Biblio

Distributed Denial of Service (DDoS) attacks became a true threat to network infrastructure. DDoS attacks are capable of inflicting major disruption to the information communication technology infrastructure. DDoS attacks aim to paralyze networks by overloading servers, network links, and network devices with illegitimate traffic. Therefore, it is important to detect and mitigate DDoS attacks to reduce the impact of DDoS attacks. In traditional networks, the hardware and software to detect and mitigate DDoS attacks are expensive and difficult to deploy. Software-Defined Network (SDN) is a new paradigm in network architecture by separating the control plane and data plane, thereby increasing scalability, flexibility, control, and network management. Therefore, SDN can dynamically change DDoS traffic forwarding rules and improve network security. In this study, a DDoS attack detection and mitigation system was built on the SDN architecture using the random forest machine-learning algorithm. The random forest algorithm will classify normal and attack packets based on flow entries. If packets are classified as a DDoS attack, it will be mitigated by adding flow rules to the switch. Based on tests that have been done, the detection system can detect DDoS attacks with an average accuracy of 98.38% and an average detection time of 36 ms. Then the mitigation system can mitigate DDoS attacks with an average mitigation time of 1179 ms and can reduce the average number of attack packets that enter the victim host by 15672 packets and can reduce the average number of CPU usage on the controller by 44,9%.

This work proposes an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators. Firstly, by employing the command filtered backstepping technology, the ``explosion of complexity'' problem is overcame. Then, the event-triggered strategy is utilized which makes that the control input is updated aperiodically when the event-trigger occurs. The utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Moreover, it can be proved that all the variables in the closed-loop system are bounded and the tracking error converges asymptotically to zero. Finally, the simulation studies are included to show the effectiveness of the proposed control scheme.

It has been a hot research topic to detect and mitigate Distributed Denial-of-Service (DDoS) attacks due to the significant increase of serious threat of such attacks. The rapid growth of Internet of Things (IoT) has intensified this trend, e.g. the Mirai botnet and variants. To address this issue, a light-weight DDoS mitigation mechanism was presented. In the proposed scheme, flooding attacks are detected by stochastic queue allocation which can be executed with widespread and inexpensive commercial products at a network edge. However, the detection process is delayed when the number of incoming flows is large because of the randomness of queue allocation. Thus, in this paper we propose an efficient queue allocation algorithm for rapid DDoS mitigation using limited resources. The idea behind the proposed scheme is to avoid duplicate allocation by decreasing the randomness of the existing scheme. The performance of the proposed scheme was confirmed via theoretical analysis and computer simulation. As a result, it was confirmed that malicious flows are efficiently detected and discarded with the proposed algorithm.

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.

In emergency situations like recent Australian bushfires, it is crucial for civilians and firefighters to receive critical information such as escape routes and safe sheltering points with guarantees on information quality attributes. Mobile Ad-hoc Networks (MANETs) can provide communications in bushfire when fixed infrastructure is destroyed and not available. Current MANET solutions, however, are mostly tested under static bushfire scenario. In this work, we investigate the impact of a realistic dynamic bushfire in a dry eucalypt forest with a shrubby understory, on the performance of data delivery solutions in a MANET. Simulation results show a significant degradation in the performance of state-of-the-art MANET quality of information solution. Other than frequent source handovers and reduced user usability, packet arrival latency increases by more than double in the 1st quartile with a median drop of 74.5 % in the overall packet delivery ratio. It is therefore crucial for MANET solutions to be thoroughly evaluated under realistic dynamic bushfire scenarios.

The convergence of Internet of Things (IoT) and cloud computing is due to the practical necessity for providing broader services to extensive user in distinct environments. However, cloud computing has numerous constraints for applications that require high-mobility and high latency, notably in adversarial situations (e.g. battlefields). These limitations can be elevated to some extent, in a fog computing model because it covers the gap between remote data-center and edge device. Since, the fog nodes are usually installed in remote areas, therefore, they impose the design of fool proof safety solution for a fog-based setting. Thus, to ensure the security and privacy of fog-based environment, numerous schemes have been developed by researchers. In the recent past, Jia et al. (Wireless Networks, DOI: 10.1007/s11276-018-1759-3) designed a fog-based three-party scheme for healthcare system using bilinear. They claim that their scheme can withstand common security attacks. However, in this work we investigated their scheme and show that their scheme has different susceptibilities such as revealing of secret parameters, and fog node impersonation attack. Moreover, it lacks the anonymity of user anonymity and has inefficient login phase. Consequently, we have suggestion with some necessary guidelines for attack resilience that are unheeded by Jia et al.

This paper aims to address the problem of Deepfake Detection using a Two-Stream Capsule Network. First we review methods used to create Deepfake content, as well as methods proposed in the literature to detect such Deepfake content. We then propose a novel architecture to detect Deepfakes, which consists of a two-stream Capsule network running in parallel that takes in both RGB images/frames as well as Error Level Analysis images. Results show that the proposed approach exhibits the detection accuracy of 73.39 % and 57.45 % for the Deepfake Detection Challenge (DFDC) and the Celeb-DF datasets respectively. These results are, however, from a preliminary implementation of the proposed approach. As part of future work, population-based optimization techniques such as Particle Swarm Optimization (PSO) will be used to tune the hyper parameters for better performance.

Logic locking is a well-explored defense mechanism against various types of hardware security attacks. Recent approaches to logic locking replace portions of a circuit with reconfigurable blocks such as look-up tables (LUTs) and switch boxes (SBs) to primarily achieve logic and routing obfuscation, respectively. However, these techniques may incur significant design overhead, and methods that can mitigate the implementation cost for a given security level are desirable. In this paper, we address this challenge by proposing an algorithm for deciding the location and inputs of the LUTs in LUT-based obfuscation to enhance security and reduce design overhead. We then introduce a locking method that combines LUTs with SBs to further robustify LUT-based obfuscation, largely independently of the specific LUT locations. We illustrate the effectiveness of the proposed approaches on a set of ISCAS benchmark circuits.

As the web keeps increasing in size, the number of vulnerable and poorly-managed websites increases commensurately. Attackers rely on armies of malicious bots to discover these vulnerable websites, compromising their servers, and exfiltrating sensitive user data. It is, therefore, crucial for the security of the web to understand the population and behavior of malicious bots.In this paper, we report on the design, implementation, and results of Aristaeus, a system for deploying large numbers of "honeysites", i.e., websites that exist for the sole purpose of attracting and recording bot traffic. Through a seven-month-long experiment with 100 dedicated honeysites, Aristaeus recorded 26.4 million requests sent by more than 287K unique IP addresses, with 76,396 of them belonging to clearly malicious bots. By analyzing the type of requests and payloads that these bots send, we discover that the average honeysite received more than 37K requests each month, with more than 50% of these requests attempting to brute-force credentials, fingerprint the deployed web applications, and exploit large numbers of different vulnerabilities. By comparing the declared identity of these bots with their TLS handshakes and HTTP headers, we uncover that more than 86.2% of bots are claiming to be Mozilla Firefox and Google Chrome, yet are built on simple HTTP libraries and command-line tools.

Quantum computers have the potential to outshine classical alternatives in solving specific problems, under the assumption of mature enough hardware. A specific subset of these problems relate to military applications. In this paper we consider the state-of-the-art of quantum technologies and different applications of this technology. Additionally, four use-cases of quantum computing specific for military applications are presented. These use-cases are directly in line with the 2021 AI strategic agenda of the Netherlands Ministry of Defense.

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches.

Pairings on elliptic curves are exploited for pairing-based cryptography, e.g., ID-based encryption and group signature authentication. For secure cryptography, it is important to choose the curves that have resistance to a special variant of the tower number field sieve (TNFS) that is an attack for the finite fields. However, for the pairings on several curves with embedding degree \$k=\10,11,13,14\\$ resistant to the special TNFS, efficient algorithms for computing the final exponentiation constructed by the lattice-based method have not been provided. For these curves, the authors present efficient algorithms with the calculation costs in this manuscript.

With the paradigm shift to cloud-based operations, reliable and secure access to and transfer of data between differing security domains has never been more essential. A Cross Domain Solution (CDS) is a guarded interface which serves to execute the secure access and/or transfer of data between isolated and/or differing security domains defined by an administrative security policy. Cross domain security requires trustworthiness at the confluence of the hardware and software components which implement a security policy. Security components must be relied upon to defend against widely encompassing threats – consider insider threats and nation state threat actors which can be both onsite and offsite threat actors – to information assurance. Current implementations of CDS systems use suboptimal Trusted Computing Bases (TCB) without any formal verification proofs, confirming the gap between blind trust and trustworthiness. Moreover, most CDSs are exclusively operated by Department of Defense agencies and are not readily available to the commercial sectors, nor are they available for independent security verification. Still, more CDSs are only usable in physically isolated environments such as Sensitive Compartmented Information Facilities and are inconsistent with the paradigm shift to cloud environments. Our purpose is to address the question of how trustworthiness can be implemented in a remotely deployable CDS that also supports availability and accessibility to all sectors. In this paper, we present a novel CDS system architecture which is the first to use a formally verified TCB. Additionally, our CDS model is the first of its kind to utilize a computation-isolation approach which allows our CDS to be remotely deployable for use in cloud-based solutions.

in this paper, we describe a method for detecting abnormal pedestrians or cars by expressing the behavioral characteristics of pedestrians on a global surveillance map in a video security system using CCTV and patrol robots. This method converts a large amount of video surveillance data into a compressed map shape format to efficiently transmit and process data. By using deep learning auto-encoder and CNN algorithm, pedestrians belonging to the abnormal category can be detected in two steps. In the case of the first-stage abnormal candidate extraction, the normal detection rate was 87.7%, the abnormal detection rate was 88.3%, and in the second stage abnormal candidate filtering, the normal detection rate was 99.8% and the abnormal detection rate was 96.5%.

Completely Automated Public Turing Tests (CAPTCHA) have been used to differentiate between computers and humans for quite some time now. There are many different varieties of CAPTCHAs - text-based, image-based, audio, video, arithmetic, etc. However, not all varieties are suitable for the visually impaired. As time goes by and Spambots and APIs grow more accurate, the CAPTCHA tests have been constantly updated to stay relevant, but that has not happened with the audio CAPTCHA. There exists an audio CAPTCHA intended for the blind/visually impaired but many blind/visually impaired find it difficult to solve. We propose an alternative to the existing system, which would make use of unique sound samples layered with music generated through GANs (Generative Adversarial Networks) along with noise and other layers of sounds to make it difficult to dissect. The user has to count the number of times the unique sound was heard in the sample and then input that number. Since there are no letters or numbers involved in the samples, speech-to-text bots/APIs cannot be used directly to decipher this system. Also, any user regardless of their native language can comfortably use this system.

The recent focus of cyber security on automated detection of malware for Android apps has omitted the study of some apps used for “legitimate” purposes, such as steganography apps. Mobile steganography apps can be used for delivering harmful messages, and while current research on steganalysis targets the detection of stego images using academic algorithms and well-built benchmarking image data sets, the community has overlooked uncovering a mobile app itself for its ability to perform steganographic embedding. Developing automatic tools for identifying the code in a suspect app as a stego app can be very challenging: steganography algorithms can be represented in a variety of ways, and there exists many image editing algorithms which appear similar to steganography algorithms.This paper proposes the first automated approach to detect Android steganography apps. We use symbolic execution to summarize an app’s image operation behavior into expression trees, and match the extracted expression trees with reference trees that represents the expected behavior of a steganography embedding process. We use a structural feature based similarity measure to calculate the similarity between expression trees. Our experiments show that, the propose approach can detect real world Android stego apps that implement common spatial domain and frequency domain embedding algorithms with a high degree of accuracy. Furthermore, our procedure describes a general framework that has the potential to be applied to other similar questions when studying program behaviors.

The growth of inter-dependency intricacies of Supervisory Control and Data Acquisition (SCADA) systems in industrial operations generates a likelihood of increased vulnerability to malicious threats and machine learning approaches have been extensively utilized in the research for vulnerability detection. Nonetheless, to improve security, an enhanced vulnerability detection using hyper-parameter-tune machine learning is proposed for early detection, classification and mitigation of SCADA communication and transmission networks by classifying benign, or malicious DNS attacks. The proposed scheme, an ensemble optimizer (GentleBoost) upon hyper-parameter tuning, gave a comparative achievement. From the simulation results, the proposed scheme had an outstanding performance within the shortest possible time with an accuracy of 99.49%, 99.23% for precision, and a recall rate of 99.75%. Also, the model was compared to other contemporary algorithms and outperformed all the other algorithms proving to be an approach to keep abreast of the SCADA network vulnerabilities and attacks.

While a huge number of IoT devices are connecting to the cyber physical systems, the demand for security of these devices are increasing. Due to the demand, world-wide competition for lightweight cryptography oriented towards small devices have been held. Although tamper resistance against illegal attacks were evaluated in the competition, there is no evaluation for embedded malicious circuits such as hardware Trojan.To achieve security evaluation for embedded malicious circuits, this study proposes an implementation method of hardware Trojan for Elephant which is one of the finalists in the competition. And also, the implementation overhead of hardware Trojans and the security risk of hardware Trojan are evaluated.

Conventional systems has to be updated as the technology advances at quick pace. A smart grid is a renovated and digitalized version of a standard electrical infrastructure that allows two-way communication between customers and the utility, which overcomes huge manual hustle. Advanced Metering Infrastructure plays a major role in a smart grid by automatically reporting the power consumption readings to the utility through communication networks. However, there is always a trade-off. Security of AMI communication is a major problem that must be constantly monitored if this technology is to be fully utilized. This paper mainly focuses on developing a virtual setup of fully functional smart meter and a web application for generating electricity bill which allows consumer to obtain demand response, where the data is managed at server side. It also focuses on analyzing the potential security concerns posed by MITM-Arp-spoofing attacks on AMI systems and session hijacking attacks on web interfaces. This work also focusses on mitigating the vulnerabilities of session hijacking on web interface by restricting the cookies so that the attacker is unable to acquire any confidential data.

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.

This article describes the process of synchronizing the generation of random sequences by a quantum random number generator (QRNG) that can be used as secret keys for known cryptographic transformations. The subject of the research is a method for synchronizing the generation of random QRNG sequences based on L1 (C/A) signals of the global positioning system (GPS) using control correcting information received from control correcting stations.

A smart home provides better living environment by allowing remote Internet access for controlling the home appliances and devices. Security of smart homes is an important application area commonly using Passive Infrared Sensors (PIRs), image capture and analysis but such solutions sometimes fail to detect an event. An unambiguous person detection is important for security applications so that no event is missed and also that there are no false alarms which result in waste of resources. Cloud platforms provide deep learning and IoT services which can be used to implement an automated and failsafe security application. In this paper, we demonstrate reliable person detection for indoor and outdoor scenarios by integrating an application running on an edge device with AWS cloud services. We provide results for identifying a person before authorizing entry, detecting any trespassing within the boundaries, and monitoring movements within the home.

This paper studies the physical layer security of four reactive relay selection methods (optimum relay selection, opportunist relay selection enhancement, suboptimal relay selection enhancement and partial relay selection enhancement) in a cooperative cognitive radio network including one pair of primary users, one eavesdropper, multiple relays and secondary users with perfect and imperfect channel state information (CSI) at receivers. In addition, we consider existing a direct link from a secondary source (S) to secondary destination receivers (D) and eavesdroppers (E). The secrecy outage probability, outage probability, intercept probability and reliability are calculated to verify the four relay selection methods with the fading channels by using Monte Carlo simulation. The results show that the loss of secrecy outage probability when remaining direct links from S to D and S to E. Additionally, the results also show that the trade-off between secrecy outage probability and the intercept probability and the optimum relay selection method outperforms other methods.

Visual Question Answering (VQA) models have achieved significant success in recent times. Despite the success of VQA models, they are mostly black-box models providing no reasoning about the predicted answer, thus raising questions for their applicability in safety-critical such as autonomous systems and cyber-security. Current state of the art fail to better complex questions and thus are unable to exploit compositionality. To minimize the black-box effect of these models and also to make them better exploit compositionality, we propose a Dynamic Neural Network (DMN), which can understand a particular question and then dynamically assemble various relatively shallow deep learning modules from a pool of modules to form a network. We incorporate compositional temporal attention to these deep learning based modules to increase compositionality exploitation. This results in achieving better understanding of complex questions and also provides reasoning as to why the module predicts a particular answer. Experimental analysis on the two benchmark datasets, VQA2.0 and CLEVR, depicts that our model outperforms the previous approaches for Visual Question Answering task as well as provides better reasoning, thus making it reliable for mission critical applications like safety and security.