Biblio

This paper provides hardware-independent authentication named as Intelligent Authentication Scheme, which rectifies the design weaknesses that may be exploited by various security attacks. The Intelligent Authentication Scheme protects against various types of security attacks such as password-guessing attack, replay attack, streaming bots attack (denial of service), keylogger, screenlogger and phishing attack. Besides reducing the overall cost, it also balances both security and usability. It is a unique authentication scheme.

In network communication domain, one of the most widely used protocol for encrypting data and securing communications is the IPSec protocol. The design of this protocol is based on two main phases which are: exchanging keys phase and transferring data phase. In this paper we focus on enhancing the exchanging keys phase which is included in the security association (SA), using a chaotic cryptosystem. Initially IPSec is based on the Internet Key Exchange (IKE) protocol for establishing the SA. Actually IKE protocol is in charge for negotiating the connection and for authenticating both nodes. However; using IKE gives rise to a major problem related to security attack such as the Man in the Middle Attack. In this paper, we propose a chaotic cryptosystem solution to generate SA file for the connected nodes of the network. By solving a 4-Dimension chaotic system, a SA file that includes 128-bit keys will be established. The proposed solution is implemented and tested using FPGA boards.

This paper is concerned with the stabilization problem of cyber physical system (CPS) exposed to a random replay attack. The study will ignore the effects of communication delays and packet losses, and the attention will be focused on the effect of replay attack on the stability of (CPS). The closed-loop system is modeled as Markovian jump linear system with two jumping parameters. Linear matrix inequality (LMI) formulation is used to give a condition for stochastic stabilization of the system. Finally the theory is illustrated through a numerical example.

Service Function Chaining (SFC) provides a special capability that defines an ordered list of network services as a virtual chain and makes a network more flexible and manageable. However, SFC is vulnerable to various attacks caused by compromised switches, especially the middlebox-bypass attack. In this paper, we propose a system that can detect not only middlebox-bypass attacks but also other incorrect forwarding actions by compromised switches. The existing solutions to protect SFC against compromised switches and middlebox-bypass attacks can only solve individual problems. The proposed system uses both probe-based and statistics-based methods to check the probe packets with random pre-assigned keys and collect statistics from middleboxes for detecting any abnormal actions in SFC. It is shown that the proposed system takes only 0.08 ms for the packet processing while it prevents SFC from the middlebox-bypass attacks and compromised switches, which is the negligible delay.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.

Twitter being the most popular content sharing platform is giving rise to automated accounts called “bots”. Majority of the users on Twitter are bots. Various machine learning (ML) algorithms are designed to detect bots avoiding the vulnerability constraints of ML-based models. This paper contributes to exploit vulnerabilities of machine learning (ML) algorithms through black-box attack. An adversarial text sequence misclassifies the results of deep learning (DL) classifiers for bot detection. Literature shows that ML models are vulnerable to attacks. The aim of this paper is to compromise the accuracy of ML-based bot detection algorithms by replacing original words in tweets with their synonyms. Our results show 7.2% decrease in the accuracy for bot tweets, therefore classifying bot tweets as legitimate tweets.

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.

Since Bitcoin, the first cryptocurrency that applied blockchain technology was developed by Satoshi Nakamoto, the cryptocurrency market has grown rapidly. Along with this growth, many vulnerabilities and attacks are threatening the Bitcoin ecosystem, which is not only at the bitcoin network-level but also at the service level that applied it, according to the survey. We intend to analyze and detect DDoS attacks on the premise that bitcoin's network-level data and service-level DDoS attacks with bitcoin are associated. We evaluate the results of the experiment according to the proposed metrics, resulting in an association between network-level data and service-level DDoS attacks of bitcoin. In conclusion, we suggest the possibility that the proposed method could be applied to other blockchain systems.

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

Consider the problem of guessing a random vector X by submitting queries (guesses) of the form "Is X equal to x?" until an affirmative answer is obtained. A key figure of merit is the number of queries required until the right vector is guessed, termed the guesswork. The goal is to devise a guessing strategy which minimizes a certain guesswork moment. We study a universal, decentralized scenario where the guesser does not know the distribution of X, and is not allowed to prepare a list of words to be guessed in advance, or to remember its past guesses. Such a scenario is useful, for example, if bots within a Botnet carry out a brute-force attack to guess a password or decrypt a message, yet cannot coordinate the guesses or even know how many bots actually participate in the attack. We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them to finite-state sources. For both, we derive the guessing exponent and prove its asymptotic optimality by deriving a matching converse. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information (SI). Finally, we design simple algorithms for sampling from the universal distributions.

This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.

Detection of network attacks is the first step to network security. Many different methods for attack detection were proposed in the past. However, descriptions of these methods are often not complete and it is difficult to verify that the actual implementation matches the description. In this demo paper, we propose to use Complex Event Processing (CEP) for developing detection methods based on network flows. By writing the detection methods in an Event Processing Language (EPL), we can address the above-mentioned problems. The SQL-like syntax of most EPLs is easily readable so the detection method is self-documented. Moreover, it is directly executable in the CEP system, which eliminates inconsistencies between documentation and implementation. The demo will show a running example of a multi-stage HTTP brute force attack detection using Esper and its EPL.

Living in the age of digital transformation, companies and individuals are moving to public and private clouds to store and retrieve information, hence the need to store and retrieve data is exponentially increasing. Existing storage technologies such as DAS are facing a big challenge to deal with these huge amount of data. Hence, newer technologies should be adopted. Storage Area Network (SAN) is a distributed storage technology that aggregates data from several private nodes into a centralized secure place. Looking at SAN from a security perspective, clearly physical security over multiple geographical remote locations is not adequate to ensure a full security solution. A SAN security framework needs to be developed and designed. This work investigates how SAN protocols work (FC, ISCSI, FCOE). It also investigates about other storages technologies such as Network Attached Storage (NAS) and Direct Attached Storage (DAS) including different metrics such as: IOPS (input output per second), Throughput, Bandwidths, latency, cashing technologies. This research work is focusing on the security vulnerabilities in SAN listing different attacks in SAN protocols and compare it to other such as NAS and DAS. Another aspect of this work is to highlight performance factors in SAN in order to find a way to improve the performance focusing security solutions aimed to enhance the security level in SAN.

Software Defined Networking (SDN) provides new functionalities to efficiently manage the network traffic, which can be used to enhance the networking capabilities to support the growing communication demands today. But at the same time, it introduces new attack vectors that can be exploited by attackers. Hence, evaluating and selecting countermeasures to optimize the security of the SDN is of paramount importance. However, one should also take into account the trade-off between security and performance of the SDN. In this paper, we present a security optimization approach for the SDN taking into account the trade-off between security and performance. We evaluate the security of the SDN using graphical security models and metrics, and use queuing models to measure the performance of the SDN. Further, we use Genetic Algorithms, namely NSGA-II, to optimally select the countermeasure with performance and security constraints. Our experimental analysis results show that the proposed approach can efficiently compute the countermeasures that will optimize the security of the SDN while satisfying the performance constraints.

An improved algorithm of the Analytic Hierarchy Process (AHP) is proposed in this paper, which is realized by constructing an improved judgment matrix. Specifically, rough set theory is used in the algorithm to calculate the weight of the network metric data, and then the improved AHP algorithm nine-point systemic is structured, finally, an improved AHP judgment matrix is constructed. By performing an AHP operation on the improved judgment matrix, the weight of the improved network metric data can be obtained. If only the rough set theory is applied to process the network index data, the objective factors would dominate the whole process. If the improved algorithm of AHP is used to integrate the expert score into the process of measurement, then the combination of subjective factors and objective factors can be realized. Based on the aforementioned theory, a new network attack metrics system is proposed in this paper, which uses a metric structure based on "attack type-attack attribute-attack atomic operation-attack metrics", in which the metric process of attack attribute adopts AHP. The metrics of the system are comprehensive, given their judgment of frequent attacks is universal. The experiment was verified by an experiment of a common attack Smurf. The experimental results show the effectiveness and applicability of the proposed measurement system.

Severe class imbalance between the majority and minority classes in large datasets can prejudice Machine Learning classifiers toward the majority class. Our work uniquely consolidates two case studies, each utilizing three learners implemented within an Apache Spark framework, six sampling methods, and five sampling distribution ratios to analyze the effect of severe class imbalance on big data analytics. We use three performance metrics to evaluate this study: Area Under the Receiver Operating Characteristic Curve, Area Under the Precision-Recall Curve, and Geometric Mean. In the first case study, models were trained on one dataset (POST) and tested on another (SlowlorisBig). In the second case study, the training and testing dataset roles were switched. Our comparison of performance metrics shows that Area Under the Precision-Recall Curve and Geometric Mean are sensitive to changes in the sampling distribution ratio, whereas Area Under the Receiver Operating Characteristic Curve is relatively unaffected. In addition, we demonstrate that when comparing sampling methods, borderline-SMOTE2 outperforms the other methods in the first case study, and Random Undersampling is the top performer in the second case study.

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as × 6) can be achieved by parallelizing off-the-shelf stream learning approaches.

In the open network environment, the network offensive information is implanted in big data environment, so it is necessary to carry out accurate location marking of network offensive information, to realize network attack detection, and to implement the process of accurate location marking of network offensive information. Combined with big data analysis method, the location of network attack nodes is realized, but when network attacks cross in series, the performance of attack information tagging is not good. An accurate marking technique for network attack information is proposed based on big data fusion tracking recognition. The adaptive learning model combined with big data is used to mark and sample the network attack information, and the feature analysis model of attack information chain is designed by extracting the association rules. This paper classifies the data types of the network attack nodes, and improves the network attack detection ability by the task scheduling method of the network attack information nodes, and realizes the accurate marking of the network attacking information. Simulation results show that the proposed algorithm can effectively improve the accuracy of marking offensive information in open network environment, the efficiency of attack detection and the ability of intrusion prevention is improved, and it has good application value in the field of network security defense.

A cyber attack is a malicious and deliberate attempt by an individual or organization to breach the integrity, confidentiality, and/or availability of data or services of an information system of another individual or organization. Being able to attribute a cyber attack is a crucial question for security but this question is also known to be a difficult problem. The main reason why there is currently no solution that automatically identifies the initiator of an attack is that attackers usually use proxies, i.e. an intermediate node that relays a host over the network. In this paper, we propose to formalize the problem of identifying the initiator of a cyber attack. We show that if the attack scenario used by the attacker is known, then we are able to resolve the cyber attribution problem. Indeed, we propose a model to formalize these attack scenarios, that we call attack patterns, and give an efficient algorithm to search for attack pattern on a communication history. Finally, we experimentally show the relevance of our approach.

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

The quantity of Internet of Things (IoT) devices in the marketplace and lack of security is staggering. The interconnectedness of IoT devices has increased the attack surface for hackers. "White Worm" technology has the potential to combat infiltrating malware. Before white worm technology becomes viable, its capabilities must be constrained to specific devices and limited to non-harmful actions. This paper addresses the current problem, international research, and the conflicting interest of individuals, businesses, and governments regarding white worm technology. Proposed is a new perspective on utilizing white worm technology to protect the vulnerability of IoT devices, while overcoming its challenges.

OpenFlow has been the main standard protocol of software defined networking (SDN) since the launch of this new networking paradigm. It is a programmable network protocol that controls traffic flows among switches and routers regardless of their platforms. Its security relies on the optional implementation of Transport Layer Security (TLS) which has been proven vulnerable. The aim of this research was to develop a secured OpenFlow, so-called Secured-OF. A stateful firewall was used to store state information for further analysis. Dynamic Bayesian Network (DBN) was used to learn denial-of-service attack and distributed denial-of-service attack. It analyzes packet states to determine the nature of an attack and adds that piece of information to the flow table entry. The proposed Secured-OF model in Ryu controller was evaluated with several performance metrics. The analytical evaluation of the proposed Secured-OF scheme was performed on an emulated network. The results showed that the proposed Secured-OF scheme offers a high attack detection accuracy at 99.5%. In conclusion, it was able to improve the security of the OpenFlow controller dramatically with trivial performance degradation compared to an SDN with no security implementation.

To improve production efficiency, more industrial control systems are connected to IT networks, and more IT technologies are applied to industrial control networks, network security has become an important problem. Industrial control network security analysis and decision-making is a effective method to solve the problem, which can predict risks and support to make decisions before the actual fault of the industrial control network system has not occurred. This paper proposes a security analysis and decision-making method with forward reasoning based on strong relevant logic for industrial control networks. The paper presents a case study in security analysis and decision-making for industrial control networks. The result of the case study shows that the proposed method is effective.

In this paper we propose a responsive autonomic and data-driven adaptive virtual networking framework (RAvN) that integrates the adaptive reconfigurable features of a popular SDN platform called open networking operating system (ONOS), the network performance statistics provided by traffic monitoring tools such as T-shark or sflow-RT and analytics and decision making skills provided from new and current machine learning techniques to detect and mitigate anomalous behavior. For this paper we focus on the development of novel detection schemes using a developed Centroid-based clustering technique and the Intragroup variance of data features within network traffic (C. Intra), with a multivariate gaussian distribution model fitted to the constant changes in the IP addresses of the network to accurately assist in the detection of low rate and high rate denial of service (DoS) attacks. We briefly discuss our ideas on the development of the decision-making and execution component using the concept of generating adaptive policy updates (i.e. anomalous mitigation solutions) on-the-fly to the ONOS SDN controller for updating network configurations and flows. In addition we provide the analysis on anomaly detection schemes used for detecting low rate and high rate DoS attacks versus a commonly used unsupervised machine learning technique Kmeans. The proposed schemes outperformed Kmeans significantly. The multivariate clustering method and the intragroup variance recorded 80.54% and 96.13% accuracy respectively while Kmeans recorded 72.38% accuracy.