Biblio

Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.

Large enterprises and organizations from both private and public sectors typically outsource a platform solution, as part of the Managed Security Services (MSSs), from 3rd party providers (MSSPs) to monitor and analyze their data containing cyber security information. Sharing such data among these large entities is believed to improve their effectiveness and efficiency at tackling cybercrimes, via improved analytics and insights. However, MSS platform customers currently are not able or not willing to share data among themselves because of multiple reasons, including privacy and confidentiality concerns, even when they are using the same MSS platform. Therefore any proposed mechanism or technique to address such a challenge need to ensure that sharing is achieved in a secure and controlled way. In this paper, we propose a new architecture and use case driven designs to enable confidential, flexible and collaborative data sharing among such organizations using the same MSS platform. MSS platform is a complex environment where different stakeholders, including authorized MSSP personnel and customers' own users, have access to the same platform but with different types of rights and tasks. Hence we make every effort to improve the usability of the platform supporting sharing while keeping the existing rights and tasks intact. As an innovative and pioneering attempt to address the challenge of data sharing in the MSS platform, we hope to encourage further work to follow so that confidential and collaborative sharing eventually happens among MSS platform customers.

A country drives by their people and the electricity energy, the availability of the electricity power reflects the strength of that country. All most everything depends on the electricity energy, So it is become very important that we use the available energy very efficiently, and here the energy management come in the picture and Non Intrusive appliance Load monitoring (NIALM) is the part of energy management, in which the energy consumption by the particular load is monitored without any intrusion of wire/circuit. In literature, NIALM has been discussed as a monitoring process for conservation of energy using single point sensing (SPS) for extraction of aggregate signal of the appliances' features, ignoring the second function of demand response (DR) assuming that it would be manual or sensor-based. This assumption is not implementable in developing countries like India, because of requirement of extra cost of sensors, and privacy concerns. Surprisingly, despite decades of research on NIALM, none of the suggested procedures has resulted in commercial application. This paper highlights the causes behind non- commercialization, and proposes a viable and easy solution worthy of commercial exploitation both for monitoring and DR management for outage reduction in respect of Indian domestic consumers. Using a approach of multi point sensing (MPS), combined with Independent Component Analysis (ICA), experiments has been done in laboratory environment and CPWD specification has been followed.

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

Security refers to precautions designed to shield the availability and integrity of information exchanged among the digital global community. Information safety measure typically protects the virtual facts from unauthorized sources to get a right of entry to, disclosure, manipulation, alteration or destruction on both hardware and software technologies. According to an evaluation through experts operating in the place of information safety, some of the new cyber-attacks are keep on emerging in all the business processes. As a stop result of the analyses done, it's been determined that although the level of risk is not excessive in maximum of the attacks, it's far a severe risk for important data and the severity of those attacks is prolonged. Prior safety structures has been established to monitor various cyber-threats, predominantly using a gadget processed data or alerts for showing each deterministic and stochastic styles. The principal finding for deterministic patterns in cyber- attacks is that they're neither unbiased nor random over the years. Consequently, the quantity of assaults in the past helps to monitor the range of destiny attacks. The deterministic styles can often be leveraged to generate moderately correct monitoring.

Internet of Battlefield Things (IoBT) devices such as actuators, sensors, wearable devises, robots, drones, and autonomous vehicles, facilitate the Intelligence, Surveillance and Reconnaissance (ISR) to Command and Control and battlefield services. IoBT devices have the ability to collect operational field data, to compute on the data, and to upload its information to the network. Securing the IoBT presents additional challenges compared with traditional information technology (IT) systems. First, IoBT devices are mass produced rapidly to be low-cost commodity items without security protection in their original design. Second, IoBT devices are highly dynamic, mobile, and heterogeneous without common standards. Third, it is imperative to understand the natural world, the physical process(es) under IoBT control, and how these real-world processes can be compromised before recommending any relevant security counter measure. Moreover, unprotected IoBT devices can be used as “stepping stones” by attackers to launch more sophisticated attacks such as advanced persistent threats (APTs). As a result of these challenges, IoBT systems are the frequent targets of sophisticated cyber attack that aim to disrupt mission effectiveness.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, current operational conditions necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a collection of prior-developed cybersecurity techniques is reviewed for applicability to conditions presented by IoBT operational environments (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) through use of supporting case study examples. The research techniques covered focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) ensuring continued trust of known IoT assets and IoBT systems.

Android operating systems have become a prime target for attackers as most of the market is currently dominated by Android users. The situation gets worse when users unknowingly download or sideload cloning applications, especially gaming applications that look like benign games. In this paper, we present, a dynamic Android gaming malware detection system based on system call analysis to classify malicious and legitimate games. We performed the dynamic system call analysis on normal and malicious gaming applications while applications are in execution state. Our analysis reveals the similarities and differences between benign and malware game system calls and shows how dynamically analyzing the behavior of malicious activity through system calls during runtime makes it easier and is more effective to detect malicious applications. Experimental analysis and results shows the efficiency and effectiveness of our approach.

In view of the demand for the continuous guarantee capability of the information system in the diversified task and the complex cyber threat environment, a dual loop architecture of the resilient cloud environment for mission assurance is proposed. Firstly, general technical architecture of cloud environment is briefly introduced. Drawing on the idea of software definition, a resilient dual loop architecture based on "perception analysis planning adjustment" is constructed. Then, the core mission assurance system deployment mechanism is designed using the idea of distributed control. Finally, the core mission assurance system is designed in detail, which is consisted of six functional modules, including mission and environment awareness network, intelligent anomaly analysis and prediction, mission and resource situation generation, mission and resource planning, adaptive optimization and adjustment. The design of the dual loop architecture of the resilient cloud environment for mission assurance will further enhance the fast adaptability of the information system in the complex cyber physical environment.

Modern multicore System-on-Chips (SoCs) are regularly designed with third-party Intellectual Properties (IPs) and software tools to manage the complexity and development cost. This approach naturally introduces major security concerns, especially for those SoCs used in critical applications and cyberinfrastructure. Despite approaches like split manufacturing, security testing and hardware metering, this remains an open and challenging problem. In this work, we propose a dynamic intrusion detection approach to address the security challenge. The proposed runtime system (SoCINT) systematically gathers information about untrusted IPs and strictly enforces the access policies. SoCINT surpasses the-state-of-the-art monitoring systems by supporting hardware tracing, for more robust analysis, together with providing smart counterintelligence strategies. SoCINT is implemented in an open source processor running on a commercial FPGA platform. The evaluation results validate our claims by demonstrating resilience against attacks exploiting erroneous or malicious IPs.

This paper outlines the concept of intellectual property utilization and develops a framework for the targeted generation of intellectual property for the benefit of various economic entities. The study proposes two types of the decision support system: (i) based on deterministic logic, and (ii) based on multi-agent systems. The results of the study offer the development of a mathematical approach to the interaction process of agents in multi-agent systems, inter alia related to the targeted generation of intellectual property.

With the rapid development of the Internet of vehicles, there is a huge amount of multimedia data becoming a hidden trouble in the Internet of Things. Therefore, it is necessary to process and store them in real time as a way of big data curation. In this paper, a method of real-time processing and storage based on CDN in vehicle monitoring system is proposed. The MPEG-DASH standard is used to process the multimedia data by dividing them into MPD files and media segments. A real-time monitoring system of vehicle on the basis of the method introduced is designed and implemented.

In this paper, we consider the runtime verification problem of safety hyperproperties for deterministic programs. Several security and information-flow policies such as data minimality, non-interference, integrity, and software doping are naturally expressed formally as safety hyperproperties. Although there are monitoring results for hyperproperties, the algorithms are very complex since these are properties over set of traces, and not over single traces. For the deterministic input-output programs that we consider, and the specific safety hyperproperties we are interested in, the problem can be reduced to monitoring of trace properties. In this paper, we present a simpler monitoring approach for safety hyperproperties of deterministic programs. The approach involves transforming the given safety hyperproperty into a trace property, extracting a characteristic predicate for the given hyperproperty, and providing a parametric monitor taking such predicate as parameter. For any hyperproperty in the considered subclass, we show how runtime verification monitors can be synthesised. We have implemented our approach in the form of a parameterised monitor for the given class, and have applied it to a number of hyperproperties including data minimisation, non-interference, integrity and software doping. We show results concerning both offline and online monitoring.

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.

Malware is one of the biggest threats in cloud computing. Malware running inside virtual machines or containers could steal critical information or continue to attack other cloud nodes. To detect malware in cloud, especially zero-day malware, signature-and machine-learning-based approaches are proposed to analyze the execution binary. However, malicious binary files may not permanently be stored in the file system of virtual machine or container, periodically scanner may not find the target files. Dynamic analysis approach usually introduce run-time overhead to virtual machines, which is not widely used in cloud. To solve these problems, we propose a memory analysis approach to detect malware, employing the deep learning technology. The system analyzes the memory image periodically during malware execution, which will not introduce run-time overhead. We first extract the memory snapshot from running virtual machines or containers. Then, the snapshot is converted to a grayscale image. Finally, we employ CNN to detect malware. In the learning phase, malicious and benign software are trained. In the testing phase, we test our system with real-world malwares.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.

In many applications, source nodes send the sensing information of the monitored objects and the sinks receive the transmitted data. Considering the limited resources of sensor nodes, location privacy preservation becomes an important issue. Although many schemes are proposed to preserve source or sink location security, few schemes can preserve the location security of source nodes and sinks. In order to solve this problem, we propose a novel of multi-branch source location privacy protection method based on random walk. This method hides the location of real source nodes by setting multiple proxy sources. And multiple neighbors are randomly selected by the real source node as receivers until a proxy source receives the packet. In addition, the proxy source is chosen randomly, which can prevent the attacker from obtaining the location-related data of the real source node. At the same time, the scheme sets up a branch interference area around the base station to interfere with the adversary by increasing routing branches. Simulation results describe that our scheme can efficiently protect source and sink location privacy, reduce the communication overhead, and prolong the network lifetime.

In this paper, we investigate the protection mechanism of source location privacy, in which back-tracing attack is performed by an adversary. A dynamic and disjoint routing mechanism (DDRM) is proposed to achieve a strong protection for source location privacy in an energy-efficient manner. Specially, the selection of intermediate node renders the message transmission randomly and flexibly. By constructing k disjoint paths, an adversary could not receive sufficient messages to locate the source node. Simulation results illustrate the effectiveness of the proposed mechanism.

Wireless sensor networks (WSNs) have the potential to be widely used in many applications. Due to lack of a protected physical boundary, wireless communications are vulnerable to unauthorized interception and detection. While encryption can provide the integrality and confidentiality of the message, it is much more difficult to adequately address the source location privacy. For static deployed WSNs, adversary can easily perform trace-back attack to locate the source nodes by monitoring the traffic. The eavesdropped messages will leak the direction information of the source location by statistic analysis on traffic flow. In this paper, we propose a theoretical analysis measurement to address the quantitative amount of the information leakage from the eavesdropped message. Through this scheme, we analyze the conditions that satisfy the optimum protection for routing protocol design. Based on the proposed principle, we design a routing algorithm to minimize the information leakage by distributing the routing path uniformly in WSN. The theoretical analysis shows the proposed routing algorithm can provide approximate maximization of source location privacy. The simulation results show the proposed routing algorithm is very efficient and can be used for practical applications.

Wireless Sensor Networks (WSNs) are used in sensitive applications such as in asset monitoring applications. Due to the sensitivity of information in these applications, it is important to ensure that flow of data between sensor nodes is secure and does not expose any information about the source node or the monitored assets. This paper proposes a scheme to preserve the source location privacy based on random routing techniques. To achieve high privacy, the proposed scheme randomly sends packet to sink node through tactically positioned agent nodes. The position of agent nodes is designed to guarantee that successive packets are routed through highly random and perplexing routing paths as compared to other routing schemes. Simulation results demonstrate that proposed scheme provides longer safety period and higher privacy against both, patient and cautious adversaries.

Wireless Sensor Networks (WSNs) consist of low-cost, resource-constrained sensor nodes and a designated node called a sink which collects data from the sensor nodes. A WSN can be used in numerous applications such as subject tracking and monitoring, where it is often desirable to keep the location of the subject private. Without location privacy protection, an adversary can locate the subject. In this paper, we propose an algorithm that tries to keep the subject location private from a global adversary, which can see the entire network traffic, in an energy efficient way.

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.

Industrial Control systems traditionally achieved security by using proprietary protocols to communicate in an isolated environment from the outside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, a device acting as a connection between the operational technology network and information technology network is proposed. The device is an intrusion detection system related to legacy systems that is able to collect and reporting data to and from industrial IoT devices. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. At a later stage, it collect and aggregate data from and to IoT domain. A simple set up is considered to prove the effectiveness of the approach.

Nowadays citizens live in a world where communication technologies offer opportunities for new interactions between people and society. Clearly, e-government is changing the way citizens relate to their government, moving the interaction of physical environment and management towards digital participation. Therefore, it is necessary for e-government to have procedures in place to prevent and lessen the negative impact of an attack or intrusion by third parties. In this research work, he focuses on the implementation of anonymous communication in a proof of concept application called “Delta”, whose function is to allow auctions and offers of products, thus marking the basis for future implementations in e-government services.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.