Biblio

Generative Adversarial Network (GAN) models have been widely used in various fields. More recently, styleGAN and styleGAN2 have been developed to synthesize faces that are indistinguishable to the human eyes, which could pose a threat to public security. But latest work has shown that it is possible to identify fakes using powerful CNN networks as classifiers. However, the reliability of these techniques is unknown. Therefore, in this paper we focus on the generation of content-preserving images from fake faces to spoof classifiers. Two GAN-based frameworks are proposed to achieve the goal in the white-box and black-box. For the white-box, a network without up/down sampling is proposed to generate face images to confuse the classifier. In the black-box scenario (where the classifier is unknown), real data is introduced as a guidance for GAN structure to make it adversarial, and a Real Extractor as an auxiliary network to constrain the feature distance between the generated images and the real data to enhance the adversarial capability. Experimental results show that the proposed method effectively reduces the detection accuracy of forensic models with good transferability.

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.

Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware executables into image-based representations. These images are then classified into different malware families using an autoencoder enhanced deep convolutional neural network (AE-DCNN). In particular, we propose a novel training mechanism wherein a DCNN classifier is trained with the help of an encoder. We conjecture that using an encoder in the proposed way provides the classifier with the extra information that is perhaps lost during the forward propagation, thereby leading to better results. The proposed approach eliminates the use of feature engineering, reverse engineering, disassembly, and other domain-specific techniques earlier used for malware classification. On the standard Malimg dataset, we achieve a 10-fold cross-validation accuracy of 99.38% and F1-score of 99.38%. Further, due to the texture-based analysis of malware files, the proposed technique is resilient to several obfuscation techniques.

The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy.

Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.

Unit Commitment (UC) problem is one of the most fundamental constrained optimization problems in the planning and operation of electric power systems and electricity markets. Solving a large-scale UC problem requires a lot of computational effort which can be improved using data driven approaches. In practice, a UC problem is solved multiple times a day with only minor changes in the input data. Hence, this aspect can be exploited by using the historical data to solve the problem. In this paper, an Artificial Intelligence (AI) based approach is proposed to solve a Security Constrained UC problem. The proposed algorithm was tested through simulations on a 4-bus power system and satisfactory results were obtained. The results were compared with those obtained using IBM CPLEX MIQP solver.

The mishandled passenger baggage in aviation industry is still a big problem. This research is focused on designing a baggage handling system (BHS) at the airport for identifying and tracking of passenger baggage based on RFID technology. The proposed BHS system consists of hardware device to identify the baggage and the cloud-based tracking application. The BHS device is designed based on UHF passive RFID technology and IoT technology. The device can be used as handheld device in check-in counter and arrival area. The device can also be used as a fixed device in screening, sortation, and transition belt conveyer. The BHS device consists of RFID reader module, a microcontroller, LCD, keypad, a WiFi module and a storage device. The user and airport staff can track the luggage position and its status through dashboard application.

Nowadays, web vulnerability is a critical issue in web applications. Web developers develop web applications, but sometimes they are not very well-versed with security concerns, thereby creating loopholes for the vulnerabilities. If a web application is developed without considering security, it is harmful for the client and the company. Different types of vulnerabilities encounter during the web application development process. Therefore, vulnerability identification is a crucial and critical task from a web application development perspective. It is vigorous to secure them from the earliest development life cycle process. In this paper, we have analyzed and classified vulnerabilities related to web application security during the development phases. Here, the concern is to identify a weakness, countermeasure, confidentiality impact, access complexity, and severity level, which affect the web application security.

Steganography is the method of hiding one type of information into other type of information, hiding a secret a message in a cover so that others can't know the presence of the secret information. It provides an extra layer of security in communication and information sharing. Security is an important aspect of the communication process; everyone want security in communication. The main purpose of this paper is to introduce security of information that people share among them. In this paper we are presenting different methods of substitution techniques of image steganography and their comparison. Least significant bit and most significant bit substitution techniques are used. Information is hidden in an image file and then decoded back for the secret message. Hiding the presence of any hidden information makes this more secure. This implementation can be used by secret service agencies and also common people for secure communication.

With the development of information technology, the Internet of things (IOT) has gradually become the third wave of global information industry revolution after computer and Internet. Artificial intelligence (AI) and IOT technology is an important prerequisite for the rapid development of the current information society. However, while AI and IOT technologies bring convenient and intelligent services to people, they also have many defects and imperfect development. Therefore, it is necessary to pay more attention to the development of AI and IOT technologies, actively improve the application system, and create a network security management system for AI and IOT applications that can timely detect intrusion, assess risk and prevent viruses. In this paper, the network security risks caused by AI and IOT applications are analyzed. Therefore, in order to ensure the security of IOT environment, network security and privacy security have become the primary problems to be solved, and management should be strengthened from technical to legal aspects.

In proof-of-stake (PoS) blockchains, stakeholders that extend the chain are selected according to the amount of stake they own. In S&P 2019 the "Ouroboros Crypsinous" system of Kerber et al. (and concurrently Ganesh et al. in EUROCRYPT 2019) presented a mechanism that hides the identity of the stakeholder when adding blocks, hence preserving anonymity of stakeholders both during payment and mining in the Ouroboros blockchain. They focus on anonymizing the messages of the blockchain protocol, but suggest that potential identity leaks from the network-layer can be removed as well by employing anonymous broadcast channels.In this work we show that this intuition is flawed. Even ideal anonymous broadcast channels do not suffice to protect the identity of the stakeholder who proposes a block.We make the following contributions. First, we show a formal network-attack against Ouroboros Crypsinous, where the adversary can leverage network delays to distinguish who is the stakeholder that added a block on the blockchain. Second, we abstract the above attack and show that whenever the adversary has control over the network delay – within the synchrony bound – loss of anonymity is inherent for any protocol that provides liveness guarantees. We do so, by first proving that it is impossible to devise a (deterministic) state-machine replication protocol that achieves basic liveness guarantees and better than (1-2f) anonymity at the same time (where f is the fraction of corrupted parties). We then connect this result to the PoS setting by presenting the tagging and reverse tagging attack that allows an adversary, across several executions of the PoS protocol, to learn the stake of a target node, by simply delaying messages for the target. We demonstrate that our assumption on the delaying power of the adversary is realistic by describing how our attack could be mounted over the Zcash blockchain network (even when Tor is used). We conclude by suggesting approaches that can mitigate such attacks.

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, which is difficult to analyze for detecting any potential abnormal behaviour and application outage. This phenomenon makes Machine Learning (ML) a natural choice to leverage and analyze the API traffic and obtain intelligent predictions. This paper proposes an ML-based technique to detect and classify API traffic based on specific features like bandwidth and number of requests per token. We employ a Support Vector Machine (SVM) as a binary classifier to classify the abnormal API traffic using its linear kernel. Due to the scarcity of the API dataset, we created a synthetic dataset inspired by the real-world API dataset. Then we used the Gaussian distribution outlier detection technique to create a training labeled dataset simulating real-world API logs data which we used to train the SVM classifier. Furthermore, to find a trade-off between accuracy and false positives, we aim at finding the optimal value of the error term (C) of the classifier. The proposed anomaly detection method can be used in a plug and play manner, and fits into the existing micro-service architecture with little adjustments in order to provide accurate results in a fast and reliable way. Our results demonstrate that the proposed method achieves an F1-score of 0.964 in detecting anomalies in API traffic with a 7.3% of false positives rate.

The popularity of cryptocurrencies has garnered interest from cybercriminals, spurring an onslaught of cryptojacking campaigns that aim to hijack computational resources for the purpose of mining cryptocurrencies. In this paper, we present a cross-stack cryptojacking defense system that spans the hardware and OS layers. Unlike prior work that is confined to detecting cryptojacking behavior within web browsers, our solution is application agnostic. We show that tracking instructions that are frequently used in cryptographic hash functions serve as reliable signatures for fingerprinting cryptojacking activity. We demonstrate that our solution is resilient to multi-threaded and throttling evasion techniques that are commonly employed by cryptojacking malware. We characterize the robustness of our solution by extensively testing a diverse set of workloads that include real consumer applications. Finally, an evaluation of our proof-of-concept implementation shows minimal performance impact while running a mix of benchmark applications.

Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24×7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved.

A service-based system (SBS) dynamically composes third-party services to deliver comprehensive functionality. As adaptive systems, SBSs can substitute equivalent services within the composition if service operations or workflow requirements change. Substituted services must maintain the original SBS quality of service (QoS) constraints. In this paper, we add security as a QoS constraint. Using a model problem of a SBS system created for self-adaptive system technology evaluation, we demonstrate the applicability of security assurance cases and service security profile exchange to build in security awareness for more informed SBS adaptation.

The proposed method allows us to determine the predicted value of the complex systems information security risk and its confidence interval using regression analysis and fuzzy logic in terms of the risk dependence on various factors: the value of resources, the level of threats, potential damage, the level of costs for creating and operating the system, the information resources control level.

Considering the lack of theoretical analysis for distributed network under APT (advanced persistent threat) attacks, a game model was proposed to solve the problem based on APT attack path. Firstly, this paper analyzed the attack paths of attackers and proposed the defensive framework of network security by analyzing the characteristics of the APT attack and the distributed network structure. Secondly, OAPG(an attack path prediction model oriented to APT) was established from the value both the attacker and the defender based on game theory, besides, this paper calculated the game equilibrium and generated the maximum revenue path of the attacker, and then put forward the best defensive strategy for defender. Finally, this paper validated the model by an instance of APT attack, the calculated results showed that the model can analyze the attacker and defender from the attack path, and can provide a reasonable defense scheme for organizations that use distributed networks.

The article discusses an approach to identifying the uncertainty of initial information based on the theory of fuzzy logic. A system of criteria for initial information is proposed, calculated on the basis of the input sample, and characterizing the measure of uncertainty present in the system. The basic requirements for the choice of membership functions of the fuzzy inference system are indicated and the final integrated output membership function is obtained, which describes the type of uncertainty of the initial information.

In this paper, an effective Advanced Persistent Threat (APT) attack response system was proposed. Reference to the NIST Cyber Security Framework (CRF) was made to present the most cost-effective measures. It has developed a system that detects and responds to real-time AM-HIDS (Anti Malware Host Intrusion Detection System) that monitors abnormal change SW of PCs as a prevention of APT. It has proved that the best government-run security measures are possible to provide an excellent cost-effectiveness environment to prevent APT attacks.

Cybersecurity is becoming very crucial in the today's world where technology is now not limited to just computers, smartphones, etc. It is slowly entering into things that are used on daily basis like home appliances, automobiles, etc. Thus, opening a new door for people with wrong intent. With the increase in speed of technology dealing with such issues also requires quick response from security people. Thus, dealing with huge variety of devices quickly will require some extent of automation in this field. Generating threat intelligence automatically and also including those which are multilingual will also add plus point to prevent well known major attacks. Here we are proposing an AI based SOAR system in which the data from various sources like firewalls, IDS, etc. is collected with individual event profiling using a deep-learning detection method. For this the very first step is that the collected data from different sources will be converted into a standardized format i.e. to categorize the data collected from different sources. For standardized format Here our system finds out about the true positive alert for which the appropriate/ needful steps will be taken such as the generation of Indicators of Compromise report and the additional evidences with the help of Security Information and Event Management system. The security alerts will be notified to the security teams with the degree of threat.

Blockchain technology (BCT) has paved a way for new potentials of handling serious data privacy, integrity and security issues in healthcare. To curb the increasing challenges in healthcare industry, healthcare organizations need to apply blockchain technology to better improve patient safety and protect patients records from counterfeiting and fraud. The purpose of this research paper was to define BCT can assist in improving pharmaceutical industries in Saudi Arabia upon utilization of its application. This study adopted quantitative methods to gather the study data. Based on healthcare leaders perception and Internet connection, lack of cooperation, and economic inequality were found to be leading factors hindering the application of blockchain technology in the pharmaceutical industries, Saudi Arabia. Factors facilitating the application of blockchain technology in the pharmaceutical industries, Saudi Arabia were found as system robustness of BCT, increased data safety and decentralization, need for enhanced supply chain management and interoperability, and government laws and policies. Adopting interventions that are targeted to specific patient population medications, effective delivery systems, transit provider reimbursement far from intensity and volume of services towards value and quality was found to compromise the pre-existent challenges and real capacity in healthcare system. Although the relationship between implementation of blockchain technology and cost spending is negative in the short-term, in the long run, the relationship is positive Blockchain helps in managing multiple levels in a more secure way, reduces paper work and amplifies verification inefficiency.

Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.

We propose a novel theoretical approach for solving a Stochastic Security Game using augmented Markov Decison Processes and an experimental evaluation. Most of the previous works mentioned in the literature focus on Linear Programming techniques seeking Strong Stackelberg Equilibria through the defender and attacker’s strategy spaces. Although effective, these techniques are computationally expensive and tend to not scale well to very large problems. By fixing the set of the possible defense strategies, our approach is able to use the well-known augmented MDP formalism to compute an optimal policy for an attacker facing a defender patrolling. Experimental results on fully observable cases validate our approach and show good performances in comparison with optimistic and pessimistic approaches. However, these results also highlight the need of scalability improvements and of handling the partial observability cases.

Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.

Security and privacy threat modeling is commonly applied to systematically identify and address design-level security and privacy concerns in the early stages of architecture and design. Identifying and resolving these threats should remain a continuous concern during the development lifecycle. Especially with contemporary agile development practices, a single-shot upfront analysis becomes quickly outdated. Despite it being explicitly recommended by experts, existing threat modeling approaches focus largely on early development phases and provide limited support during later implementation phases.In this paper, we present an integrated threat analysis toolchain to support automated, continuous threat elicitation, assessment, and mitigation as part of a continuous integration pipeline in the GitLab DevOps platform. This type of automation allows for continuous attention to security and privacy threats during development at the level of individual commits, supports monitoring and managing the progress in addressing security and privacy threats over time, and enables more advanced and fine-grained analyses such as assessing the impact of proposed changes in different code branches or merge/pull requests by analyzing the changes to the threat model.