A Classification of Semisymmetric Cubic Graphs of Order 28p&sup2

A graph is said to be semisymmetric if its full automorphism group actstransitively on its edge set but not on its vertex set. In this paper, we prove thatthere is only one semisymmetric cubic graph of order 28p<sub>2</sub>, where p is a prime.DOI : http://dx.doi.org/10.22342/jims.16.2.38.139-14

A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks

The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would significantly limit the potential damage of malware. This is a more profound risk in Cyber-Physical Systems (CPSs), where a malware attack may cause significant physical damage to the infrastructure. Due to limited on-device available memory and processing power in CPS devices, most of the efforts for protecting CPS networks are focused on the edge layer, where the majority of security mechanisms are deployed. Since the majority of advanced and sophisticated malware programs are combining features from different families, these malicious programs are not similar enough to any existing malware family and easily evade binary classifier detection. Therefore, in this article, we propose a novel multilabel fuzzy clustering system for malware attack attribution. Our system is deployed on the edge layer to provide insight into applicable malware threats to the CPS network. We leverage static analysis by utilizing Opcode frequencies as the feature space to classify malware families. We observed that a multilabel classifier does not classify a part of samples. We named this problem the instance coverage problem. To overcome this problem, we developed an ensemble-based multilabel fuzzy classification method to suggest the relevance of a malware instance to the stricken families. This classifier identified samples of VirusShare, RansomwareTracker, and BIG2015 with an accuracy of 94.66%, 94.26%, and 97.56%, respectively

Perfect 3-colorings of the Cubic Graphs of Order 10

Perfect coloring is a generalization of the notion of completely regular codes, given by Delsarte. A perfect m-coloring of a graph G with m colors is a partition of the vertex set of G into m parts A_1, A_2, ..., A_m such that, for all $i,j \in \lbrace 1, ... , m \rbrace$, every vertex of A_i is adjacent to the same number of vertices, namely, a_{ij} vertices, of A_j. The matrix $A=(a_{ij})_{i,j\in \lbrace 1,... ,m\rbrace }$, is called the parameter matrix. We study the perfect 3-colorings (also known as the equitable partitions into three parts) of the cubic graphs of order 10. In particular, we classify all the realizable parameter matrices of perfect 3-colorings for the cubic graphs of order 10

NORMAL 6-VALENT CAYLEY GRAPHS OF ABELIAN GROUPS

Abstract : We call a Cayley graph Γ = Cay (G, S) normal for G, if the right regular representation R(G) of G is normal in the full automorphism group of Aut(Γ). In this paper, a classification of all non-normal Cayley graphs of finite abelian group with valency 6 was presented

Detection of algorithmically-generated domains: An adversarial machine learning approach

Domain name detection techniques are widely used to detect Algorithmically Generated Domain names (AGD) applied by Botnets. A major difficulty with these algorithms is to detect those generated names which are meaningful. In this way, Command and Control (C2) servers are detected. Machine learning techniques have been of great use to generalize the attributes of the meaningful names, generated algorithmically. To resist such techniques, the distribution of characters is used as a basis to generate meaningful domain names. Such techniques are called adversarial attacks attempting to fool machine learning methods. However, our experiments with more than 252757 samples show that in addition to character distribution of domain names, randomness property and pronounceability attributes are of great use to detect such meaningful names. Using these additional attributes, we have been able to identify malicious domain names with an accuracy of 98.19%