Biblio

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.

This paper describes a design of IoT enabled real-time bus tracking system. In this work a bus tracking mobile phone app is developed, using that people can exactly locate the bus status and time to bus arrival at bus-stop. This work uses high-frequency RFID tags at buses and RFID receivers at busstops and with NodeMCU real-time RIFD tagging (bus running) information is collected and uploaded on the cloud. Users can access the bus running and status from the cloud on the mobile app in real-time.

Radio Frequency Identification (RFID) technology has attracted much attention due to its variety of applications, e.g., inventory control and object tracking. Tag identification protocols are essential in such applications. However, in such protocols, significant time and power are consumed on inevitable simultaneous tag replies (collisions) because tags can't sense the media to organize their replies to the reader. In this paper, novel reader-tag interaction method is proposed in which low-complexity Digital Baseband Modulation Termination (DBMT) circuit is added to RFID tags to enhance collision resolution efficiency in conjunction with Streamlined Collision Resolution (SCR) scheme. The reader, in the proposed SCR, cuts off or reduces the power of its continuous wave signal for specific periods if corrupted data is detected. On the other hand, DBMT circuit at the tag measures the time of the reader signal cutoff, which in turn, allows the tag to interpret different cutoff periods into commands. SCR scheme is applied to ALOHA- and Tree-based protocols with varying numbers of tags to evaluate the performance under low and high collision probabilities. SCR provides a significant enhancement to both types of protocols with robust synchronization within collision slots. This novel reader-tag interaction method provides a new venue for revisiting tag identification and counting protocols.

With the wide application of video information, the protection of video information from illegal access has been widely investigated recently. An efficient selective encryption scheme for high efficiency video coding (HEVC) based on hyperchaotic Lorenz system is proposed. Firstly, the hyperchaotic Lorenz system is discretized and the generated chaotic state values are converted into chaotic pseudorandom sequences for encryption. The important syntax elements in HEVC are then selectively encrypted with the generated stream cipher. The experimental results show that the encrypted video is highly disturbed and the video information cannot be recognized. Through the analysis of objective index results, it is shown that the scheme is both efficient and security.

Satisfying security requirements for Mobile Ad-hoc Networks (MANETs) is a key challenge due to the limited power budget for the nodes composing those networks. Therefore, it is essential to exploit lightweight cryptographic algorithms to preserve the confidentiality of the messages being transmitted between different nodes in MANETs. At the heart of such algorithms lies the Elliptic Curve Cryptography (ECC). The importance of ECC lies in offering equivalent security with smaller key sizes, which results in faster computations, lower power consumption, as well as memory and bandwidth savings. However, when exploiting ECC in MANETs, it is essential to properly choose the parameters of ECC such that an acceptable level of confidentiality is achieved without entirely consuming the power budget of nodes. In addition, the delay of the communication should not abruptly increase. In this paper, we study the effect of changing the prime number use in ECC on power consumption, delay, and the security of the nodes in MANETs. Once a suitable prime number is chosen, a comparative analysis is conducted between two reactive routing protocols, namely, Ad-hoc on Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) in terms of power consummation and delay. Experimental results show that a prime number value of 197 for ECC alongside with DSR for routing preserve an acceptable level of security for MANETs with low average power consumption and low average delay in the communication.

Augmented Reality has a long history and has seen major technical advantages in the last years. With WebXR, a new web standard, Mobile Augmented Reality (MAR) applications are now available in the web browser. With our work, we implemented an Augmented Reality Information System and conducted a case study to evaluate the user acceptance of such an application build with WebXR. Our results indicate that the user acceptance regarding web-based MAR applications for our specific use case seems to be given. With our proposed architecture we also lay the foundation for other AR information systems.

In contrast to traditional grids, smart grids can help utilities save energy, thereby reducing operating costs. In the smart grid, the quality of monitoring and control can be fully improved by combining computing and intelligent communication knowledge. However, this will expose the system to FDI attacks, and the system is vulnerable to intrusion. Therefore, it is very important to detect such erroneous data injection attacks and provide an algorithm to protect the system from such attacks. In this paper, a FDI detection method based on LSTM has been proposed, which is validated by the simulation on the ieee-14 bus platform.

There is an increasing concern about possible hostile modification done to ICs, which are used in various critical applications. Such malicious modifications are referred to as Hardware Trojan. A novel procedure to detect these malicious Trojans using Genetic algorithm along with the logical masking technique which masks the Trojan module when embedded is presented in this paper. The circuit features such as transition probability and SCOAP are used as suitable parameters to identify the rare nodes which are more susceptible for Trojan insertion. A set of test patterns called optimal test patterns are generated using Genetic algorithm to claim that these test vectors are more feasible to detect the presence of Trojan in the circuit under test. The proposed methodologies are validated in accordance with ISCAS '85 and ISCAS '89 benchmark circuits. The experimental results proven that it achieves maximum Trigger coverage, Trojan coverage and is also able to successfully mask the inserted Trojan when it is triggered by the optimal test patterns.

This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NNis constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping functionis to design a GBT that performs as well as the KLT without requiring to explicitly com-pute the covariance matrix for each residual block to be transformed. To avoid signallingany additional information required to compute the inverse GBT-NN, we also introduce acoding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentageof preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT.

Wireless sensor networks (WSNs) are underlying network infrastructure for a variety of surveillance applications. The network should be tolerant of unexpected failures of sensor nodes to meet the Quality of Service (QoS) requirements of these applications. One major cause of failure is active security attacks such as Depletion-of-Battery (DoB) attacks. This paper model the problem of detecting such attacks as an anomaly detection problem in a dynamic graph. The problem is addressed by employing a cluster ensemble approach called the K-Means Spectral and Hierarchical ensemble (KSH) approach. The experimental result shows that KSH detected DoB attacks with better accuracy when compared to baseline approaches.

This paper outlines an approach to build an Intrusion detection system for a network interface device. This research work has developed a hybrid intrusion detection system which involves various machine learning techniques along with inference detection for a comparative analysis. It is explained in 2 phases: Training (Model Training and Inference Network Building) and Detection phase (Working phase). This aims to solve all the current real-life problem that exists in machine learning algorithms as machine learning techniques are stiff they have their respective classification region outside which they cease to work properly. This paper aims to provide the best working machine learning technique out of the many used. The machine learning techniques used in comparative analysis are Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) along with NSLKDD dataset for testing and training of our Network Intrusion Detection Model. The accuracy recorded for Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machines(SVM) respectively when tested independently are 98.088%, 82.971%, 95.75%, 81.971% and when tested with inference detection model are 98.554%, 66.687%, 97.605%, 93.914%. Therefore, it can be concluded that our inference detection model helps in improving certain factors which are not detected using conventional machine learning techniques.

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.

This work investigates the impact of mechanical strain on wake-up behavior of planar HfO2 ferroelectric capacitor-based memory. External in-plane strain was applied using a four-point bending tool and strain impact on remanent polarization and coercive voltage of the ferroelectric was monitored. It was established that compressive strain is beneficial for 2Pr improvement, while tensile strain leads to its degradation, with a sensitivity of -8.4 ± 0.5 % per 0.1 % of strain. Strain-induced polarization rotation is considered to be the most likely mechanism affecting 2Pr At the same time, no strain impact on Vcwas observed in the investigated strain range. The results seen here can be utilized to undertake stress engineering of ferroelectric memory in order to improve its performance.

In order to improve the security and anti-interference ability of industrial control system, this paper proposes an improved industrial neural network defense method based on the PCA dimension reduction and the improved deep neural network. Firstly, the proposed method reduces the dimensionality of the industrial data using the dimension reduction theory of principal component analysis (PCA). Then the deep neural network extracts the features of the network. Finally, the softmax classifier classifies industrial data. Experiment results show that compared with unintegrated algorithm, this method achieves higher recognition accuracy and has great application potential.

Wireless sensor networks (WSN) hold numerous battery operated, compact sized, and inexpensive sensor nodes, which are commonly employed to observe the physical parameters in the target environment. As the sensor nodes undergo arbitrary placement in the open areas, there is a higher possibility of affected by distinct kinds of attacks. For resolving the issue, intrusion detection system (IDS) is developed. This paper presents a new optimal Support Vector Machine (OSVM) based IDS in WSN. The presented OSVM model involves the proficient selection of optimal kernels in the SVM model using whale optimization algorithm (WOA) for intrusion detection. Since the SVM kernel gets altered using WOA, the application of OSVM model can be used for the detection of intrusions with proficient results. The performance of the OSVM model has been investigated on the benchmark NSL KDDCup 99 dataset. The resultant simulation values portrayed the effectual results of the OSVM model by obtaining a superior accuracy of 94.09% and detection rate of 95.02%.

Securing various data types such as text, image, and video is needed in real-time communications. The transmission of data over an insecure channel is a permanent challenge, especially in mass Internet applications. Preserving confidentiality and integrity of data toward malicious attacks, accidental devastation, change during transfer, or while in storage must be improved. Data Encryption Standard (DES) is considered as a symmetric-key algorithm that is most widely used for various security purposes. In this work, a Key-based Enhancement of the DES (KE-DES) technique for securing text is proposed. The KEDES is implemented by the application of two steps: the first is merging the Odd/Even bit transformation of every key bit in the DES algorithm. The second step is replacing the right-side expansion of the original DES by using Key-Distribution (K-D) function. The K-D allocation consists of 8-bits from Permutation Choice-1 (PC-1) key outcome. The next 32-bits outcomes from the right-side of data, there is also 8-bits outcome from Permutation Choice-2 (PC-2) in each round. The key and data created randomly, in this case, provide adequate security and the KEDES model is considered more efficient for text encryption.

In this work, a novel approach to detect and diagnose single and combined faults in the Induction Motor (IM) is proposed. In Condition Monitoring Systems (CMS) based on the Motor Current Signature Analysis (MCSA), the simultaneous occurrence of multiple faults is a major challenge. An innovative technique called Multiple Windowed Harmonic Wavelet Packet Transform (MWHWPT) is used in order to discriminate between the faulty components of the IM, even during compound faults. Thus, each motor component is monitored by a specific Fault Index (FI) which allows the fault diagnosis without the need for a classifier. The tests carried on Rotor and Bearing faults show high fault diagnosis rate even during compound faults and proves the competitive performance of the proposed approach with literature works.

Cloud Computing is a technology which provides flexibility through scalability. Like, Cloud computing, nowadays, Fog computing is considered more revolutionary and dynamic technology. But the main problem with the Fog computing is to take care of its security as in this also person identification is done by single Sign-In system. To come out from the security problem raised in Fog computing, an innovative approach has been suggested here. In the present paper, an approach has been proposed that combines different biometric techniques to verify the authenticity of a person and provides a complete model that will be able to provide a necessary level of verification and security in fog computing. In this model, several biometric techniques have been used and each one of them individually helps extract out more authentic and detailed information after every step. Further, in the presented paper, different techniques and methodologies have been examined to assess the usefulness of proposed technology in reducing the security threats. The paper delivers a capacious technique for biometric authentication for bolstering the fog security.

In this paper, we propose models and schemes for coded and uncoded packet transmission over time invariant (TIC) and time variant (TVC) channels. We provide an approximation of the delay induced assuming fmite number of time slots to transmit a given number of packets. We propose an adaptive physical layer (PHY)-aware coded scheme that designs smart acknowledgments (ACK) via an optimal selection of coded packets to transmit at a given SNR. We apply our proposed schemes to channels with complex fading behavior and high round trip (RTT) delays. We compare the accuracy of TVC coded scheme to the TIC coded scheme, and we show the throughput-delay efficacy of adaptive coded schemes driven by PHY-awareness in the mitigation of high RTT environments, with up to 3 fold gains.

In addition to the feature of real-time analytics, fog computing allows detection nodes to be located at the edges of the network. On the other hand, intrusion detection systems require prompt and accurate attack analysis and detection. These systems must promptly respond appropriately to an event. Increasing the speed of data transfer and response requires less bandwidth in the network, reducing the data sent to the cloud and increasing information security as some of the advantages of using detection nodes at the edges of the network in fog computing. The use of neural networks in the analyzer engine is important for the low consumption of system resources, avoidance of explicit production of detection rules, detection of known deformed attacks, and the ability to manage noise and outlier data. The current paper proposes and implements the architecture of network intrusion detection nodes in fog computing, in addition to presenting the proposed fog network architecture. In the proposed architecture, each node can, in addition to performing intrusion detection operations, observe the nodes around it, find the compromised node or intrusion node, and inform the nodes close to it to disconnect from that node.

Network intrusion detection is an important research direction of network security. The diversification of network intrusion mode and the increasing amount of network data make the traditional detection methods can not meet the requirements of the current network environment. The development of deep learning technology and its successful application in the field of artificial intelligence provide a new solution for network intrusion detection. In this paper, the convolutional neural network in deep learning is applied to network intrusion detection, and an intelligent detection model which can actively learn is established. The experiment on KDD99 data set shows that it can effectively improve the accuracy and adaptive ability of intrusion detection, and has certain effectiveness and advancement.

In this paper, we study a wireless powered cellular network (WPCN) supported with network coding capability. In particular, we consider a network consisting of k cellular users (CUs) served by a hybrid access point (HAP) that takes over energy transfer to the users on top of information transmission over both the uplink (UL) and downlink (DL). Each CU has k+1 states representing its communication behavior, and collectively are referred to as the user demand profile. Opportunistically, when the CUs have information to be exchanged through the HAP, it broadcasts this information in coded format to the exchanging pairs, resulting in saving time slots over the DL. These saved slots are then utilized by the HAP to prolong the network lifetime and enhance the network throughput. We quantify, analytically, the performance gain of our network-coded WPCN over the conventional one, that does not employ network coding, in terms of network lifetime and throughput. We consider the two extreme cases of using all the saved slots either for energy boosting or throughput enhancement. In addition, a lifetime/throughput optimization is carried out by the HAP for balancing the saved slots assignment in an optimized fashion, where the problem is formulated as a mixed-integer linear programming optimization problem. Numerical results exhibit the network performance gains from the lifetime and throughput perspectives, for a uniform user demand profile across all CUs. Moreover, the effect of biasing the user demand profile of some CUs in the network reveals considerable improvement in the network performance gains.

With the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.

The emergence of the Internet of Things (IoT) is not only a global revolution in the information industry, but also brought tremendous changes to our lives. With the development of the technology and means of the IoT, information security issues have gradually emerged, and intrusion attacks have become one of the main problems of the IoT network security. The network layer of the IoT is the key connecting the platform and sensors or controllers of the IoT, and it is also the most standardized, the strongest and the most mature part of the whole physical network architecture. Its large-scale development has led to the network layer's security issues will receive more attention and face more challenges. This paper proposes an intrusion detection algorithm deployed on the network layer of the IoT, which uses the BPSO algorithm to extract features from the NSL-KDD dataset, and applies support vector machines (SVM) as the core model of the algorithm to detect and identify abnormal data, especially DoS attacks. Experimental results show that the model's detection rate of abnormal data and DoS attacks are significantly improved.

Network function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to “named information” or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures.