Biblio

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intend to process the source code directly by treating them as text. To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and using them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming. In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement Vul-CNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN can achieve better accuracy than eight state-of-the-art vul-nerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN can detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.

One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method. Traditional COA attack search methods that passively search for attacks can be difficult, especially as the network gets bigger. To address these issues, new autonomous COA techniques are being developed, and among them, an intelligent spatial algorithm is designed in this paper for efficient operations in scalable networks. On top of the spatial search, a Monte-Carlo (MC)-based temporal approach is additionally considered for taking care of time-varying network behaviors. Therefore, we propose a spatio-temporal attack COA search algorithm for scalable and time-varying networks.

The blockchain network is often considered a reliable and secure network. However, some security attacks, such as eclipse attacks, have a significant impact on blockchain networks. In order to perform an eclipse attack, the attacker must be able to control enough IP addresses. This type of attack can be mitigated by blocking incoming connections. Connected machines may only establish outbound connections to machines they trust, such as those on a whitelist that other network peers maintain. However, this technique is not scalable since the solution does not allow nodes with new incoming communications to join the network. In this paper, we propose a scalable and secure trust-based solution against eclipse attacks with a peer-selection strategy that minimizes the probability of eclipse attacks from nodes in the network by developing a trust point. Finally, we experimentally analyze the proposed solution by creating a network simulation environment. The analysis results show that the proposed solution reduces the probability of an eclipse attack and has a success rate of over 97%.

Virtual Private Networks (VPNs) have become a communication medium for accessing information, data exchange and flow of information. Many organizations require Intranet or VPN, for data access, access to servers from computers and sharing different types of data among their offices and users. A secure VPN environment is essential to the organizations to protect the information and their IT infrastructure and their assets. Every organization needs to protect their computer network environment from various malicious cyber threats. This paper presents a comprehensive network security management which includes significant strategies and protective measures during the management of a VPN in an organization. The paper also presents the procedures and necessary counter measures to preserve the security of VPN environment and also discussed few Identified Security Strategies and measures in VPN. It also briefs the Network Security and their Policies Management for implementation by covering security measures in firewall, visualized security profile, role of sandbox for securing network. In addition, a few identified security controls to strengthen the organizational security which are useful in designing a secure, efficient and scalable VPN environment, are also discussed.

Cloud computing provides customers with enormous compute power and storage capacity, allowing them to deploy their computation and data-intensive applications without having to invest in infrastructure. Many firms use cloud computing as a means of relocating and maintaining resources outside of their enterprise, regardless of the cloud server's location. However, preserving the data in cloud leads to a number of issues related to data loss, accountability, security etc. Such fears become a great barrier to the adoption of the cloud services by users. Cloud computing offers a high scale storage facility for internet users with reference to the cost based on the usage of facilities provided. Privacy protection of a user's data is considered as a challenge as the internal operations offered by the service providers cannot be accessed by the users. Hence, it becomes necessary for monitoring the usage of the client's data in cloud. In this research, we suggest an effective cloud storage solution for accessing patient medical records across hospitals in different countries while maintaining data security and integrity. In the suggested system, multifactor authentication for user login to the cloud, homomorphic encryption for data storage with integrity verification, and integrity verification have all been implemented effectively. To illustrate the efficacy of the proposed strategy, an experimental investigation was conducted.

Worldwide, societies are rapidly becoming more connected, owing primarily to the growing number of intelligent things and smart applications (e.g, smart automobiles, smart wearable devices, etc.) These have occurred in tandem with the Internet Of Things, a new method of connecting the physical and virtual worlds. It is a new promising paradigm whereby every ‘thing’ can connect to anything via the Internet. However, with IoT systems being deployed even on large-scale, security concerns arise amongst other challenges. Hence the need to allocate appropriate protection of resources. The realization of secure IoT systems could only be accomplished with a comprehensive understanding of the particular needs of a specific system. How-ever, this paradigm lacks a proper and exhaustive classification of security requirements. This paper presents an approach towards understanding and classifying the security requirements of IoT devices. This effort is expected to play a role in designing cost-efficient and purposefully secured future IoT systems. During the coming up with and the classification of the requirements, We present a variety of set-ups and define possible attacks and threats within the scope of IoT. Considering the nature of IoT and security weaknesses as manifestations of unrealized security requirements, We put together possible attacks and threats in categories, assessed the existent IoT security requirements as seen in literature, added more in accordance with the applied domain of the IoT and then classified the security requirements. An IoT system can be secure, scalable, and flexible by following the proposed security requirement classification.

A mail spoofing attack is a harmful activity that modifies the source of the mail and trick users into believing that the message originated from a trusted sender whereas the actual sender is the attacker. Based on the previous work, this paper analyzes the transmission process of an email. Our work identifies new attacks suitable for bypassing SPF, DMARC, and Mail User Agent’s protection mechanisms. We can forge much more realistic emails to penetrate the famous mail service provider like Tencent by conducting the attack. By completing a large-scale experiment on these well-known mail service providers, we find some of them are affected by the related vulnerabilities. Some of the bypass methods are different from previous work. Our work found that this potential security problem can only be effectively protected when all email service providers have a standard view of security and can configure appropriate security policies for each email delivery node. In addition, we also propose a mitigate method to defend against these attacks. We hope our work can draw the attention of email service providers and users and effectively reduce the potential risk of phishing email attacks on them.

We present the new class of non-uniform Rowhammer access patterns that bypass undocumented, proprietary in-DRAM Target Row Refresh (TRR) while operating in a production setting. We show that these patterns trigger bit flips on all 40 DDR4 DRAM devices in our test pool. We make a key observation that all published Rowhammer access patterns always hammer “aggressor” rows uniformly. While uniform accesses maximize the number of aggressor activations, we find that in-DRAM TRR exploits this behavior to catch aggressor rows and refresh neighboring “victims” before they fail. There is no reason, however, to limit Rowhammer attacks to uniform access patterns: smaller technology nodes make underlying DRAM technologies more vulnerable, and significantly fewer accesses are nowadays required to trigger bit flips, making it interesting to investigate less predictable access patterns. The search space for non-uniform access patterns, however, is tremendous. We design experiments to explore this space with respect to the deployed mitigations, highlighting the importance of the order, regularity, and intensity of accessing aggressor rows in non-uniform access patterns. We show how randomizing parameters in the frequency domain captures these aspects and use this insight in the design of Blacksmith, a scalable Rowhammer fuzzer that generates access patterns that hammer aggressor rows with different phases, frequencies, and amplitudes. Blacksmith finds complex patterns that trigger Rowhammer bit flips on all 40 of our recently purchased DDR4 DIMMs, \$2.6 \textbackslashtimes\$ more than state of the art, and generating on average \$87 \textbackslashtimes\$ more bit flips. We also demonstrate the effectiveness of these patterns on Low Power DDR4X devices. Our extensive analysis using Blacksmith further provides new insights on the properties of currently deployed TRR mitigations. We conclude that after almost a decade of research and deployed in-DRAM mitigations, we are perhaps in a worse situation than when Rowhammer was first discovered.

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

Securing communication between any two wireless devices or users is challenging without compromising sensitive/personal data. To address this problem, we have developed an artificial intelligence (AI) algorithm to secure communication on virtualized wireless networks. To detect cyberattacks in a virtualized environment is challenging compared to traditional wireless networks setting. However, we successfully investigate an efficient cyberattack detection algorithm using an AI algorithm in a Bayesian learning model for detecting cyberattacks on the fly. We have studied the results of Random Forest and deep neural network (DNN) models to detect the cyberattacks on a virtualized wireless network, having considered the required transmission power as a threshold value to classify suspicious activities in our model. We present both formal mathematical analysis and numerical results to support our claims. The numerical results show our accuracy in detecting cyberattacks in the proposed Bayesian model is better than Random Forest and DNN models. We have also compared both models in terms of detection errors. The performance comparison results show our proposed approach outperforms existing approaches in detection accuracy, precision, and recall.

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poison samples are injected into the training dataset. While these poison samples look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label poisoning attack against transfer learning, which creates poison images with their center close to the target image in the feature space. Our attack, Bullseye Polytope, improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end transfer learning, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) when crafting the poison samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without using extra poison samples.

In recent years, with the rapid development of DNN, the algorithm complexity in a series of fields such as computer vision and natural language processing is increasing rapidly. FPGA-based DNN accelerators have demonstrated superior flexibility and performance, with higher energy efficiency compared to high-performance devices such as GPU. However, the computing resources of a single FPGA are limited and it is difficult to flexibly meet the requirements of high throughput and high energy efficiency of different computing scales. Therefore, this paper proposes a DNN implementation method based on the scalable heterogeneous FPGA cluster to adapt to different tasks and achieve high throughput and energy efficiency. Firstly, the method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between multiple boards. Secondly, a task deployment method based on dichotomy is proposed to maximize the balance of task execution time of different pipeline stages to improve throughput and energy efficiency. Thirdly, optimize DNN computing module according to the relationship between computing power and bandwidth, and improve energy efficiency by reducing waste of ineffective resources and improving resource utilization. The experiment results on Alexnet and VGG-16 demonstrate that we use Zynq 7035 cluster can at most achieves ×25.23 energy efficiency of optimized AMD AIO processor. Compared with previous works of single FPGA and FPGA cluster, the energy efficiency is improved by 59.5% and 18.8%, respectively.

Fog Computing was envisioned to solve problems like high latency, mobility, bandwidth, etc. that were introduced by Cloud Computing. Fog Computing has enabled remotely connected IoT devices and sensors to be managed efficiently. Nonetheless, the Fog-Cloud paradigm suffers from various security and privacy related problems. Blockchain ensures security in a trustless way and therefore its applications in various fields are increasing rapidly. In this work, we propose a Fog-Cloud architecture that enables Blockchain to ensure security, scalability, and privacy of remotely connected IoT devices. Furthermore, our proposed architecture also efficiently manages common problems like ever-increasing latency and energy consumption that comes with the integration of Blockchain in Fog-Cloud architecture.

Because network nodes are transparent in media streaming applications, traditional networks cannot utilize the scalability feature of Scalable video coding (SVC). Compared with the traditional network, SDN supports various flows in a more fine-grained and scalable manner via the OpenFlow protocol, making QoS requirements easier and more feasible. In previous studies, a Ternary Content-Addressable Memory (TCAM) space in the switch has not been considered. This paper proposes a scalable QoS-aware multicast scheme for SVC streams, and formulates the scalable QoS-aware multicast routing problem as a nonlinear programming model. Then, we design heuristic algorithms that reduce the TCAM space consumption and construct the multicast tree for SVC layers according to video streaming requests. To alleviate video quality degradation, a dynamic layered multicast routing algorithm is proposed. Our experimental results demonstrate the performance of this method in terms of the packet loss ratio, scalability, the average satisfaction, and system utility.

We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.

The pervasive and resource-constrained nature of Internet of Things (IoT) devices makes them attractive to be targeted by different means of cyber threats. There are a vast amount of botnets being deployed every day that aim to increase their presence on the Internet for realizing malicious activities with the help of the compromised interconnected devices. Therefore, monitoring IoT networks using intrusion detection systems is one of the major countermeasures against such threats. In this work, we present a machine learning based Wi-Fi intrusion detection system developed specifically for IoT devices. We show that a single multi-class classifier, which operates on the encrypted data collected from the wireless data link layer, is able to detect the benign traffic and six types of IoT attacks with an overall accuracy of 96.85%. Our model is a scalable one since there is no need to train different classifiers for different IoT devices. We also present an alternative attack classifier that outperforms the attack classification model which has been developed in an existing study using the same dataset.

Internet of Things (IoT) is penetrating into every aspect of our personal lives including our body, our home and our living environment which poses numerous security challenges. The number of heterogeneous connected devices is increasing exponentially in IoT, which in turn increases the attack surface of IoT. This forces the need for uniform, distributed security mechanism which can efficiently detect the attack at faster rate in highly scalable IoT environment. The proposed work satisfies this requirement by providing a security framework which combines Fog computing and Software Defined Networking (SDN). The experimental results depicts the effectiveness in protecting the IoT applications at faster rate

The network security problem brought by the cloud computing has become an important issue to be dealt with in information construction. Since anomaly detection and attack detection in cloud environment need to find the vulnerability through the reverse analysis of data flow, it is of great significance to carry out the reverse analysis of unknown network protocol in the security application of cloud environment. To solve this problem, an improved mining method on bitstream protocol association rules with unknown type and format is proposed. The method combines the location information of the protocol framework to make the frequent extraction process more concise and accurate. In addition, for the frame separation problem of unknown protocol, we design a hierarchical clustering algorithm based on Jaccard distance and a frame field delimitation method based on the proximity of information entropy between bytes. The experimental results show that this technology can correctly resolve the protocol format and realize the purpose of anomaly detection in cloud computing, and ensure the security of cloud services.

IPsec is widely used for internet security because it offers confidentiality, integrity, and authenticity also protects from replay attacks. IP Security depends on numerous frameworks, organization propels, and cryptographic techniques. IPsec is a heavyweight complex security protocol suite. Because of complex architecture and implementation processes, security implementers prefer TLS. Because of complex implementation, it is impractical to manage over the IoT devices. We propose a simplified and lite version of internet security protocol implemented with only ESP. For encryption, we use AES, RAS-RLP public key cryptography.

The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.

Mining of data is used to analyze facts to discover formerly unknown patterns, classifying and grouping the records. There are several crucial scalable statistics mining platforms that have been developed in latest years. RapidMiner is a famous open source software which can be used for advanced analytics, Weka and Orange are important tools of machine learning for classifying patterns with techniques of clustering and regression, whilst Knime is often used for facts preprocessing like information extraction, transformation and loading. This article encapsulates the most important and robust platforms.

The root causes of many security vulnerabilities include a pernicious combination of two problems, often regarded as inescapable aspects of computing. First, the protection mechanisms provided by the mainstream processor architecture and C/C++ language abstractions, dating back to the 1970s and before, provide only coarse-grain virtual-memory-based protection. Second, mainstream system engineering relies almost exclusively on test-and-debug methods, with (at best) prose specifications. These methods have historically sufficed commercially for much of the computer industry, but they fail to prevent large numbers of exploitable bugs, and the security problems that this causes are becoming ever more acute.In this paper we show how more rigorous engineering methods can be applied to the development of a new security-enhanced processor architecture, with its accompanying hardware implementation and software stack. We use formal models of the complete instruction-set architecture (ISA) at the heart of the design and engineering process, both in lightweight ways that support and improve normal engineering practice - as documentation, in emulators used as a test oracle for hardware and for running software, and for test generation - and for formal verification. We formalise key intended security properties of the design, and establish that these hold with mechanised proof. This is for the same complete ISA models (complete enough to boot operating systems), without idealisation.We do this for CHERI, an architecture with hardware capabilities that supports fine-grained memory protection and scalable secure compartmentalisation, while offering a smooth adoption path for existing software. CHERI is a maturing research architecture, developed since 2010, with work now underway on an Arm industrial prototype to explore its possible adoption in mass-market commercial processors. The rigorous engineering work described here has been an integral part of its development to date, enabling more rapid and confident experimentation, and boosting confidence in the design.

The dynamicity and complexity of clouds highlight the importance of automated root cause analysis solutions for explaining what might have caused a security incident. Most existing works focus on either locating malfunctioning clouds components, e.g., switches, or tracing changes at lower abstraction levels, e.g., system calls. On the other hand, a management-level solution can provide a big picture about the root cause in a more scalable manner. In this paper, we propose DOMINOCATCHER, a novel provenance-based solution for explaining the root cause of security incidents in terms of management operations in clouds. Specifically, we first define our provenance model to capture the interdependencies between cloud management operations, virtual resources and inputs. Based on this model, we design a framework to intercept cloud management operations and to extract and prune provenance metadata. We implement DOMINOCATCHER on OpenStack platform as an attached middleware and validate its effectiveness using security incidents based on real-world attacks. We also evaluate the performance through experiments on our testbed, and the results demonstrate that DOMINOCATCHER incurs insignificant overhead and is scalable for clouds.

Cloud computing (CC) systems prevail to be the widespread computational paradigms for offering immense scalable and elastic services. Computing resources in cloud environment should be scheduled to facilitate the providers to utilize the resources moreover the users could get low cost applications. The most prominent need in job scheduling is to ensure Quality of service (QoS) to the user. In the boundary of the third party the scheduling takes place hence it is a significant condition for assuring its security. The main objective of our work is to offer QoS i.e. cost, makespan, minimized migration of task with security enforcement moreover the proposed algorithm guarantees that the admitted requests are executed without violating service level agreement (SLA). These objectives are attained by the proposed Fuzzy Ant Bee Colony algorithm. The experimental outcome confirms that secured job scheduling objective with assured QoS is attained by the proposed algorithm.

Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics' exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, and it is also scalable with low latency.