Biblio

With the widespread of Artificial Intelligence (AI)-enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluating the performances of security applications. This paper presents the description of new datasets, named ToNİoT datasets that include distributed data sources collected from Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, and datasets of Network traffic. The paper aims to describe the new testbed architecture used to collect Linux datasets from audit traces of hard disk, memory and process. The architecture was designed in three distributed layers of edge, fog, and cloud. The edge layer comprises IoT and network systems, the fog layer includes virtual machines and gateways, and the cloud layer includes data analytics and visualization tools connected with the other two layers. The layers were programmatically controlled using Software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Linux ToNİoT datasets would be used to train and validate various new federated and distributed AI-enabled security solutions such as intrusion detection, threat intelligence, privacy preservation and digital forensics. Various Data analytical and machine learning methods are employed to determine the fidelity of the datasets in terms of examining feature engineering, statistics of legitimate and security events, and reliability of security events. The datasets can be publicly accessed from [1].

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

The Fog is an emergent computing architecture that will support the mobility and geographic distribution of Internet of Things (IoT) nodes and deliver context-aware applications with low latency to end-users. It forms an intermediate layer between IoT devices and the Cloud. However, Fog computing brings many requirements that increase the cost of security management. It inherits the security and trust issues of Cloud and acquires some of the vulnerable features of IoT that threaten data and application confidentiality, integrity, and availability. Several existing solutions address some of the security challenges following adequate adaptation, but others require new and innovative mechanisms. These reflect the need for a Fog architecture that provides secure access, efficient authentication, reliable and secure communication, and trust establishment among IoT devices and Fog nodes. The Fog might be more convenient to deploy decentralized authentication solutions for IoT than the Cloud if appropriately designed. In this short survey, we highlight the Fog security challenges related to IoT security requirements and architectural design. We conduct a comparative study of existing Fog architectures then perform a critical analysis of different authentication schemes in Fog computing, which confirms some of the fundamental requirements for effective authentication of IoT devices based on the Fog, such as decentralization, less resource consumption, and low latency.

Many autonomous control systems are frequently exposed to attacks, so methods for attack identification are crucial for a safe operation. To preserve the privacy of the subsystems and achieve scalability in large-scale systems, identification algorithms should not require global model knowledge. We analyze a previously presented method for hierarchical attack identification, that is embedded in a distributed control setup for systems of systems with coupled nonlinear dynamics. It is based on the exchange of local sensitivity information and ideas from sparse signal recovery. In this paper, we prove sufficient conditions under which the method is guaranteed to identify all components affected by some unknown attack. Even though a general class of nonlinear dynamic systems is considered, our rigorous theoretical guarantees are applicable to practically relevant examples, which is underlined by numerical experiments with the IEEE 30 bus power system.

Carefully constructed cyber-attacks directly influence the data integrity and the operational functionality of the smart energy grid. In this paper, we have explored the data integrity attack behaviour in a wide-area sensor-enabled IIoT-SCADA system. We have demonstrated that an intelligent cyber-attacker can inject false information through the sensor devices that may remain stealthy in the traditional detection module and corrupt estimated system states at the utility control centres. Next, to protect the operation, we defined a set of critical measurements that need to be protected for the resilient operation of the grid. Finally, we placed the measurement units using an optimal allocation strategy by ensuring that a limited number of nodes are protected against the attack while the system observability is satisfied. Under such scenarios, a wide range of experiments has been conducted to evaluate the performance considering IEEE 14-bus, 24 bus-reliability test system, 85-bus, 141-bus and 145-bus test systems. Results show that by ensuring the protection of around 25% of the total nodes, the IIoT-SCADA enabled energy grid can be protected against injection attacks while observability of the network is well-maintained.

Industrial IoT (IIoT) is a specialized subset of IoT which involves the interconnection of industrial devices with ubiquitous control and intelligent processing services to improve industrial system's productivity and operational capability. In essence, IIoT adapts a use-case specific architecture based on RFID sense network, BLE sense network or WSN, where heterogeneous industrial IoT devices can collaborate with each other to achieve a common goal. Nonetheless, most of the IIoT deployments are brownfield in nature which involves both new and legacy technologies (SCADA (Supervisory Control and Data Acquisition System)). The merger of these technologies causes high degree of cross-linking and decentralization which ultimately increases the complexity of IIoT systems and introduce new vulnerabilities. Hence, industrial organizations becomes not only vulnerable to conventional SCADA attacks but also to a multitude of IIoT specific threats. However, there is a lack of understanding of these attacks both with respect to the literature and empirical evaluation. As a consequence, it is infeasible for industrial organizations, researchers and developers to analyze attacks and derive a robust security mechanism for IIoT. In this paper, we developed a multi-layer taxonomy of IIoT attacks by considering both brownfield and greenfield architecture of IIoT. The taxonomy consists of 11 layers 94 dimensions and approximately 100 attack techniques which helps to provide a holistic overview of the incident attack pattern, attack characteristics and impact on industrial system. Subsequently, we have exhibited the practical relevance of developed taxonomy by applying it to a real-world use-case. This research will benefit researchers and developers to best utilize developed taxonomy for analyzing attack sequence and to envisage an efficient security platform for futuristic IIoT applications.

Majority of the daily and business activities nowadays are integrated and interconnected to the world across national, geographic and boundaries. Securing the Internet of Things (IoT) system is a challenge as these low powered devices in IoT system are very vulnerable to cyber-attacks and this will reduce the reliability of the system. Software Defined Network (SDN) intends to greatly facilitate the policy enforcement and dynamic network reconfiguration. This paper presents several architectures in the integration of IoT via SDN to improve security in the network and system.

This paper presents an event-triggered statistical estimation strategy and a data collection architecture for situational awareness (SA) in microgrids. An estimation agent structure based on the event-triggered Kalman filter is proposed and implemented for state estimation layer of the SA using long range wide area network (LoRAWAN) protocol. A setup has been developed which provides enormous data collection capabilities from smart meters in order to realize an adequate level of SA in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualization with a customized dashboard. It is shown that by using the developed estimation strategy, an adequate level of SA can be achieved with a minimum installation and communication cost to have an accurate average state estimation of the microgrid.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

Opportunistic routing (OR) is gaining popularity in low-duty wireless sensor network (WSN), so the need for efficient and reliable data transmission is becoming more essential. Reliable transmission is only feasible if the routing protocols are secure and efficient. Due to high energy consumption, current cryptographic schemes for WSN are not suitable. Trust-based OR will ensure security and reliability with fewer resources and minimum energy consumption. OR selects the set of potential candidates for each sensor node using a prioritized metric by load balancing among the nodes. This paper introduces a trust-based load-balanced OR for duty-cycled wireless sensor networks. The candidates are prioritized on the basis of a trusted OR metric that is divided into two parts. First, the OR metric is based on the average of four probability distributions: the distance from node to sink distribution, the expected number of hops distribution, the node degree distribution, and the residual energy distribution. Second, the trust metric is based on the average of two probability distributions: the direct trust distribution and the recommended trust distribution. Finally, the trusted OR metric is calculated by multiplying the average of two metrics distributions in order to direct more traffic through the higher priority nodes. The simulation results show that our proposed protocol provides a significant improvement in the performance of the network compared to the benchmarks in terms of energy consumption, end to end delay, throughput, and packet delivery ratio.

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.

Unauthorized access or intrusion is a massive threatening issue in the modern era. This study focuses on designing a model for an ideal intrusion detection system capable of defending a network by alerting the admins upon detecting any sorts of malicious activities. The study proposes a two layered anomaly-based detection model that uses filter co-relation method for dimensionality reduction along with Random forest and Support Vector Machine as its classifiers. It achieved a very good detection rate against all sorts of attacks including a low rate of false alarms as well. The contribution of this study is that it could be of a major help to the computer scientists designing good intrusion detection systems to keep an industry or organization safe from the cyber threats as it has achieved the desired qualities of a functional IDS model.

The coupling of safety-relevant embedded- and cyber-space components to build Cyber-Physical Systems (CPS) extends the functionality and quality in many business domains, while also creating new ones. Prime examples like Internet of Things and Industry 4.0 enable new technologies and extend the service capabilities of physical entities by building a universe of connected devices. In addition to higher complexity, the coupling of these heterogeneous systems results in many new challenges, which should be addressed by engineers and administrators. Here, security represents a major challenge, which may be well addressed in cyber-space engineering, but less in embedded system or CPS design. Although model-based engineering provides significant benefits for system architects, like reducing complexity and automated analysis, as well as being considered as standard methodology in embedded systems design, the aspect of security may not have had a major role in traditional engineering concepts. Especially the characteristics of CPS, as well as the coupling of safety-relevant (physical) components with high-scalable entities of the cyber-space domain have an enormous impact on the overall level of security, based on the introduced side effects and uncertainties. Therefore, we aim to define a model-based security-engineering framework, which is tailored to the needs of CPS engineers. Hereby, we focus on the actual modeling process, the evaluation of security, as well as quantitatively expressing security of a deployed CPS. Overall and in contrast to other approaches, we shift the engineering concepts on a semantic level, which allows to address the proposed challenges in CPS in the most efficient way.

Due to the rapid growth of using Internet of Things (IoT) devices in the daily life, the need to achieve an acceptable level of security and privacy according to the real security risks for these devices is rising. Security risks may include privacy threats like gaining sensitive information from a device, and authentication problems from counterfeit or cloned devices. It becomes more challenging to add strong security features to extremely constrained devices compared to battery operated devices that have more computational and storage capabilities. We propose a novel application specific instruction-set architecture that allows flexibility on many design levels and achieves the required security level for the Electronic Product Code (EPC) passive Radio Frequency Identification (RFID) tag device. Our solution moves a major design effort from hardware to software, which largely reduces the final unit cost. The proposed architecture can be implemented with 4,662 gate equivalent units (GEs) for 65 nm CMOS technology excluding the memory and the cryptographic units. The synthesis results fulfill the requirements of extremely constrained devices and allow the inclusion of cryptographic units into the datapath of the proposed application-specific instruction set processor (ASIP).

Compute-intensive platforms such as micro-servers and embedded systems have already undergone a shift from a single-chip to multichip architecture to achieve better yield and lower cost. However, performance of multichip systems is limited by the latency and power-hungry chip-to-chip wired I/Os. On the other hand, wireless interconnections are emerging as an energy-efficient and low latency interconnect solution for such multichip systems as it can mask long multi-hop off-chip wired I/O communication. Despite efficient communication, the unguided on and off-chip wireless communication introduce security vulnerabilities in the system. In this work, we propose a reconfigurable, secure millimeter-wave (mm-Wave) wireless interconnection architecture (AReS) for multichip systems capable of detecting and defending against emerging threats including Hardware Trojans (HTs) and Denial-of-Service (DoS) using a Machine Learning (ML)-based approach. The ML-based approach is used to classify internal and external attack to enable the required defense mechanism. To serve this purpose, we design a reconfigurable Medium Access Control (MAC) and a suitable communication protocol to enable sustainable communication even under jamming attack from both internal and external attackers. The proposed architecture also reuses the in-built test infrastructure to detect and withstand a persistent jamming attack in a wireless multichip system. Through simulation, we show that, the proposed wireless interconnection can sustain chip-to-chip communication even under persistent jamming attack with an average 1.44xand 1.56x latency degradation for internal and external attacks respectively for application-specific traffic.

Research teams or institutions in different countries need an effective and secure online platform for collaboration and data sharing. It is essential to build such a collaboration platform with strong data security and privacy. In this paper, we propose a platform for researchers to collaborate and share their data by leveraging attribute-based access control (ABAC) and blockchain technologies. ABAC provides an access control paradigm whereby access rights are granted to users through attribute-based policies, instead of user identities and roles. Hyperledger fabric permission blockchain is used to enable a decentralized secure data sharing environment and preserves user’s privacy. The proposed platform allows researchers to fully control their data, manage access to the data at a fine-grained level, keep file updates with proof of authorship, and ensure data integrity and privacy.

With the advancement of computing and communication technologies, data transmission in the internet are getting bigger and faster. However, it is necessary to secure the data to prevent fraud and criminal over the internet. Furthermore, most of the data related to statistics requires to be analyzed securely such as weather data, health data, financial and other services. This paper presents an implementation of cloud security using homomorphic encryption for data analytic in the cloud. We apply the homomorphic encryption that allows the data to be processed without being decrypted. Experimental results show that, for the polynomial degree 26, 28, and 210, the total executions are 2.2 ms, 4.4 ms, 25 ms per data, respectively. The implementation is useful for big data security such as for environment, financial and hospital data analytics.

Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.

Nowadays, cybersecurity threat is a big challenge to all organizations that present their services over the Internet. Distributed Denial of Service (DDoS) attack is the most effective and used attack and seriously affects the quality of service of each E-organization. Hence, mitigation this type of attack is considered a persistent need. In this paper, we used Network Load Balancing (NLB) and High Availability Proxy (HAProxy) as mitigation techniques. The NLB is used in the Windows platform and HAProxy in the Linux platform. Moreover, Internet Information Service (IIS) 10.0 is implemented on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 as web servers. We evaluated each load balancer efficiency in mitigating synchronize (SYN) DDoS attack on each platform separately. The evaluation process is accomplished in a real network and average response time and average CPU are utilized as metrics. The results illustrated that the NLB in the Windows platform achieved better performance in mitigation SYN DDOS compared to HAProxy in the Linux platform. Whereas, the average response time of the Window webservers is reduced with NLB. However, the impact of the SYN DDoS on the average CPU usage of the IIS 10.0 webservers was more than those of the Apache 2 webservers.

The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.

In recent years, we have seen an advent in software attestation defenses targeting embedded systems which aim to detect tampering with a device's running program. With a persistent threat of an increasingly powerful attacker with physical access to the device, attestation approaches have become more rooted into the device's hardware with some approaches even changing the underlying microarchitecture. These drastic changes to the hardware make the proposed defenses hard to apply to new systems. In this paper, we present and evaluate LAHEL as the means to study the implementation and pitfalls of a hardware-based attestation mechanism. We limit LAHEL to utilize existing technologies without demanding any hardware changes. We implement LAHEL as a hardware IP core which interfaces with the CoreSight Debug Architecture available in modern ARM cores. We show how LAHEL can be integrated to system on chip designs allowing for microcontroller vendors to easily add our defense into their products. We present and test our prototype on a Zynq-7000 SoC, evaluating the security of LAHEL against powerful time-of-check-time-of-use (TOCTOU) attacks, while demonstrating improved performance over existing attestation schemes.

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.

This paper presents SWAT - System-Wide Analysis Toolkit. It is based on open source emulation and debugging projects and implements the approaches for non-intrusive system-wide analysis and debugging: lightweight OS-agnostic virtual machine introspection, full system execution replay, non-intrusive debugging with WinDbg, and full system reverse debugging. These features are based on novel non-intrusive introspection and reverse debugging methods. They are useful for stealth debugging and analysis of the platforms with custom kernels. SWAT includes multi-platform emulator QEMU with additional instrumentation and debugging features, GUI for convenient QEMU setup and execution, QEMU plugin for non-intrusive introspection, and modified version of GDB. Our toolkit may be useful for the developers of the virtual platforms, emulators, and firmwares/drivers/operating systems. Virtual machine intospection approach does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. This paper includes the description of SWAT components, analysis methods, and some SWAT use cases.

Phishing attacks are the most common form of attacks that can happen over the internet. This method involves attackers attempting to collect data of a user without his/her consent through emails, URLs, and any other link that leads to a deceptive page where a user is persuaded to commit specific actions that can lead to the successful completion of an attack. These attacks can allow an attacker to collect vital information of the user that can often allow the attacker to impersonate the victim and get things done that only the victim should have been able to do, such as carry out transactions, or message someone else, or simply accessing the victim's data. Many studies have been carried out to discuss possible approaches to prevent such attacks. This research work includes three machine learning algorithms to predict any websites' phishing status. In the experimentation these models are trained using URL based features and attempted to prevent Zero-Day attacks by using proposed software proposal that differentiates the legitimate websites and phishing websites by analyzing the website's URL. From observations, the random forest classifier performed with a precision of 97%, a recall 99%, and F1 Score is 97%. Proposed model is fast and efficient as it only works based on the URL and it does not use other resources for analysis, as was the case for past studies.

Through the widespread use of information technologies, institutions have started to offer most of their services electronically. The best example of this is e-government. Since institutions provide their services to the electronic environment, the quality of the services they provide increases and their access to services becomes easier. Since personal information can be verified with inter-agency information sharing systems, wrong or unfair transactions can be prevented. Since information sharing between institutions is generally done through web services, protection of personal data transmitted via web services is of great importance. There are comprehensive national and international regulations on the protection of personal data. According to these regulations, protection of personal data shared between institutions is a legal obligation; protection of personal data is an issue that needs to be handled comprehensively. This study, protection of personal data shared between institutions through web services against software developers is discussed. With a proposed application, it is aimed to take a new security measure for the protection of personal data. The proposed application consists of a web interface prepared using React and Java programming languages and rest services that provide anonymization of personal data.