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Jyotiyana, D., Saxena, V. P..  2016.  Fault attack for scalar multiplication over finite field (E(Fq)) on Elliptic Curve Digital Signature Algorithm. 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE). :1–4.

Elliptic Curve Cryptosystems are very much delicate to attacks or physical attacks. This paper aims to correctly implementing the fault injection attack against Elliptic Curve Digital Signature Algorithm. More specifically, the proposed algorithm concerns to fault attack which is implemented to sufficiently alter signature against vigilant periodic sequence algorithm that supports the efficient speed up and security perspectives with most prominent and well known scalar multiplication algorithm for ECDSA. The purpose is to properly injecting attack whether any probable countermeasure threatening the pseudo code is determined by the attack model according to the predefined methodologies. We show the results of our experiment with bits acquire from the targeted implementation to determine the reliability of our attack.

Jyothi, R., Cholli, Nagaraj G..  2019.  New Approach to Secure Cluster Heads in Wireless Sensor Networks. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :1097–1101.
This Wireless Sensor Network is a network of devices that communicates the information gathered from a monitored field through wireless links. Small size sensor nodes constitute wireless sensor networks. A Sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. Applications of wireless sensor networks include home automation, street lighting, military, healthcare and industrial process monitoring. As wireless sensor networks are distributed across large geographical area, these are vulnerable to various security threats. This affects the performance of the wireless sensor networks. The impact of security issues will become more critical if the network is used for mission-critical applications like tactical battlefield. In real life deployment scenarios, the probability of failure of nodes is more. As a result of resource constraints in the sensor nodes, traditional methods which involve large overhead computation and communication are not feasible in WSNs. Hence, design and deployment of secured WSNs is a challenging task. Attacks on WSNs include attack on confidentiality, integrity and availability. There are various types of architectures that are used to deploy WSNs. Some of them are data centric, hierarchical, location based, mobility based etc. This work discusses the security issue of hierarchical architecture and proposes a solution. In hierarchical architectures, sensor nodes are grouped to form clusters. Intra-cluster communication happens through cluster heads. Cluster heads also facilitate inter-cluster communication with other cluster heads. Aggregation of data generated by sensor nodes is done by cluster heads. Aggregated data also get transferred to base through multi-hop approach in most cases. Cluster heads are vulnerable to various malicious attacks and this greatly affects the performance of the wireless sensor network. The proposed solution identifies attacked cluster head and changes the CH by identifying the fittest node using genetic algorithm based search.
Juzi Zhao, Subramaniam, S., Brandt-Pearce, M..  2014.  Intradomain and interdomain QoT-aware RWA for translucent optical networks. Optical Communications and Networking, IEEE/OSA Journal of. 6:536-548.

Physical impairments in long-haul optical networks mandate that optical signals be regenerated within the (so-called translucent) network. Being expensive devices, regenerators are expected to be allocated sparsely and must be judiciously utilized. Next-generation optical-transport networks will include multiple domains with diverse technologies, protocols, granularities, and carriers. Because of confidentiality and scalability concerns, the scope of network-state information (e.g., topology, wavelength availability) may be limited to within a domain. In such networks, the problem of routing and wavelength assignment (RWA) aims to find an adequate route and wavelength(s) for lightpaths carrying end-to-end service demands. Some state information may have to be explicitly exchanged among the domains to facilitate the RWA process. The challenge is to determine which information is the most critical and make a wise choice for the path and wavelength(s) using the limited information. Recently, a framework for multidomain path computation called backward-recursive path-computation (BRPC) was standardized by the Internet Engineering Task Force. In this paper, we consider the RWA problem for connections within a single domain and interdomain connections so that the quality of transmission (QoT) requirement of each connection is satisfied, and the network-level performance metric of blocking probability is minimized. Cross-layer heuristics that are based on dynamic programming to effectively allocate the sparse regenerators are developed, and extensive simulation results are presented to demonstrate their effectiveness.

 

Juyal, S., Sharma, S., Harbola, A., Shukla, A. S..  2020.  Privacy and Security of IoT based Skin Monitoring System using Blockchain Approach. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1—5.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

Juuti, Mika, Szyller, Sebastian, Marchal, Samuel, Asokan, N..  2019.  PRADA: Protecting Against DNN Model Stealing Attacks. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :512–527.
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives.
Juszczyszyn, Krzysztof, Kolaczek, Grzegorz.  2019.  Complex Networks Monitoring and Security and Fraud Detection for Enterprises. 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :124–125.
The purpose of Complex Networks Monitoring and Security and Fraud Detection for Enterprises - CoNeSec track is two-fold: Firstly, the track offers a forum for scientists and engineers to exchange ideas on novel analytical techniques using network log data. Secondly, the track has a thematic focus on emerging technology for complex network, security and privacy. We seek publications on all theoretical and practical work in areas related to the theme above.
Juretus, Kyle, Savidis, Ioannis.  2019.  Increasing the SAT Attack Resiliency of In-Cone Logic Locking. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.

A method to increase the resiliency of in-cone logic locking against the SAT attack is described in this paper. Current logic locking techniques provide protection through the addition of circuitry outside of the original logic cone. While the additional circuitry provides provable security against the SAT attack, other attacks, such as the removal attack, limit the efficacy of such techniques. Traditional in-cone logic locking is not prone to removal attacks, but is less secure against the SAT attack. The focus of this paper is, therefore, the analysis of in-cone logic locking to increase the security against the SAT attack, which provides a comparison between in-cone techniques and newly developed methodologies. A novel algorithm is developed that utilizes maximum fanout free cones (MFFC). The application of the algorithm limits the fanout of incorrect key information. The MFFC based algorithm resulted in an average increase of 61.8% in the minimum number of iterations required to complete the SAT attack across 1,000 different variable orderings of the circuit netlist while restricted to a 5% overhead in area.

Juretus, Kyle, Savidis, Ioannis.  2016.  Reduced Overhead Gate Level Logic Encryption. Proceedings of the 26th Edition on Great Lakes Symposium on VLSI. :15–20.

Untrusted third-parties are found throughout the integrated circuit (IC) design flow resulting in potential threats in IC reliability and security. Threats include IC counterfeiting, intellectual property (IP) theft, IC overproduction, and the insertion of hardware Trojans. Logic encryption has emerged as a method of enhancing security against such threats, however, current implementations of logic encryption, including the XOR or look-up table (LUT) techniques, have high per-gate overheads in area, performance, and power. A novel gate level logic encryption technique with reduced per-gate overheads is described in this paper. In addition, a technique to expand the search space of a key sequence is provided, increasing the difficulty for an adversary to extract the key value. A power reduction of 41.50%, an estimated area reduction of 43.58%, and a performance increase of 34.54% is achieved when using the proposed gate level logic encryption instead of the LUT based technique for an encrypted AND gate.

Jurado, Mireya, Smith, Geoffrey.  2019.  Quantifying Information Leakage of Deterministic Encryption. Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop. :129–139.
In order to protect user data while maintaining application functionality, encrypted databases can use specialized cryptography such as property-revealing encryption, which allows a property of the underlying plaintext values to be computed from the ciphertext. One example is deterministic encryption which ensures that the same plaintext encrypted under the same key will produce the same ciphertext. This technology enables clients to make queries on sensitive data hosted in a cloud server and has considerable potential to protect data. However, the security implications of deterministic encryption are not well understood. We provide a leakage analysis of deterministic encryption through the application of the framework of quantitative information flow. A key insight from this framework is that there is no single "right'' measure by which leakage can be quantified: information flow depends on the operational scenario and different operational scenarios require different leakage measures. We evaluate leakage under three operational scenarios, modeled using three different gain functions, under a variety of prior distributions in order to bring clarity to this problem.
Junwei Wang, Haifeng Wang.  2014.  Trust-based QoS routing algorithm for Wireless Sensor Networks. Control and Decision Conference (2014 CCDC), The 26th Chinese. :2492-2495.

With the rapid development of Wireless Sensor Networks (WSNs), besides the energy efficient, Quality of Service (QoS) supported and the validity of packet transmission should be considered under some circumstances. In this paper, according to summing up LEACH protocol's advantages and defects, combining with trust evaluation mechanism, energy and QoS control, a trust-based QoS routing algorithm is put forward. Firstly, energy control and coverage scale are adopted to keep load balance in the phase of cluster head selection. Secondly, trust evaluation mechanism is designed to increase the credibility of the network in the stage of node clusting. Finally, in the period of information transmission, verification and ACK mechanism also put to guarantee validity of data transmission. In this paper, it proposes the improved protocol. The improved protocol can not only prolong nodes' life expectancy, but also increase the credibility of information transmission and reduce the packet loss. Compared to typical routing algorithms in sensor networks, this new algorithm has better performance.

Junosza-Szaniawski, K., Nogalski, D., Wójcik, A..  2020.  Exact and approximation algorithms for sensor placement against DDoS attacks. 2020 15th Conference on Computer Science and Information Systems (FedCSIS). :295–301.
In DDoS attack (Distributed Denial of Service), an attacker gains control of many network users by a virus. Then the controlled users send many requests to a victim, leading to lack of its resources. DDoS attacks are hard to defend because of distributed nature, large scale and various attack techniques. One of possible ways of defense is to place sensors in the network that can detect and stop an unwanted request. However, such sensors are expensive so there is a natural question about a minimum number of sensors and their optimal placement to get the required level of safety. We present two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact linear programming solvers.
Junnarkar, Aparna A., Singh, Y. P., Deshpande, Vivek S..  2018.  SQMAA: Security, QoS and Mobility Aware ACO Based Opportunistic Routing Protocol for MANET. 2018 4th International Conference for Convergence in Technology (I2CT). :1–6.
The QoS performance of MANET routing protocols is significantly affected by the mobility conditions in network. Secondly, as MANET open nature network, there is strong possibility of different types of vulnerabilities such as blackhole attack, malicious attack, DoS attacks etc. In this research work, we are designing the novel opportunistic routing protocol in order to address the challenges of network security as well as QoS improvement. There two algorithms designed in this paper. First we proposed and designed novel QoS improvement algorithm based on optimization scheme called Ant Colony Optimization (ACO) with swarm intelligence approach. This proposed method used the RSSI measurements to determine the distance between two mobile nodes in order to select efficient path for communication. This new routing protocol is named as QoS Mobility Aware ACO (QMAA) Routing Protocol. Second, we designed security algorithm for secure communication and user's authentication in MANET under the presence attackers in network. With security algorithm the QoS aware protocol is proposed named as Secure-QMAA (SQMAA). The SQMAA achieved secure communications while guaranteed QoS performance against existing routing protocols. The simulation results shows that under the presence of malicious attackers, the performance of SQMAA are efficient as compared to QMAA and state-of-art routing protocol.
Junjie, Jia, Haitao, Qin, Wanghu, Chen, Huifang, Ma.  2019.  Trajectory Anonymity Based on Quadratic Anonymity. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :485—492.
Due to the leakage of privacy information in the sensitive region of trajectory anonymity publishing, which is resulted by the attack, this paper aims at the trajectory anonymity algorithm of division of region. According to the start stop time of the trajectory, the current sensitive region is found with the k-anonymity set on the synchronous trajectory. If the distance between the divided sub-region and the adjacent anonymous area is not greater than the threshold d, the area will be combined. Otherwise, with the guidance of location mapping, the forged location is added to the sub-region according to the original location so that the divided sub-region can meet the principle of k-anonymity. While the forged location retains the relative position of each point in the sensitive region, making that the divided sub-region and the original Regional anonymity are consistent. Experiments show that compared with the existing trajectory anonymous algorithm and the synchronous trajectory data set with the same privacy, the algorithm is highly effective in both privacy protection and validity of data quality.
Junho Hong, Chen-Ching Liu, Govindarasu, M..  2014.  Integrated Anomaly Detection for Cyber Security of the Substations. Smart Grid, IEEE Transactions on. 5:1643-1653.

Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.

Junho Hong, Chen-Ching Liu, Govindarasu, M..  2014.  Detection of cyber intrusions using network-based multicast messages for substation automation. Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES. :1-5.

This paper proposes a new network-based cyber intrusion detection system (NIDS) using multicast messages in substation automation systems (SASs). The proposed network-based intrusion detection system monitors anomalies and malicious activities of multicast messages based on IEC 61850, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV). NIDS detects anomalies and intrusions that violate predefined security rules using a specification-based algorithm. The performance test has been conducted for different cyber intrusion scenarios (e.g., packet modification, replay and denial-of-service attacks) using a cyber security testbed. The IEEE 39-bus system model has been used for testing of the proposed intrusion detection method for simultaneous cyber attacks. The false negative ratio (FNR) is the number of misclassified abnormal packets divided by the total number of abnormal packets. The results demonstrate that the proposed NIDS achieves a low fault negative rate.
 

Junho Hong, Chen-Ching Liu, Govindarasu, M..  2014.  Integrated Anomaly Detection for Cyber Security of the Substations. Smart Grid, IEEE Transactions on. 5:1643-1653.

Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.
 

Junguo Liao, Mingyan Wang.  2014.  A new dynamic updating key strategy based on EMSA in wireless mesh networks. Information and Communications Technologies (ICT 2014), 2014 International Conference on. :1-5.

In the security protocols of Efficient Mesh Security Association(EMSA), the key updating strategy is an effective method to ensure the security of communication. For the existing strategy of periodic automatic key updating, the PTK(Pairwise Transit Key) is updated through the complex 4-way handshake to produce each time. Once the update frequency of the PTK is faster, it will have a greater impact on throughput and delay of the network. On this basis, we propose a new strategy of dynamic key updating to ensure the safety and performance of wireless mesh networks. In the new strategy, mesh point(MP) and mesh authenticator(MA) negotiate a random function at the initial certification, and use the PTK which is generated by the 4-way handshake as the initial seed. When the PTK updating cycle comes, both sides generate the new keys using the random function, which do not have to generate a new PTK by complex 4-way handshake. The analysis of performance compared with existing strategies showed that the dynamic key updating strategy proposed in this paper have a larger increase in delay and throughput of the network.

Jungum, Nevin Vunka, Mohamudally, Nawaz, Nissanke, Nimal.  2020.  Device Selection Decision Making using Multi-Criteria for Offloading Application Mobile Codes. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :326–331.
With fast growing research in the area of application partitioning for offloading, determining which devices to prioritize over the other for mobile code offloading is fundamental. Multiple methods can be adopted using both single-criterion and multiple-criteria strategies. Due to the characteristics of pervasive environments, whereby devices having different computing capability, different level of privacy and security and the mobility nature in such environment makes the decision-making process complex. To this end, this paper proposes a method using a combination of the method Analytic Hierarchy Process (AHP) to calculate weights criteria of participating devices. Next the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is considered to sort in order of priority the participating devices, hence facilitating the decision to opt for which participating device first. An evaluation of the method is also presented.
Jung, Taebo, Jung, Kangsoo, Park, Sehwa, Park, Seog.  2017.  A noise parameter configuration technique to mitigate detour inference attack on differential privacy. 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). :186–192.

Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information from the database itself. It is well known for protecting the privacy from arbitrary attack. However, recent research argues that there is a several ways to infer sensitive information from data although the differential privacy is applied. One of this inference method is to use the correlation between the data. In this paper, we investigate the new privacy threats using attribute correlation which are not covered by traditional studies and propose a privacy preserving technique that configures the differential privacy's noise parameter to solve this new threat. In the experiment, we show the weaknesses of traditional differential privacy method and validate that the proposed noise parameter configuration method provide a sufficient privacy protection and maintain an accuracy of data utility.

Jung, M. Y., Jang, J. W..  2017.  Data management and searching system and method to provide increased security for IoT platform. 2017 International Conference on Information and Communication Technology Convergence (ICTC). :873–878.

Existing data management and searching system for Internet of Things uses centralized database. For this reason, security vulnerabilities are found in this system which consists of server such as IP spoofing, single point of failure and Sybil attack. This paper proposes data management system is based on blockchain which ensures security by using ECDSA digital signature and SHA-256 hash function. Location that is indicated as IP address of data owner and data name are transcribed in block which is included in the blockchain. Furthermore, we devise data manegement and searching method through analyzing block hash value. By using security properties of blockchain such as authentication, non-repudiation and data integrity, this system has advantage of security comparing to previous data management and searching system using centralized database or P2P networks.

Jung, Junyoung, Cho, Jinsung, Lee, Ben.  2020.  A Secure Platform for IoT Devices based on ARM Platform Security Architecture. 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—4.
Recent IoT services are being used in various fields such as smart homes, smart factories, smart cars and industrial systems. These various IoT services are implemented through hyper-connected IoT devices, and accordingly, security requirements of these devices are being highlighted. In order to satisfy the security requirements of IoT devices, various studies have been conducted such as HSM, Security SoC, and TrustZone. In particular, ARM proposed Platform Security Architecture (PSA), which is a security architecture that provide execution isolation to safely manage and protect the computing resources of low- end IoT devices. PSA can ensure confidentiality and integrity of IoT devices based on its structural features, but conversely, it has the problem of increasing development difficulty in using the security functions of PSA. To solve this problem, this paper analyzes the security requirements of an IoT platform and proposes secure platform based on PSA. To evaluate the proposed secure platform, a PoC implementation is provided based on hardware prototype consisting of FPGA. Our experiments with the PoC implementation verify that the proposed secure platform offers not only high security but also convenience of application development for IoT devices.
Jung, Jaemin, Choi, Jongmoo, Cho, Seong-je, Han, Sangchul, Park, Minkyu, Hwang, Youngsup.  2018.  Android Malware Detection Using Convolutional Neural Networks and Data Section Images. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. :149-153.
The paper proposes a new technique to detect Android malware effectively based on converting malware binaries into images and applying machine learning techniques on those images. Existing research converts the whole executable files (e.g., DEX files in Android application package) of target apps into images and uses them for machine learning. However, the entire DEX file (consisting of header section, identifier section, data section, optional link data area, etc.) might contain noisy information for malware detection. In this paper, we convert only data sections of DEX files into grayscale images and apply machine learning on the images with Convolutional Neural Networks (CNN). By using only the data sections for 5,377 malicious and 6,249 benign apps, our technique reduces the storage capacity by 17.5% on average compared to using the whole DEX files. We apply two CNN models, Inception-v3 and Inception-ResNet-v2, which are known to be efficient in image processing, and examine the effectiveness of our technique in terms of accuracy. Experiment results show that the proposed technique achieves better accuracy with smaller storage capacity than the approach using the whole DEX files. Inception-ResNet-v2 with the stochastic gradient descent (SGD) optimization algorithm reaches 98.02% accuracy.
Jung, Byungho, Kim, Taeguen, Im, Eul Gyu.  2018.  Malware Classification Using Byte Sequence Information. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. :143–148.

The number of new malware and new malware variants have been increasing continuously. Security experts analyze malware to capture the malicious properties of malware and to generate signatures or detection rules, but the analysis overheads keep increasing with the increasing number of malware. To analyze a large amount of malware, various kinds of automatic analysis methods are in need. Recently, deep learning techniques such as convolutional neural network (CNN) and recurrent neural network (RNN) have been applied for malware classifications. The features used in the previous approches are mostly based on API (Application Programming Interface) information, and the API invocation information can be obtained through dynamic analysis. However, the invocation information may not reflect malicious behaviors of malware because malware developers use various analysis avoidance techniques. Therefore, deep learning-based malware analysis using other features still need to be developed to improve malware analysis performance. In this paper, we propose a malware classification method using the deep learning algorithm based on byte information. Our proposed method uses images generated from malware byte information that can reflect malware behavioral context, and the convolutional neural network-based sentence analysis is used to process the generated images. We performed several experiments to show the effecitveness of our proposed method, and the experimental results show that our method showed higher accuracy than the naive CNN model, and the detection accuracy was about 99%.

Junejo, Khurum Nazir, Goh, Jonathan.  2016.  Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning. Proceedings of the 2Nd ACM International Workshop on Cyber-Physical System Security. :34–43.

Cyber-physical systems (CPS) are often network integrated to enable remote management, monitoring, and reporting. Such integration has made them vulnerable to cyber attacks originating from an untrusted network (e.g., the internet). Once an attacker breaches the network security, he could corrupt operations of the system in question, which may in turn lead to catastrophes. Hence there is a critical need to detect intrusions into mission-critical CPS. Signature based detection may not work well for CPS, whose complexity may preclude any succinct signatures that we will need. Specification based detection requires accurate definitions of system behaviour that similarly can be hard to obtain, due to the CPS's complexity and dynamics, as well as inaccuracies and incompleteness of design documents or operation manuals. Formal models, to be tractable, are often oversimplified, in which case they will not support effective detection. In this paper, we study a behaviour-based machine learning (ML) approach for the intrusion detection. Whereas prior unsupervised ML methods have suffered from high missed detection or false-positive rates, we use a high-fidelity CPS testbed, which replicates all main physical and control components of a modern water treatment facility, to generate systematic training data for a supervised method. The method does not only detect the occurrence of a cyber attack at the physical process layer, but it also identifies the specific type of the attack. Its detection is fast and robust to noise. Furthermore, its adaptive system model can learn quickly to match dynamics of the CPS and its operating environment. It exhibits a low false positive (FP) rate, yet high precision and recall.