network vulnerabilities and work out various targets to attack where deception is becoming an increasingly crucial technique for the defenders to delay these attacks. For securing computer networks, the defenders use various deceptive decoy objects to detect, confuse, and distract attackers. By trapping the attackers, these decoys gather information, waste their time and resources, and potentially prevent future attacks. However, we have to consider that an attacker with the help of smart techniques may detect the decoys and avoid them. One of the well-known challenges in using decoys is that it can be difficult to design effective decoys that are hard to distinguish from real objects, especially against sophisticated attackers who may be aware of the use of decoys. Both real and decoy objects have observable features that may give the attacker the ability to distinguish one from the other. One way for a defender to enhance a decoy’s effectiveness is to modify a few features of either the real or fake objects. But such information manipulation or system modification for the defender needs to be cost-effective. Game-theoretical models are often useful to analyze strategic interactions between agents to find the best decision-making solutions. In this thesis, I study some game-theoretic and adversarial machine learning models to determine optimal strategies for the defender and focus on employing decoys to prevent security threats. The first game model I work to design practical decoy objects that can fool a sophisticated attacker. This model allows us to investigate many aspects of how a defender should optimize efforts to conceal deceptive objects, which can be applied to honeypots, disguising network traffic, and other domains. Furthermore, its theoretical foundation provides the benefits and limitations of adversarial learning methods for generating deceptive objects. In our model, we allow the defender to modify either the real or fake object that renders objects indistinguishable for the attacker thus, improve deception noticeably. By using this model, we seek to capture some key aspects of cyber deception that are missing from other game-theoretic models. In particular, we focus on whether the defender can design convincing decoy objects and the limitations of deception if some discriminating features of real and fake objects are not easily maskable. To my knowledge, this the first model that introduces a two-sided deception technique to mislead the attackers. However, an important element to take into account for the use of two-sided deception is cost. Here we show, in some cases, deception is either unnecessary or too costly to be effective. The deception level mainly relates to attackers’ sophistication, wherein naïve attackers are easy to deceive even with a low-cost strategy. This game model provides a new and more nuanced way to consider the quality of various deception strategies but strives to solve large and complex two-sided feature deception problems. To further develop and scale the model, we use the Adversarial Machine Learning (AML) approach that can generate fake samples that look like real samples and real samples that look like fake samples when the feature space is complex and large. The technique can also be used as a robust classifier for the binary classification problem in a dynamic learning environment. We also present the empirical analysis of the AML algorithm and discuss some possible use cases of our model. The next game-theoretic model I design to use deceptively crafted honey traffic to confound the knowledge gained by an adversary through passive network reconnaissance. This model characterizes how a defender should deploy honey traffic in the presence of a sophisticated attacker and finds the optimal strategy for deploying honey flows that are fast enough to be used for realistic networks. These optimal defender strategies deter an attacker from acting on the existence of real vulnerabilities found in network traffic. Our proposed model balances cost and benefit trade-offs, but can still be solved quickly enough to be used in a complex network environment. We show that the strategic optimization benefit is the highest when the cost of producing honey flow is reasonable, which is the most likely real application scenario, and the network defender should generate more honey flows to cover the highly valued vulnerabilities. We extend the current game model by addressing that the attack distributes beliefs over various types of honey traffic, reflecting the quality of the honey flows, indicating how hard it is for the attacker to distinguish. In addition, the attacker needs to pay a cost to attack. This model further captures how the honey traffic quality impacts attacks decision-making strategies. We show that high quality honey traffic makes it harder for the attacker to distinguish between real and honey traffic

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM_2.5): A Case Study in the Paso del Norte Region

This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality’s monitoring stations corresponding to 2014–2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality’s monitoring stations corresponding to 2014–2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models

The Role of the Discount Policy of Prepayment on Environmentally Friendly Inventory Management

Nowadays, more and more consumers consider environmentally friendly products in their purchasing decisions. Companies need to adapt to these changes while paying attention to standard business systems such as payment terms. The purpose of this study is to optimize the entire profit function of a retailer and to find the optimal selling price and replenishment cycle when the demand rate depends on the price and carbon emission reduction level. This study investigates an economic order quantity model that has a demand function with a positive impact of carbon emission reduction besides the selling price. In this model, the supplier requests payment in advance on the purchased cost while offering a discount according to the payment in the advanced decision. Three different types of payment-in-advance cases are applied: (1) payment in advance with equal numbers of instalments, (2) payment in advance with a single instalment, and (3) the absence of payment in advance. Numerical examples and sensitivity analysis illustrate the proposed model. Here, the total profit increases for all three cases with higher values of carbon emission reduction level. Further, the study finds that the profit becomes maximum for case 2, whereas the selling price and cycle length become minimum. This study considers the sustainable inventory model with payment-in-advance settings when the demand rate depends on the price and carbon emission reduction level. From the literature review, no researcher has undergone this kind of study in the authors’ knowledge

Exposure to environmentally relevant phthalate mixture during pregnancy alters the physical and hemato-biochemical parameters in Black Bengal goats

Several environmental pollutants, mostly chemicals and plasticizers, have an effect on the reproduction of small ruminants, causing abortion, delayed estrus, and decreased fertility. Phthalates are common in our environment and have been identified as endocrine disrupting chemicals (EDCs). The research work investigated the impact of dietary exposure to a phthalate mixture on physical and hemato-biochemical parameters in pregnant Black Bengal (BB) goats. A total of 20 clinically healthy, 1–2 months pregnant, aged 6–8 months with a body weight of 10–12 kg BB goats were collected and divided into two (n = 10) groups. The treatment group received a standard goat ration with a combination of different phthalates mixture while the control group was provided the same ration with the vehicle of aphthalatemixture until parturition. The physical parameters were measured with appropriate tools and blood samples were collected for hemato-biochemical tests. The results showed that the physiological parameters (body condition score, respiration rate and heart rate) were significantly (P < 0.05) reduced in phthalate-exposed goats without altering rectal temperature and rumen motility. The hematological parameters: RBC count, WBC count, hemoglobin concentration, hematocrit values and RBC indices were significantly (P < 0.05) lower in phthalate-exposed goats. Phthalate-exposed BB goats had significantly (P < 0.05) higher neutrophil and lower lymphocyte counts. Serum glucose, total protein, albumin and total cholesterol levels were significantly (P < 0.05) lower in phthalate-exposed BB goats but higher the values of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and blood urea nitrogen (BUN) levels in treated BB goats. It may be concluded that exposure to a phthalate mixture during pregnancy alters the physical, hematological and biochemical parameters in BB goats