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2021-03-15
Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., Sharma, R..  2020.  CrypTFlow: Secure TensorFlow Inference. 2020 IEEE Symposium on Security and Privacy (SP). :336–353.
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as ResNet50 and DenseNet121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.
2021-03-04
Abedin, N. F., Bawm, R., Sarwar, T., Saifuddin, M., Rahman, M. A., Hossain, S..  2020.  Phishing Attack Detection using Machine Learning Classification Techniques. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). :1125—1130.

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.

2021-02-15
Zhu, L., Zhou, X., Zhang, X..  2020.  A Reversible Meaningful Image Encryption Scheme Based on Block Compressive Sensing. 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP). :326–330.
An efficient and reversible meaningful image encryption scheme is proposed in this paper. The plain image is first compressed and encrypted simultaneously by Adaptive Block Compressive Sensing (ABCS) framework to create a noise-like secret image. Next, Least Significant Bit (LSB) embedding is employed to embed the secret image into a carrier image to generate the final meaningful cipher image. In this scheme, ABCS improves the compression and efficiency performance, and the embedding and extraction operations are absolutely reversible. The simulation results and security analyses are presented to demonstrate the effectiveness, compression, secrecy of the proposed scheme.
2021-02-08
Bhoi, G., Bhavsar, R., Prajapati, P., Shah, P..  2020.  A Review of Recent Trends on DNA Based Cryptography. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). :815–822.
One of the emerging methodologies nowadays in the field of cryptography based on human DNA sequences. As the research says that even a limited quantity of DNA can store gigantic measure of information likewise DNA can process and transmit the information, such potential of DNA give rise to the idea of DNA cryptography. A synopsis of the research carried out in DNA based security presented in this paper. Included deliberation contain encryption algorithms based on random DNA, chaotic systems, polymerase chain reaction, coupled map lattices, and other common encryption algorithms. Purpose of algorithms are specific or general as some of them are only designed to encrypt the images or more specific images like medical images or text data and others designed to use it as general for images and text data. We discussed divergent techniques that proposed earlier based on random sample DNA, medical image encryption, image encryption, and cryptanalysis done on various algorithms. With the help of this paper, one can understand the existing algorithms and can design a DNA based encryption algorithm.
2021-01-22
Xu, H., Jiang, H..  2019.  An Image Encryption Schema Based on Hybrid Optimized Chaotic System. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :784–788.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

2020-11-02
Kadhim, H., Hatem, M. A..  2019.  Secure Data Packet in MANET Based Chaos-Modified AES Algorithm. 2019 2nd International Conference on Engineering Technology and its Applications (IICETA). :208–213.
Security is one of the more challenging problem for wireless Ad-Hoc networks specially in MANT due their features like dynamic topology, no centralized infrastructure, open architecture, etc. that make its more prone to different attacks. These attacks can be passive or active. The passive attack it hard to detect it in the network because its targets the confidential of data packet by eavesdropping on it. Therefore, the privacy preservation for data packets payload which it transmission over MANET has been a major part of concern. especially for safety-sensitive applications such as, privacy conference meetings, military applications, etc. In this paper it used symmetric cryptography to provide privacy for data packet by proposed modified AES based on five proposed which are: Key generation based on multi chaotic system, new SubByte, new ShiftRows, Add-two-XOR, Add-Shiftcycl.
Xiong, Wenjie, Shan, Chun, Sun, Zhaoliang, Meng, Qinglei.  2018.  Real-time Processing and Storage of Multimedia Data with Content Delivery Network in Vehicle Monitoring System. 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM). :1—4.

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.

2020-09-21
Farrag, Sara, Alexan, Wassim, Hussein, Hisham H..  2019.  Triple-Layer Image Security Using a Zigzag Embedding Pattern. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). :1–8.
This paper proposes a triple-layer, high capacity, message security scheme. The first two layers are of a cryptographic nature, whereas the third layer is of a steganographic nature. In the first layer, AES-128 encryption is performed on the secret message. In the second layer, a chaotic logistic map encryption is applied on the output of the first secure layer to increase the security of the scheme. In the third layer of security, a 2D image steganography technique is performed, where the least significant bit (LSB) -embedding is done according to a zigzag pattern in each of the three color planes of the cover image (i.e. RGB). The distinguishing feature of the proposed scheme is that the secret data is hidden in a zigzag manner that cannot be predicted by a third party. Moreover, our scheme achieves higher values of peak signal to noise ratio (PPSNR), mean square error (MSE), the structural similarity index metric (SSIM), normal cross correlation (NCC) and image fidelity (IF) compared to its counterparts form the literature. In addition, a histogram analysis as well as the high achieved capacity are magnificent indicators for a reliable and high capacity steganographic scheme.
2020-09-08
Xu, Hong-Li, JIANG, HongHua.  2019.  An Image Encryption Schema Based on Hybrid Optimized Chaotic System. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :784–788.
The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.
2020-09-04
Li, Chengqing, Feng, Bingbing, Li, Shujun, Kurths, Jüergen, Chen, Guanrong.  2019.  Dynamic Analysis of Digital Chaotic Maps via State-Mapping Networks. IEEE Transactions on Circuits and Systems I: Regular Papers. 66:2322—2335.
Chaotic dynamics is widely used to design pseudo-random number generators and for other applications, such as secure communications and encryption. This paper aims to study the dynamics of the discrete-time chaotic maps in the digital (i.e., finite-precision) domain. Differing from the traditional approaches treating a digital chaotic map as a black box with different explanations according to the test results of the output, the dynamical properties of such chaotic maps are first explored with a fixed-point arithmetic, using the Logistic map and the Tent map as two representative examples, from a new perspective with the corresponding state-mapping networks (SMNs). In an SMN, every possible value in the digital domain is considered as a node and the mapping relationship between any pair of nodes is a directed edge. The scale-free properties of the Logistic map's SMN are proved. The analytic results are further extended to the scenario of floating-point arithmetic and for other chaotic maps. Understanding the network structure of a chaotic map's SMN in digital computers can facilitate counteracting the undesirable degeneration of chaotic dynamics in finite-precision domains, also helping to classify and improve the randomness of pseudo-random number sequences generated by iterating the chaotic maps.
Jing, Huiyun, Meng, Chengrui, He, Xin, Wei, Wei.  2019.  Black Box Explanation Guided Decision-Based Adversarial Attacks. 2019 IEEE 5th International Conference on Computer and Communications (ICCC). :1592—1596.
Adversarial attacks have been the hot research field in artificial intelligence security. Decision-based black-box adversarial attacks are much more appropriate in the real-world scenarios, where only the final decisions of the targeted deep neural networks are accessible. However, since there is no available guidance for searching the imperceptive adversarial perturbation, boundary attack, one of the best performing decision-based black-box attacks, carries out computationally expensive search. For improving attack efficiency, we propose a novel black box explanation guided decision-based black-box adversarial attack. Firstly, the problem of decision-based adversarial attacks is modeled as a derivative-free and constraint optimization problem. To solve this optimization problem, the black box explanation guided constrained random search method is proposed to more quickly find the imperceptible adversarial example. The insights into the targeted deep neural networks explored by the black box explanation are fully used to accelerate the computationally expensive random search. Experimental results demonstrate that our proposed attack improves the attack efficiency by 64% compared with boundary attack.
2020-07-16
Ayub, Md. Ahsan, Smith, Steven, Siraj, Ambareen.  2019.  A Protocol Independent Approach in Network Covert Channel Detection. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :165—170.

Network covert channels are used in various cyberattacks, including disclosure of sensitive information and enabling stealth tunnels for botnet commands. With time and technology, covert channels are becoming more prevalent, complex, and difficult to detect. The current methods for detection are protocol and pattern specific. This requires the investment of significant time and resources into application of various techniques to catch the different types of covert channels. This paper reviews several patterns of network storage covert channels, describes generation of network traffic dataset with covert channels, and proposes a generic, protocol-independent approach for the detection of network storage covert channels using a supervised machine learning technique. The implementation of the proposed generic detection model can lead to a reduction of necessary techniques to prevent covert channel communication in network traffic. The datasets we have generated for experimentation represent storage covert channels in the IP, TCP, and DNS protocols and are available upon request for future research in this area.

2020-07-03
Bhandari, Chitra, Kumar, Sumit, Chauhan, Sudha, Rahman, M A, Sundaram, Gaurav, Jha, Rajib Kumar, Sundar, Shyam, Verma, A R, Singh, Yashvir.  2019.  Biomedical Image Encryption Based on Fractional Discrete Cosine Transform with Singular Value Decomposition and Chaotic System. 2019 International Conference on Computing, Power and Communication Technologies (GUCON). :520—523.

In this paper, new image encryption based on singular value decomposition (SVD), fractional discrete cosine transform (FrDCT) and the chaotic system is proposed for the security of medical image. Reliability, vitality, and efficacy of medical image encryption are strengthened by it. The proposed method discusses the benefits of FrDCT over fractional Fourier transform. The key sensitivity of the proposed algorithm for different medical images inspires us to make a platform for other researchers. Theoretical and statistical tests are carried out demonstrating the high-level security of the proposed algorithm.

2020-06-22
Das, Subhajit, Mondal, Satyendra Nath, Sanyal, Manas.  2019.  A Novel Approach of Image Encryption Using Chaos and Dynamic DNA Sequence. 2019 Amity International Conference on Artificial Intelligence (AICAI). :876–880.
In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.
2020-05-11
Cui, Zhicheng, Zhang, Muhan, Chen, Yixin.  2018.  Deep Embedding Logistic Regression. 2018 IEEE International Conference on Big Knowledge (ICBK). :176–183.
Logistic regression (LR) is used in many areas due to its simplicity and interpretability. While at the same time, those two properties limit its classification accuracy. Deep neural networks (DNNs), instead, achieve state-of-the-art performance in many domains. However, the nonlinearity and complexity of DNNs make it less interpretable. To balance interpretability and classification performance, we propose a novel nonlinear model, Deep Embedding Logistic Regression (DELR), which augments LR with a nonlinear dimension-wise feature embedding. In DELR, each feature embedding is learned through a deep and narrow neural network and LR is attached to decide feature importance. A compact and yet powerful model, DELR offers great interpretability: it can tell the importance of each input feature, yield meaningful embedding of categorical features, and extract actionable changes, making it attractive for tasks such as market analysis and clinical prediction.
2020-03-23
Unnikrishnan, Grieshma, Mathew, Deepa, Jose, Bijoy A., Arvind, Raju.  2019.  Hybrid Route Recommender System for Smarter Logistics. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :239–244.
The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.
2020-03-12
Liang, Shiaofang, Li, Mingchen, Li, Wenjing.  2019.  Research on Traceability Algorithm of Logistics Service Transaction Based on Blockchain. 2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :186–189.

The traditional logistics transaction lacks a perfect traceability mechanism, and the data information's integrity and safety are not guaranteed in the existing traceability system. In order to solve the problem of main body responsibility caused by the participation of many stakeholders and the uncompleted supervision system in the process of logistics service transaction, This paper proposes a traceability algorithm for logistics service transactions based on blockchain. Based on the logistics service supply chain and alliance chain, the paper firstly investigates the traditional logistics service supply chain, analyzes the existing problems, and combines the structural characteristics of the blockchain to propose a decentralized new logistics service supply chain concept model based on blockchain. Then, using Globe sandara 1 to standardize the physical products and data circulating in the new logistics service supply chain, form unified and standard traceable data, and propose a multi-dimensional traceable data model based on logistics service supply chain. Based on the proposed model, combined with the business process of the logistics service supply chain and asymmetric encryption, a blockchain-based logistics service transaction traceability algorithm is designed. Finally, the simulation results show that the algorithm realizes the end-to-end traceability of the logistics service supply chain, and the service transaction is transparent while ensuring the integrity and security of the data.

Wu, Hanqing, Cao, Jiannong, Yang, Yanni, Tung, Cheung Leong, Jiang, Shan, Tang, Bin, Liu, Yang, Wang, Xiaoqing, Deng, Yuming.  2019.  Data Management in Supply Chain Using Blockchain: Challenges and a Case Study. 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1–8.

Supply chain management (SCM) is fundamental for gaining financial, environmental and social benefits in the supply chain industry. However, traditional SCM mechanisms usually suffer from a wide scope of issues such as lack of information sharing, long delays for data retrieval, and unreliability in product tracing. Recent advances in blockchain technology show great potential to tackle these issues due to its salient features including immutability, transparency, and decentralization. Although there are some proof-of-concept studies and surveys on blockchain-based SCM from the perspective of logistics, the underlying technical challenges are not clearly identified. In this paper, we provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based SCM systems. We summarize and discuss four crucial technical challenges in terms of scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing blockchain-based food traceability system is reported to provide more insights on how to tackle these technical challenges in practice.

2020-02-26
Vlachokostas, Alex, Prousalidis, John, Spathis, Dimosthenis, Nikitas, Mike, Kourmpelis, Theo, Dallas, Stefanos, Soghomonian, Zareh, Georgiou, Vassilis.  2019.  Ship-to-Grid Integration: Environmental Mitigation and Critical Infrastructure Resilience. 2019 IEEE Electric Ship Technologies Symposium (ESTS). :542–547.

The United States and European Union have an increasing number of projects that are engaging end-use devices for improved grid capabilities. Areas such as building-to-grid and vehicle-to-grid are simple examples of these advanced capabilities. In this paper, we present an innovative concept study for a ship-to-grid integration. The goal of this study is to simulate a two-way power flow between ship(s) and the grid with GridLAB-D for the port of Kyllini in Greece, where a ship-to-shore interconnection was recently implemented. Extending this further, we explore: (a) the ability of ships to meet their load demand needs, while at berth, by being supplied with energy from the electric grid and thus powering off their diesel engines; and (b) the ability of ships to provide power to critical loads onshore. As a result, the ship-to-grid integration helps (a) mitigate environmental pollutants from the ships' diesel engines and (b) provide resilience to nearby communities during a power disruption due to natural disasters or man-made threats.

2020-01-21
Han, Danyang, Yu, Jinsong, Song, Yue, Tang, Diyin, Dai, Jing.  2019.  A Distributed Autonomic Logistics System with Parallel-Computing Diagnostic Algorithm for Aircrafts. 2019 IEEE AUTOTESTCON. :1–8.
The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.
Le, Duc C., Nur Zincir-Heywood, A..  2019.  Machine Learning Based Insider Threat Modelling and Detection. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :1–6.

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.

2019-12-10
Ponuma, R, Amutha, R, Haritha, B.  2018.  Compressive Sensing and Hyper-Chaos Based Image Compression-Encryption. 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). :1-5.

A 2D-Compressive Sensing and hyper-chaos based image compression-encryption algorithm is proposed. The 2D image is compressively sampled and encrypted using two measurement matrices. A chaos based measurement matrix construction is employed. The construction of the measurement matrix is controlled by the initial and control parameters of the chaotic system, which are used as the secret key for encryption. The linear measurements of the sparse coefficients of the image are then subjected to a hyper-chaos based diffusion which results in the cipher image. Numerical simulation and security analysis are performed to verify the validity and reliability of the proposed algorithm.

2019-10-08
Tripathi, S. K., Pandian, K. K. S., Gupta, B..  2018.  Hardware Implementation of Dynamic Key Value Based Stream Cipher Using Chaotic Logistic Map. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). :1104–1108.

In the last few decades, the relative simplicity of the logistic map made it a widely accepted point in the consideration of chaos, which is having the good properties of unpredictability, sensitiveness in the key values and ergodicity. Further, the system parameters fit the requirements of a cipher widely used in the field of cryptography, asymmetric and symmetric key chaos based cryptography, and for pseudorandom sequence generation. Also, the hardware-based embedded system is configured on FPGA devices for high performance. In this paper, a novel stream cipher using chaotic logistic map is proposed. The two chaotic logistic maps are coded using Verilog HDL and implemented on commercially available FPGA hardware using Xilinx device: XC3S250E for the part: FT256 and operated at frequency of 62.20 MHz to generate the non-recursive key which is used in key scheduling of pseudorandom number generation (PRNG) to produce the key stream. The realization of proposed cryptosystem in this FPGA device accomplishes the improved efficiency equal to 0.1186 Mbps/slice. Further, the generated binary sequence from the experiment is analyzed for X-power, thermal analysis, and randomness tests are performed using NIST statistical.

2019-08-05
Kaiafas, G., Varisteas, G., Lagraa, S., State, R., Nguyen, C. D., Ries, T., Ourdane, M..  2018.  Detecting Malicious Authentication Events Trustfully. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1-6.

Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.

2019-06-10
Farooq, H. M., Otaibi, N. M..  2018.  Optimal Machine Learning Algorithms for Cyber Threat Detection. 2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim). :32-37.

With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.