Biblio

A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.

In many-cores based on Network-on-Chip (NoC), several applications execute simultaneously, sharing computation, communication and memory resources. This resource sharing leads to security and trust problems. Hardware Trojans (HTs) may steal sensitive information, degrade system performance, and in extreme cases, induce physical damages. Methods available in the literature to prevent attacks include firewalls, denial-of-service detection, dedicated routing algorithms, cryptography, task migration, and secure zones. The goal of this paper is to add an HT in an NoC, able to execute three types of attacks: packet duplication, block applications, and misrouting. The paper qualitatively evaluates the attacks' effect against methods available in the literature, and its effects showed in an NoC-based many-core. The resulting system is an open-source NoC-based many-core for researchers to evaluate new methods against HT attacks.

The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.

Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.

This paper describes a realisation of a ResNet face recognition method through Zigbee-based wireless protocol. The system uses a CC2530 Zigbee-based radio frequency chip with connected VC0706 camera on it. The Arduino Nano had been used for organisation of data compression and effective division of Zigbee packets. The proposed solution also simplifies a data transmission within a strict bandwidth of Zigbee protocol and reliable packet forwarding in case of frequency distortion. The following investigation model uses Raspberry Pi 3 with connected Zigbee End Device (ZED) for successful receiving of important images and acceleration of deep learning interfaces. The model is integrated into a smart security system based on Zigbee modules, MySQL database, Android application and works in the background by using daemons procedures. To protect data, all wireless connections had been encrypted by the 128-bit Advanced Encryption Standard (AES-128) algorithm. Experimental results show a possibility to implement complex systems under restricted requirements of available transmission protocols.

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.

Many detection approaches have been proposed to address growing threat of Distributed Denial of Service (DDoS) attacks on the Internet. The attack detection is the initial step in most of the mitigation systems. This study examined the methods used to detect DDoS attacks with the focus on learning based approaches. These approaches were compared based on their efficiency, operating load and scalability. Finally, it is discussed in details.

Existing Lipschitz-based provable defences to adversarial examples only cover the L2 threat model. We introduce the first bound that makes use of Lipschitz continuity to provide a more general guarantee for threat models based on any Lp norm. Additionally, a new strategy is proposed for designing network architectures that exhibit superior provable adversarial robustness over conventional convolutional neural networks. Experiments are conducted to validate our theoretical contributions, show that the assumptions made during the design of our novel architecture hold in practice, and quantify the empirical robustness of several Lipschitz-based adversarial defence methods.

This paper presents an overhead analysis of the use of digital signature mechanisms in the Message Queue Telemetry Transport (MQTT) protocol for three classes of constrained-device. Because the resources provided by constrained-devices are very limited, the purpose of this overhead analysis is to help find out the advantages and disadvantages of each class of constrained-devices after a security mechanism has been applied, namely by applying a digital signature mechanism. The objective of using this digital signature mechanism is for providing integrity, that if the payload sent and received in its destination is still original and not changed during the transmission process. The overhead analysis aspects performed are including analyzing decryption time, signature verification performance, message delivery time, memory and flash usage in the three classes of constrained-device. Based on the overhead analysis result, it can be seen that for decryption time and signature verification performance, the Class-2 device is the fastest one. For message delivery time, the smallest time needed for receiving the payload is Class-l device. For memory usage, the Class-2 device is providing the biggest available memory and flash.

Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.

Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.

Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.

Password authentication is the most widely used authentication method in information systems. The traditional proactive password detection method is generally implemented by counting password length, character class number and computing password information entropy to improve password security. However, passwords that pass proactive password detection do not represent that they are secure. In this paper, based on the research of the characteristics of password distribution under big data, we propose an online password guessing method, which collects a dataset of guessing passwords composed of weak passwords, high frequency passwords and personal information related passwords. It is used to guess the 13k password dataset leaked in China's largest ticketing website, China Railways 12306 website. The experimental results show that even if our guess object has passed the strict proactive password detection, we can construct a guessing password dataset contain only 100 passwords, and effectively guess 4.84% of the passwords.

With the disconnected nature of some Automatic Test Systems, there is no possibility for a centralized infrastructure of sense and response in Cybersecurity. For scalability, a cost effective onboard approach will be necessary. In smaller companies where connectivity is not a concern, costly commercial solutions will impede the implementation of surveillance and compliance options. In this paper we propose to demonstrate an open source strategy using freely available Security Technical Implementation Guidelines (STIGs), internet resources, and supporting software stacks, such as OpenScap, HubbleStack, and (ElasticSearch, Logstash, and Kibana (ElasticStack)) to deliver an affordable solution to this problem. OpenScap will provide tools for managing system security and standards compliance. HubbleStack will be employed to automate compliance via its components: NOVA (an auditing engine), Nebula (osquery integration), Pulsar (event system) and Quasar (reporting system). Our intention is utilize NOVA in conjunction with OpenScap to CVE (Common Vulnerabilities and Exposures) scan and netstat for open ports and processes. Additionally we will monitor services and status, firewall settings, and use Nebula's integration of Facebook's osquery to detect vulnerabilities by querying the Operating System. Separately we plan to use Pulsar, a fast file integrity manger, to monitor the integrity of critical files such as system, test, and Hardware Abstraction Layer (HAL) software to ensure the system retains its integrity. All of this will be reported by Quasar, HubbleStack's reporting engine. We will provide situational awareness through the use of the open source Elastic Stack. ElasticSearch is a RESTful search and analytics engine. Logstash is an open source data processing pipeline that enables the ingestion of data from multiple sources sending it through extensible interfaces, in this case ElasticSearch. Kibana supports the visualization of data. Essentially Elastic Stack will be the presentation layer, HubbleStack will be the broker of the data to Elastic Stash, with the other HubbleStack components feeding that data. All of the tools involved are open source in nature, reducing the cost to the overhead required to keep configurations up to date, training on use, and analytics required to review the outputs.

The confinement of light in three dimensions (3D) is an active research topic in Nanophotonics, since it allows for ultimate control over photons [1]. A powerful tool to this end is a 3D photonic band gap crystal with a tailored defect that acts as a cavity or even a waveguide [2]. When a one-dimensional array of cavities is coupled, an intricate waveguiding system appears, known as a CROW (coupled resonator optical waveguide) [3]. Remarkably, 3D superlattices of coupled cavities that resonate inside a 3D band gap have not been studied to date. Recently, theoretical work has predicted the occurrence of "Cartesian light", wherein light propagates by hopping only in high symmetry directions in space [4]. This represents the optical analog of the Anderson model for spins or electrons that is relevant for neuromorphic computing and may lead to intricate lasing [5].

As a collection of innovative technologies, blockchain has solved the problem of reliable transmission and exchange of information on untrusted networks. The underlying implementation is the basis for the reliability of blockchain, which consists of various cryptographic algorithms for the use of identity authentication and privacy protection of distributed ledgers. The cryptographic algorithm plays a vital role in the blockchain, which guarantees the confidentiality, integrity, verifiability and non-repudiation of the blockchain. In order to get the most suitable cryptographic algorithm for the blockchain system, this paper proposed a method using Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and score the comprehensive performance of the three types of cryptographic algorithms applied in the blockchain, including symmetric cryptographic algorithms, asymmetric cryptographic algorithms and hash algorithms. This paper weighs the performance differences of cryptographic algorithms considering the aspects of security, operational efficiency, language and hardware support and resource consumption. Finally, three cryptographic algorithms are selected that are considered to be the most suitable ones for block-chain systems, namely ECDSA, sha256 and AES. This result is also consistent with the most commonly used cryptographic algorithms in the current blockchain development direction. Therefore, the reliability and practicability of the algorithm evaluation pro-posed in this paper has been proved.

Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeynets for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce an undiscounted, infinite-horizon Markov decision process on a continuous state space in order to model the defender's problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Finally, we simulate the policy for a conceptual network. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.

This paper mainly focuses on presenting a novel attack detection system to thread out the risk issues in IoT. The presented attack detection system links the interconnection of DevOps as it creates the correlation between development and IT operations. Further, the presented attack detection model ensures the operational security of different applications. In view of this, the implemented system incorporates two main stages named Proposed Feature Extraction process and Classification. The data from every application is processed with the initial stage of feature extraction, which concatenates the statistical and higher-order statistical features. After that, these extracted features are supplied to classification process, where determines the presence of attacks. For this classification purpose, this paper aims to deploy the optimized Deep Belief Network (DBN), where the activation function is tuned optimally. Furthermore, the optimal tuning is done by a renowned meta-heuristic algorithm called Lion Algorithm (LA). Finally, the performance of proposed work is compared and proved over other conventional methods.

Resource constrained devices such as sensors and RFIDs are utilized in many application areas to sense, store and transmit the sensitive data. This data must be encrypted to achieve confidentiality. The implementation of traditional public key encryption (PKE) techniques by these devices is always challenging as they possess very limited computational resources. Various encryption schemes based on identity-based encryption (IBE) and certificate-less public key encryption (CL-PKE) have been proposed to overcome limitations of PKI. However, many of these schemes involve the computationally expensive exponentiation and bilinear pairing operations on elliptic curve group to encrypt the messages. In this context, we propose a lightweight optimized CL-PKE scheme in which exponentiation and pairing operations are completely eliminated during encryption and only involves computation of cheaper addition and multiplication operations on elliptic curve. Implementation of the proposed scheme confirms its lightweight nature as compared to original CL-PKE scheme.

We present a system architecture for autonomic operation, administration and maintenance of both the optical and digital layers within the integrated optical transport network infrastructure. This framework encompasses the end-to-end instrumentation: From equipment commissioning to automatic discovery and bring-up, to self-managed, self-(re)configuring optical transport layer. We leverage prevalent networking protocols to build an autonomic control plane for the optical network elements. Various aspects of security, a critical element for self-managed operations, are addressed. We conclude with a discussion on the interaction with SDN, and how autonomic functions can benefit from these capabilities, a brief survey of standardization activities and scope for future work.

Android applications (Apps) have penetrated almost every aspect of our lives, bring users great convenience as well as security concerns. Even though Android system adopts permission mechanism to restrict Apps from accessing important resources of a smartphone, such as telephony, camera and GPS location, users face still significant risk of privacy leakage due to the overprivileged permissions. The overprivileged permission means the extra permission declared by the App but has nothing to do with its function. Unfortunately, there doesn't exist any tool for ordinary users to detect the overprivileged permission of an App, hence most users grant any permission declared by the App, intensifying the risk of private information leakage. Although some previous studies tried to solve the problem of permission overprivilege, their methods are not applicable nowadays because of the progress of App protection technology and the update of Android system. Towards this end, we develop a user-friendly tool based on frequent item set mining for the detection of overprivileged permissions of Android Apps, which is named Droidtector. Droidtector can operate in online or offline mode and users can choose any mode according to their situation. Finally, we run Droidtector on 1000 Apps crawled from Google Play and find that 479 of them are overprivileged, accounting for about 48% of all the sample Apps.

BLE is used to transmit and receive data between sensors and devices. Most of the IOT devices employ BLE for wireless communication because it suits their requirements such as less energy constraints. The major security vulnerabilities in BLE protocol can be used by attacker to perform MITM attacks and hence violating confidentiality and integrity of data. Although BLE 4.2 prevents most of the attacks by employing elliptic-curve diffie-Hellman to generate LTK and encrypt the data, still there are many devices in the market that are using BLE 4.0, 4.1 which are vulnerable to attacks. This paper shows the simple demonstration of possible attacks on BLE devices that use various existing tools to perform spoofing, MITM and firmware attacks. We also discussed the security, privacy and its importance in BLE devices.

Autonomous technologies have been rapidly replacing the traditional manual intervention nearly in every aspect of our life. These technologies essentially require robots to carry out their automated processes. Nowadays, with the emergence of industry 4.0, robots are increasingly being remote-controlled via client-server connection, which creates uncommon vulnerabilities that allow attackers to target those robots. The development of an open source operational environment for robots, known as Robot Operating System (ROS) has come as a response to these demands. Security and privacy are crucial for the use of ROS as the chance of a compromise may lead to devastating ramifications. In this paper, an overview of ROS and the attacks targeting it are detailed and discussed. Followed by a review of the ROS security and digital investigation studies.

Over the last decade, a technology called Internet of Things (IoT) has been evolving at a rapid pace. It enables the development of endless applications in view of availability of affordable components which provide smart ecosystems. The IoT devices are constrained devices which are connected to the internet and perform sensing tasks. Each device is identified by their unique address and also makes use of the Constrained Application Protocol (CoAP) as one of the main web transfer protocols. It is an application layer protocol which does not maintain secure channels to transfer information. For authentication and end-to-end security, Datagram Transport Layer Security (DTLS) is one of the possible approaches to boost the security aspect of CoAP, in addition to which there are many suggested ways to protect the transmission of sensitive information. CoAP uses DTLS as a secure protocol and UDP as a transfer protocol. Therefore, the attacks on UDP or DTLS could be assigned as a CoAP attack. An attack on DTLS could possibly be launched in a single session and a strong authentication mechanism is needed. Man-In-The-Middle attack is one the peak security issues in CoAP as cited by Request For Comments(RFC) 7252, which encompasses attacks like Sniffing, Spoofing, Denial of Service (DoS), Hijacking, Cross-Protocol attacks and other attacks including Replay attacks and Relay attacks. In this work, a client-server architecture is setup, whose end devices communicate using CoAP. Also, a proxy system was installed across the client side to launch an active interception between the client and the server. The work will further be enhanced to provide solutions to mitigate these attacks.

Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants' personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants' privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.