Biblio

Now a days, Cloud computing has brought a unbelievable change in companies, organizations, firm and institutions etc. IT industries is advantage with low investment in infrastructure and maintenance with the growth of cloud computing. The Virtualization technique is examine as the big thing in cloud computing. Even though, cloud computing has more benefits; the disadvantage of the cloud computing environment is ensuring security. Security means, the Cloud Service Provider to ensure the basic integrity, availability, privacy, confidentiality, authentication and authorization in data storage, virtual machine security etc. In this paper, we presented a Local outlier factors mechanism, which may be helpful for the detection of Distributed Denial of Service attack in a cloud computing environment. As DDoS attack becomes strong with the passing of time, and then the attack may be reduced, if it is detected at first. So we fully focused on detecting DDoS attack to secure the cloud environment. In addition, our scheme is able to identify their possible sources, giving important clues for cloud computing administrators to spot the outliers. By using WEKA (Waikato Environment for Knowledge Analysis) we have analyzed our scheme with other clustering algorithm on the basis of higher detection rates and lower false alarm rate. DR-LOF would serve as a better DDoS detection tool, which helps to improve security framework in cloud computing.

In this paper, we highlight and study the threat arising from the unattended wearable devices pre-paired with a smartphone over a wireless communication medium. Most users may not lock their wearables due to their small form factor, and may strip themselves off of these devices often, leaving or forgetting them unattended while away from homes (or shared office spaces). An “insider” attacker (potentially a disgruntled friend, roommate, colleague, or even a spouse) can therefore get hold of the wearable, take it near the user's phone (i.e., within radio communication range) at another location (e.g., user's office), and surreptitiously use it across physical barriers for various nefarious purposes, including pulling and learning sensitive information from the phone (such as messages, photos or emails), and pushing sensitive commands to the phone (such as making phone calls, sending text messages and taking pictures). The attacker can then safely restore the wearable, wait for it to be left unattended again and may repeat the process for maximum impact, while the victim remains completely oblivious to the ongoing attack activity. This malicious behavior is in sharp contrast to the threat of stolen wearables where the victim would unpair the wearable as soon as the theft is detected. Considering the severity of this threat, we also respond by building a defense based on audio proximity, which limits the wearable to interface with the phone only when it can pick up on an active audio challenge produced by the phone.

The data transmitted from the wearable device commonly includes sensitive data. So, application service using the data collected from the unauthorized wearable devices can cause serious problems. Also, it is important to authenticate any wearable device and then, protect the transmitted data between the wearable devices and the application server. In this paper, we propose an authentication protocol, which is designed by using the Transport Layer Security (TLS) handshake protocol combined with a mobile authentication proxy. By using the proposed authentication protocol, we can authenticate the wearable device. And we can secure data transmission since session key is shared between the wearable device and the application server. In addition, the proposed authentication protocol is secure even when the mobile authentication proxy is unreliable.

This paper proposes DeepAuth, an in-situ authentication framework that leverages the unique motion patterns when users entering passwords as behavioural biometrics. It uses a deep recurrent neural network to capture the subtle motion signatures during password input, and employs a novel loss function to learn deep feature representations that are robust to noise, unseen passwords, and malicious imposters even with limited training data. DeepAuth is by design optimised for resource constrained platforms, and uses a novel split-RNN architecture to slim inference down to run in real-time on off-the-shelf smartwatches. Extensive experiments with real-world data show that DeepAuth outperforms the state-of-the-art significantly in both authentication performance and cost, offering real-time authentication on a variety of smartwatches.

Wireless mesh networking (WMN) is a new technology aimed to introduce the benefits of using multi-hop and multi-path to the wireless world. However, the absence of a fast and reliable handoff protocol is a major drawback especially in a technology designed to feature high mobility and scalability. We propose a fast and efficient handoff authentication protocol for wireless mesh networks. It is a token-based authentication protocol using pre-distributed parameters. We provide a performance comparison among our protocol, UFAP, and other protocols including EAP-TLS and EAP-PEAP tested in an actual setup. Performance analysis will prove that our proposed handoff authentication protocol is 250 times faster than EAP-PEAP and 500 times faster than EAP-TLS. The significant improvement in performance allows UFAP to provide seamless handoff and continuous operation even for real-time applications which can only tolerate short delays under 50 ms.

Because major smartphone platforms are equipped with Bluetooth Low Energy (BLE) capabilities, more and more smart devices have adopted BLE technologies to communicate with smartphones. In order to support the mesh topology in BLE networks, several proposals have been designed. Among them, the Bluetooth Special Interest Group (SIG) recently released a specification for Bluetooth mesh networks based upon BLE technology. This paper focuses on this standard solution and analyses its security protocol with hardware security in mind. As it is expected that internet of things (IoT) devices will be deployed everywhere, the risk of physical attacks must be assessed. First, we provide a comprehensive survey of the security features involved in Bluetooth mesh. Then, we introduce some physical attacks identified as serious threats for the IoT and discuss their relevance in the case of Bluetooth mesh networks. Finally, we briefly discuss possible countermeasures to reach a secure implementation.

Edge computing can potentially play a crucial role in enabling user authentication and monitoring through context-aware biometrics in military/battlefield applications. For example, in Internet of Military Things (IoMT) or Internet of Battlefield Things (IoBT),an increasing number of ubiquitous sensing and computing devices worn by military personnel and embedded within military equipment (combat suit, instrumented helmets, weapon systems, etc.) are capable of acquiring a variety of static and dynamic biometrics (e.g., face, iris, periocular, fingerprints, heart-rate, gait, gestures, and facial expressions). Such devices may also be capable of collecting operational context data. These data collectively can be used to perform context-adaptive authentication in-the-wild and continuous monitoring of soldier's psychophysical condition in a dedicated edge computing architecture.

Ultra-dense Networks are attracting significant interest due to their ability to provide the next generation 5G cellular networks with a high data rate, low delay, and seamless coverage. Several factors, such as interferences, energy constraints, and backhaul bottlenecks may limit wireless networks densification. In this paper, we study the effect of mobile node densification, access node densification, and their aggregation into virtual entities, referred to as virtual cells, on location privacy. Simulations show that the number of tracked mobile nodes might be statistically reduced up to 10 percent by implementing virtual cells. Moreover, experiments highlight that success of tracking attacks has an inverse relationship to the number of moving nodes. The present paper is a preliminary attempt to analyse the effectiveness of cell virtualization to mitigate location privacy threats in ultra-dense networks.

Many network intrusion detection systems use byte sequences to detect lateral movements that exploit remote vulnerabilities. Attackers bypass such detection by stealing valid credentials and using them to transmit from one computer to another without creating abnormal network traffic. We call this method Credential-based Lateral Movement. To detect this type of lateral movement, we develop the concept of a Network Login Structure that specifies normal logins within a given network. Our method models a network login structure by automatically extracting a collection of login patterns by using a variation of the market-basket algorithm. We then employ an anomaly detection approach to detect malicious logins that are inconsistent with the enterprise network's login structure. Evaluations show that the proposed method is able to detect malicious logins in a real setting. In a simulated attack, our system was able to detect 82% of malicious logins, with a 0.3% false positive rate. We used a real dataset of millions of logins over the course of five months within a global financial company for evaluation of this work.

SQL Injection is one of the most critical security vulnerability in web applications. Most web applications use SQL as web applications. SQL injection mainly affects these websites and web applications. An attacker can easily bypass a web applications authentication and authorization and get access to the contents they want by SQL injection. This unauthorised access helps the attacker to retrieve confidential data's, trade secrets and can even delete or modify valuable documents. Even though, to an extend many preventive measures are found, till now there are no complete solution for this problem. Hence, from the surveys and analyses done, an enhanced methodology is proposed against SQL injection disclosure and deterrence by ensuring proper authentication using Heisenberg analysis and password security using Honey pot mechanism.

The evolution of cloud gaming systems is substantially the security requirements for computer games. Although online game development often utilizes artificial intelligence and human computer interaction, game developers and providers often do not pay much attention to security techniques. In cloud gaming, location-based games are augmented reality games which take the original principals of the game and applies them to the real world. In other terms, it uses the real world to impact the game experience. Because the execution of such games is distributed in cloud computing, users cannot be certain where their input and output data are managed. This introduces the possibility to input incorrect data in the exchange between the gamer's terminal and the gaming platform. In this context, we propose a new gaming concept for augmented reality and location-based games in order to solve the aforementioned cheating scenario problem. The merit of our approach is to establish an accurate and verifiable proof that the gamer reached the goal or found the target. The major novelty in our method is that it allows the gamer to submit an authenticated proof related to the game result without altering the privacy of positioning data.

Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.

The Internet of Things (IoT) devices have expanded into many aspects of everyday life. As these smart home devices grow more popular, security concerns increase. Researchers have modeled the privacy and security threats for smart home devices, but have yet to fully address the problem of unintended user access within the home. Often, smart home devices are purchased by one of the family members and associated with the same family member's account, yet are shared by the entire home. Currently most devices implement a course-grained access control model where someone in the home either has complete access or no access. We provide scenarios that highlight the need for exible authorization control and seamless authentication in IoT devices, especially in multi-user environments. We present design recommendations for IoT device manufacturers to provide fine-grained access control and authentication and describe the challenges to meeting the expectations of all users within a home.

As one of the most commonly used protocols in VPN technology, IPsec has many advantages. However, certain difficulties are posed to the audit work by the protection of in-formation. In this paper, we propose an audit method via man-in-the-middle mechanism, and design a prototype system with DPDK technology. Experiments are implemented in an IPv4 network environment, using default configuration of IPsec VPN configured with known PSK, on operating systems such as windows 7, windows 10, Android and iOS. Experimental results show that the prototype system can obtain the effect of content auditing well without affecting the normal communication between IPsec VPN users.

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem.

In the near future, vehicular cloud will help to improve traffic safety and efficiency. Unfortunately, a computing of vehicular cloud and fog cloud faced a set of challenges in security, authentication, privacy, confidentiality and detection of misbehaving vehicles. In addition to, there is a need to recognize false messages from received messages in VANETs during moving on the road. In this work, the security issues and challenges for computing in the vehicular cloud over for computing is studied.

Fog computing is a new paradigm which extends cloud computing services into the edge of the network. Indeed, it aims to pool edge resources in order to deal with cloud's shortcomings such as latency problems. However, this proposal does not ensure the honesty and the good behavior of edge devices. Thus, security places itself as an important challenge in front of this new proposal. Authentication is the entry point of any security system, which makes it an important security service. Traditional authentication schemes endure latency issues and some of them do not satisfy fog-computing requirements such as mutual authentication between end devices and fog servers. Thus, new authentication protocols need to be implemented. In this paper, we propose a new efficient authentication scheme for fog computing architecture. Our scheme ensures mutual authentication and remedies to fog servers' misbehaviors. Moreover, fog servers need to hold only a couple of information to verify the authenticity of every user in the system. Thus, it provides a low overhead in terms of storage capacity. Finally, we show through experimentation the efficiency of our scheme.

As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT system. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to address the security challenges in the IoT. The proposed FOCUS system leverages the virtual private network (VPN) to secure the access channel to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against distributed denial of service (DDoS) attacks, which can further enhance the security of the IoT system. FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast and efficient protection. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a very low response latency.

While vehicle to everything (V2X) communication enables safety-critical automotive control systems to better support various connected services to improve safety and convenience of drivers, they also allow automotive attack surfaces to increase dynamically in modern vehicles. Many researchers as well as hackers have already demonstrated that they can take remote control of the targeted car by exploiting the vulnerabilities of in-vehicle networks such as Controller Area Networks (CANs). For assuring CAN security, we focus on how to authenticate electronic control units (ECUs) in real-time by addressing the security challenges of in-vehicle networks. In this paper, we propose a novel and lightweight authentication protocol with an attack-resilient tree algorithm, which is based on one-way hash chain. The protocol can be easily deployed in CAN by performing a firmware update of ECU. We have shown analytically that the protocol achieves a high level of security. In addition, the performance of the proposed protocol is validated on CANoe simulator for virtual ECUs and Freescale S12XF used in real vehicles. The results show that our protocol is more efficient than other authentication protocol in terms of authentication time, response time, and service delay.

Modern vehicles may contain a considerable number of ECUs (Electronic Control Units) which are connected through various means of communication, with the CAN (Controller Area Network) protocol being the most widely used. However, several vulnerabilities such as the lack of authentication and the lack of data encryption have been pointed out by several authors, which ultimately render vehicles unsafe to their users and surroundings. Moreover, the lack of security in modern automobiles has been studied and analyzed by other researchers as well as several reports about modern car hacking have (already) been published. The contribution of this work aimed to analyze and test the level of security and how resilient is the CAN protocol by taking a BMW E90 (3-series) instrument cluster as a sample for a proof of concept study. This investigation was carried out by building and developing a rogue device using cheap commercially available components while being connected to the same CAN-Bus as a man in the middle device in order to send spoofed messages to the instrument cluster.

The Controller Area Network (CAN) is a broadcast communications network invented by Robert Bosch GmbH in 1986. CAN is the standard communication network found in automobiles, industry equipment, and many space applications. To be used in these environments, CAN is designed for efficiency and reliability, rather than security. This research paper closely examines the security risks within the CAN protocol and proposes a feasible solution. In this research, we investigate the problems with implementing certain security features in the CAN protocol, such as message authentication and protections against replay and denial-of-service (DoS) attacks. We identify the restrictions of the CAN bus, and we demonstrate how our proposed implementation meets these restrictions. Many previously proposed solutions lack security, feasibility, and/or efficiency; however, a solution must not drastically hinder the real-time operation speed of the network. The solution proposed in this research is tested with a simulative CAN environment. This paper proposes an alteration to the standard CAN bus nodes and the CAN protocol to better protect automobiles and other CAN-related systems from attacks.

The Controller Area Network (CAN) is a widely used industry-standard intra-vehicle broadcast network that connects the Electronic Control Units (ECUs) which control most car systems. The CAN contains substantial vulnerabilities that can be exploited by attackers to gain control of the vehicle, due to its lack of security measures. To prevent an attacker from sending malicious messages through the CAN bus to take over a vehicle, we propose the addition of a secure hardware-based module, or Security ECU (SECU), onto the CAN bus. The SECU can perform key distribution and message verification, as well as corrupting malicious messages before they are fully received by an ECU. Only software modification is needed for existing ECUs, without changing the CAN protocol. This provides backward compatibility with existing CAN systems. Furthermore, we collect 6.673 million CAN bus messages from various cars, and find that the CAN messages collectively have low entropy, with an average of 11.915 bits. This finding motivates our proposal for CAN bus message compression, which allows us to significantly reduce message size to fit the message and its message authentication code (MAC) within one CAN frame, enabling fast authentication. Since ECUs only need to generate the MACs (and not verify them), the delay and computation overhead are also reduced compared to traditional authentication mechanisms. Our authentication mechanism is implemented on a realistic testbed using industry standard MCP2551 CAN transceivers and Raspberry Pi embedded systems. Experimental results demonstrate that our mechanism can achieve real-time message authentication on the CAN bus with minimal latency.

Computing the probability of vulnerability exploitation in Bayesian attack graphs (BAGs) is a key process for the network security assessment. The conditional probability of vulnerability exploitation could be obtained from the exploitability of the NIST's Common Vulnerability Scoring System (CVSS). However, the method which N. Poolsappasit et al. proposed for computing conditional probability could be used only in the CVSS metric version v2.0, and can't be used in other two versions. In this paper, we present two methods for computing the conditional probability based on CVSS's other two metric versions, version 1.0 and version 3.0, respectively. Based on the CVSS, the conditional probability computation of vulnerability exploitation is complete by combining the method of N. Poolsappasit et al.

Secure logging as an indispensable part of any secure system in practice is well-understood by both academia and industry. However, providing security for audit logs on an untrusted machine in a large distributed system is still a challenging task. The emergence and wide availability of log management tools prompted plenty of work in the security community that allows clients or auditors to verify integrity of the log data. Most recent solutions to this problem focus on the space-efficiency or public verifiability of forward security. Unfortunately, existing secure audit logging schemes have significant performance limitations that make them impractical for realtime large-scale distributed applications: Existing cryptographic hashing is computationally expensive for logging in task intensive or resource-constrained systems especially to prove individual log events, while Merkle-tree approach has fundamental limitations when face with highly concurrent, large-scale log streams due to its serially appending feature. The verification step of Merkle-tree based approach requiring a logarithmic number of hash computations is becoming a bottleneck to improve the overall performance. There is a huge gap between the flux of log streams collected and the computational efficiency of integrity verification in the large-scale distributed systems. In this work, we develop a novel scheme, performance of which favorably compares with the existing solutions. The performance guarantees that we achieve stem from a novel data structure called concurrent authenticated tree, which allows log events concurrently appending and removes the need to wait for append operations to complete sequentially. We implement a prototype using chameleon hashing based on discrete log and Merkle history tree. A comprehensive experimental evaluation of the proposed and existing approaches is used to validate the analytical models and verify our claims. The results demonstrate that our proposed scheme verifying in a concurrent way is significantly more efficient than the previous tree-based approach.

We propose an approach to enforce security in disruption- and delay-tolerant networks (DTNs) where long delays, high packet drop rates, unavailability of central trusted entity etc. make traditional approaches unfeasible. We use trust model based on subjective logic to continuously evaluate trustworthiness of security credentials issued in distributed manner by network participants to deal with absence of centralised trusted authorities.